3D Gaussian splatting (3DGS) has demonstrated impressive 3D reconstruction performance with explicit scene representations. Given the widespread application of 3DGS in 3D reconstruction and generation tasks, there is an urgent need to protect the copyright of 3DGS assets. However, existing copyright protection techniques for 3DGS overlook the usability of 3D assets, posing challenges for practical deployment. Here we describe splats in splats, the first 3DGS steganography framework that embeds 3D content in 3DGS itself without modifying any attributes. To achieve this, we take a deep insight into spherical harmonics (SH) and devise an importance-graded SH coefficient encryption strategy to embed the hidden SH coefficients. Furthermore, we employ a convolutional autoencoder to establish a mapping between the original Gaussian primitives' opacity and the hidden Gaussian primitives' opacity. Extensive experiments indicate that our method significantly outperforms existing 3D steganography techniques, with 5.31% higher scene fidelity and 3x faster rendering speed, while ensuring security, robustness, and user experience.

3D Gaussian Splatting (3DGS) has emerged as a powerful representation for 3D scenes, widely adopted due to its exceptional efficiency and high-fidelity visual quality. Given the significant value of 3DGS assets, recent works have introduced specialized watermarking schemes to ensure copyright protection and ownership verification. However, can existing 3D Gaussian watermarking approaches genuinely guarantee robust protection of the 3D assets? In this paper, for the first time, we systematically explore and validate possible vulnerabilities of 3DGS watermarking frameworks. We demonstrate that conventional watermark removal techniques designed for 2D images do not effectively generalize to the 3DGS scenario due to the specialized rendering pipeline and unique attributes of each gaussian primitives. Motivated by this insight, we propose GSPure, the first watermark purification framework specifically for 3DGS watermarking representations. By analyzing view-dependent rendering contributions and exploiting geometrically accurate feature clustering, GSPure precisely isolates and effectively removes watermark-related Gaussian primitives while preserving scene integrity. Extensive experiments demonstrate that our GSPure achieves the best watermark purification performance, reducing watermark PSNR by up to 16.34dB while minimizing degradation to original scene fidelity with less than 1dB PSNR loss. Moreover, it consistently outperforms existing methods in both effectiveness and generalization.

The rapid advancement of large language models (LLMs) has resulted in increasingly sophisticated AI-generated content, posing significant challenges in distinguishing LLM-generated text from human-written language. Existing detection methods, primarily based on lexical heuristics or fine-tuned classifiers, often suffer from limited generalizability and are vulnerable to paraphrasing, adversarial perturbations, and cross-domain shifts. In this work, we propose SentiDetect, a model-agnostic framework for detecting LLM-generated text by analyzing the divergence in sentiment distribution stability. Our method is motivated by the empirical observation that LLM outputs tend to exhibit emotionally consistent patterns, whereas human-written texts display greater emotional variability. To capture this phenomenon, we define two complementary metrics: sentiment distribution consistency and sentiment distribution preservation, which quantify stability under sentiment-altering and semantic-preserving transformations. We evaluate SentiDetect on five diverse domains and a range of advanced LLMs, including Gemini-1.5-Pro, Claude-3, GPT-4-0613, and LLaMa-3.3. Experimental results demonstrate its superiority over state-of-the-art baselines, with over 16% and 11% F1 score improvements on Gemini-1.5-Pro and GPT-4-0613, respectively. Moreover, SentiDetect also shows greater robustness to paraphrasing, adversarial attacks, and text length variations, outperforming existing detectors in challenging scenarios.

Federated Learning (FL) enables collaborative model training without data sharing, yet participants face a fundamental challenge, e.g., simultaneously ensuring fairness across demographic groups while protecting sensitive client data. We introduce a differentially private fair FL algorithm (FedPF) that transforms this multi-objective optimization into a zero-sum game where fairness and privacy constraints compete against model utility. Our theoretical analysis reveals an inverse relationship: privacy mechanisms that protect sensitive attributes can reduce the statistical power available for detecting and correcting demographic biases under finite samples in federated settings. We further show that our theoretical bounds are consistent with a non-monotonic fairness-utility relationship, which is empirically validated by experiments where moderate fairness constraints improve generalization before excessive enforcement degrades performance. Compared with mainstream algorithms, even under strict privacy constraints, FedPF still maintains the lowest discrimination level among all tested algorithms while retaining high utility. Experimental validation demonstrates up to 42.9 % discrimination reduction across three datasets while maintaining competitive accuracy, but more importantly, reveals that achieving strong privacy and fairness simultaneously requires carefully balanced tradeoffs rather than optimizing either objective in isolation. Furthermore, hardware-level simulations demonstrate that FedPF maintains a low computational footprint, making it suitable for resource-constrained edge devices. The source code for our proposed algorithm is publicly accessible at https://github.com/szpsunkk/FedPF.

Cross-view geo-localization (CVGL) establishes correspondences between ground-level and satellite images of the same geographic location, serving as a fundamental technology for smart city applications, including autonomous navigation, urban planning, and location-based services. Current CVGL approaches fall into two categories: feature-based methods achieve superior performance through 2D representation learning but lack interpretability. Spatial-based methods provide geometric understanding and interpretable matching but suffer from limited spatial modeling and weak cross-view alignment, leading to lower performance. We reformulate CVGL from a spatial perspective and propose an auxiliary task-enhanced network. The network captures spatial semantics and provides explicit alignment processes with visualizable results. We introduce an auxiliary spatial semantic alignment (SSA) task that learns spatial structure via vision foundation models (VFM) and BEV transformation to enhance the primary CVGL task. The primary task captures visual semantics, including texture and appearance. Within this unified framework with shared encoders, the primary task enriches the learned embeddings by fusing spatial structure with visual semantics, yielding spatially complete representations. Extensive experiments on three standard CVGL benchmarks demonstrate that our method significantly surpasses previous spatial-based approaches while maintaining competitive performance with state-of-the-art (SOTA) feature-based methods, achieving 98.48% R@1 on CVUSA and 71.05% R@1 on CVACT\_test. We provide comprehensive analyses through pixel-level activation maps and feature-space UMAP visualizations to validate both effectiveness and interpretability.

Semisupervised social graph node classification (SSGNC) attempts to deduce node-related information of social graph with limited labeled training samples. It is primarily deployed in large-scale graph processing, e.g., malicious client detection, knowledge graph, and recommender system. However, in this article, we identify that the SSGNC model is also extremely sensitive to backdoor attacks. We present a novel persistent clean-label backdoor attack (PerCBA) on SSGNC, which selectively poisons unmarked training nodes before learning to compel the trained model to misclassify trigger-embedded inputs into malicious class. Specifically, PerCBA employs a style-agnostic trigger generator with adjustable perturbation strategy to produce perturbed triggers. These triggers are pasted onto a small subset of unmarked nodes ($< \, 4\%$), enabling the adversary to covertly poison the training graph and implant backdoors into the model without modifying labels. Additionally, to ensure SSGNC robustness when confronted with homogenous threats, we present a testing sample filtering-based defense strategy for PerCBA. It employs feature distribution to identify poisoned nodes and applies Gaussian blur and thresholding to remove the trigger fraction, thereby restoring suspicious data to clean states. Extensive experiments on SOTA SSGNC models and datasets indicate that PerCBA performs high attack success rates (maxima 96.25%) while remaining evasive, and the defense method can effectively mitigate attacks and purify backdoored models.

Multi-agent architectures leveraging Large Language Models (LLMs) have significantly advanced the precision of Question Answering (QA) systems across diverse domains. However, existing frameworks remain vulnerable to adversarial manipulations, including poisoning, backdoor, and jailbreak at tacks, primarily due to their reliance on centralized orchestration. To mitigate these risks, we propose AgentChain, a framework that substitutes centralized control with a distributed semantic consensus process. By modeling the blockchain as an ideal functionality, AgentChain establishes a secure distributed layer to coordinate role allocation, answer proposal, evaluation and voting through a decentralized council. Specifically, we design Proof-of-Content-Quality (PoCQ) mechanism to ensure that the f inal answers reflect a robust semantic agreement among the majority of honest agents. Furthermore, we propose an incentive mechanism based on stake reassignment that penalizes malicious agents by reducing their rewards, ultimately phasing them out of the network. Comprehensive evaluations across eight datasets demonstrate that AgentChain achieves superior performance and resilience. AgentChain minimizes the impact of poisoning attacks on precision to less than 3% and reduces the success rate of backdoor and jailbreak attacks to less than 4%. These findings highlight the effectiveness and trustworthiness of AgentChain in mitigating security threats while maintaining high QA accuracy.

Graph Neural Networks (GNNs) with differential privacy have been proposed to preserve graph privacy when nodes represent personal and sensitive information. However, existing methods ignore that nodes with different importance may yield diverse privacy demands, which may lead to over-protecting some nodes and decrease model utility. In this paper, we study the problem of importance-grained privacy, where nodes contain personal data that need to be kept private but are critical for training a GNN. We propose NAP-GNN, a node-importance-grained privacy-preserving GNN algorithm with privacy guarantees based on adaptive differential privacy to safeguard node information. First, we propose a Topology-based Node Importance Estimation (TNIE) method to infer unknown node importance with neighborhood and centrality awareness. Second, an adaptive private aggregation method is proposed to perturb neighborhood aggregation from node-importance-grain. Third, we propose to privately train a graph learning algorithm on perturbed aggregations in an adaptive residual connection mode over multi-layer convolution for node- wise tasks. The theoretical analysis shows that NAP-GNN can guarantee privacy. Empirical experiments over five real-world graph datasets show that NAP-GNN achieves a better trade-off between privacy and accuracy.

Signed graphs serve as fundamental data structures for representing positive and negative relationships in social networks, with signed graph neural networks (SGNNs) emerging as the primary tool for their analysis. Our investigation reveals that balance theory, while essential for modeling signed relationships in SGNNs, inadvertently introduces exploitable vulnerabilities to black-box attacks. To showcase this, we propose balance-attack, a novel adversarial strategy specifically designed to compromise graph balance degree, and develop an efficient heuristic algorithm to solve the associated NP-hard optimization problem. While existing approaches attempt to restore attacked graphs through balance learning techniques, they face a critical challenge we term “Irreversibility of Balance-related Information,” as restored edges fail to align with original attack targets. To address this limitation, we introduce Balance Augmented-Signed Graph Contrastive Learning (BA-SGCL), an innovative framework that combines contrastive learning with balance augmentation techniques to achieve robust graph representations. By maintaining high balance degree in the latent space, BA-SGCL not only effectively circumvents the irreversibility challenge but also significantly enhances model resilience. Extensive experiments across multiple SGNN architectures and real-world datasets demonstrate both the effectiveness of our proposed balance-attack and the superior robustness of BA-SGCL, advancing the security and reliability of signed graph analysis in social networks. Datasets and codes of the proposed framework are at the github repositoryhttps://github.com/JialongZhou666/BA-SGCL.git.

To defend against adversarial structural attacks on graphs, we analyze attacks through the lens of mutual information and discover the “pairwise effect". This effect reveals that structural attacks effectively degrade the performance of victim GNNs when these GNNs receive the modified structure paired with the given node attributes as training input. Therefore, we propose a novel defense strategy that renders structural attacks ineffective by disrupting the pairing of modified structures and node attributes during the training of victim GNNs, which we call “disrupting the pairwise effect". To implement this idea, we propose two simple yet effective training strategies: Structural Fine-Tuning (SF) and Progressive Structural Training (PST), which disrupt the pairwise effect through node attributes pre-training followed by structure fine-tuning and progressive structure training, respectively. Compared to existing robust GNNs, our strategies avoid time-consuming techniques, thereby improving the robustness of GNNs while enhancing training speed. Additionally, these strategies can be easily applied to a wide range of commonly used GNNs, including robust GNN variants, making them highly adaptable to different models and applications. We provide theoretical analysis of the proposed training strategies and conduct extensive experiments on various datasets to demonstrate their effectiveness. Datasets and codes of this paper are available at https://github.com/Xing-Ai1003/Revisiting-Adversarial-Robustness-of-GNNs.

Reliable encrypted traffic classification is crucial for fine-grained and efficient network security management, enabling accurate user behavior recognition and cybercrime forensics. While AI-based methods can automatically extract subtle features from traffic data, existing approaches often fail to effectively capture and integrate features across different levels of traffic granularity, namely the byte, packet and flow levels. Current graph-based methods heavily rely on manual feature engineering to construct global IP-based graphs, overlooking critical packet-level temporal features and byte-level raw information. Focusing on only one or two levels of traffic granularity is unreliable and insufficient, ultimately compromising model accuracy and robustness. To address these limitations, we propose BPF-DAG, a byte-packet-flow feature fusion framework based on dynamic attributed graphs, for reliable encrypted traffic classification. To the best of our knowledge, this is the first method that integrates temporal packet relations into flow interaction patterns while directly leveraging raw byte-level data. Specifically, we introduce a multi-granularity feature fusion strategy that dynamically updates an IP-based graph by iteratively assigning edge attributes derived from evolving flow representations. During the joint training of the Transformer and the graph neural network, temporal representations are learned from raw packet sequences and reflected in edge attributes dynamically for further message aggregation. Experiments on the ISCX VPN-nonVPN, Tor-nonTor, MIRAGE-2019 and MIRAGE-2024 datasets show that BPF-DAG outperforms recent state-of-the-art methods in terms of classification performance.

3D reconstruction is a cornerstone for creating immersive digital experiences in metaverse. Owing to explicit scene representation and efficient rendering, Gaussian splatting (GS) has become a prominent research focus in 3D reconstruction. However, the input images for GS are often imperfect, as those collected via highly-interfered wireless environment (HIWE) tend to be distorted, thereby undermining the accuracy of 3D reconstruction and limiting scalability. This paper proposes a novel Gaussian semantic splatting (GSS) scheme, designed for agile, reliable, and communication-efficient 3D reconstruction in the metaverse. Specifically, the semantic communication encoder/decoder (SCED) within GSS performs sequential semantic encoding and channel encoding using the proposed reliable and efficient semantic communication (RESC) algorithm, enabling the receiver to recover images with near-perfect accuracy. These images are then processed by the memory-efficient Gaussian renderer (MEGR), which employs an agile Gaussian splatting rendering (AGSR) algorithm to complete the 3D reconstruction and render a series of new viewpoint images. Additionally, a semantic control unit (SCU) is designed to oversee the components, enhancing the overall efficiency of the 3D reconstruction process. Experimental results demonstrate that GSS achieves competitive 3D reconstruction quality in HIWE, delivering real-time rendering speeds of 145 FPS at an$800\times 800$resolution while reducing storage memory overhead by more than 12 times compared to the state-of-the-art (SOTA) scheme.

Multimodal Recommendation Systems (MRSs) boost traditional user-item interaction-based methods by incorporating multimodal information. However, existing methods ignore the inherent noise brought by (1) noisy semantic priors in multimodal content, and (2) noisy user interactions in history records, therefore diminishing model performance. To fill this gap, we propose to denoise MRSs by jointly EValuating structure Effectiveness and mitigating Noisy links (EVEN). Firstly, for semantic prior noise in multimodal content, EVEN builds item homogeneous consistency and denoises it by evaluating behavior-driven confidence. Secondly, for noise in user interactions, EVEN updates user feedback by denoising observed interactions following implicit contribution evaluation of high-order representations. Thirdly, EVEN performs cross-modal alignment through self-guided structure learning, reinforcing task-specific inter-modal dependency modeling and cross-modal fusion. Through extensive experiments on three widely-used datasets, EVEN achieves an average improvement of 8.95% and 5.90% in recommendation accuracy compared with LGMRec and FREEDOM, respectively, without extending the total training time.

Semantic Communication (SC) enhances communication efficacy by abstracting and decoding semantic information via shared knowledge instead of bitstream, while considerably reducing redundancy and reinforcing efficiency in downstream tasks including image recognition, language processing, internet of things, etc. Due to the extensive data collection, processing, and training, the SC shared knowledge is invaluable Intellectual Property (IP), and despite the owners' desire to prevent misuse, the knowledge still remains vulnerable to theft while related IP protection has yet to be explored. To bridge this gap, we propose WatCom, the first SC IP protection methodology via watermarking. Specifically, we implant a stealthy backdoor into the semantic shared knowledge to verify model ownership, which can solely be activated by the owner-exclusive implicit trigger to validate ownership. The backdoor is implanted by poisoning-training strategy, facilitating SC system to respond normally to regular inputs while producing verification outputs (i.e., backdoor activation) for trigger-infected samples. To ensure imperceptibility, we leverage one generator to synthesize infected data that is nearly indistinguishable from regular data, which thereby obfuscates the verification information presence and enhances security against adversarial detection. Experiments based on multiple datasets and systems demonstrate WatCom can effectively verify system ownership (IP Verification Rate$\sim 100 \%$) while maintaining transmission efficacy (Peak Signal-to-Noise Ratio drop$< 2 ~\text{dB}$).

Multi-object 3D Gaussian Splatting (3DGS) technology aims to efficiently synthesize complex 3D scenes from images while allowing users to manipulate objects through textual prompts. Training multi-object 3DGS models requires substantial computational resources, making it necessary to protect the generated 3D objects from unauthorized reproduction, modification, and distribution. Existing watermarking solutions suffer from high memory consumption and require additional time overhead. Moreover, they cannot precisely localize watermarks to specific objects, making it difficult to trace individual contributions when multiple creators collaborate on a same multi-object scene. To address these challenges, we propose 3D-MGW, a novel memory-efficient grouped watermark for multi-object 3DGS. Within 3D-MGW, background scenes are reconstructed from images, while diffusion models are incorporated to guide highquality 3DGS synthesis from prompts. To eliminate additional training overhead, watermark embedding is integrated within the 3DGS training process rather than implementing it separately. Subsequently, a grouped Gaussian strategy is introduced to enable granular, high-capacity multi-object watermark. Additionally, a Gaussian compression module is proposed to eliminate redundant primitives, reducing the storage footprint of Gaussian models. Through comprehensive experiments, our 3D-MGW demonstrates exceptional performance, achieving 95% watermark extraction accuracy under 64-bit watermarks while reducing storage utilization by up to 61%, highlighting its substantial potential for multi-object 3DGS applications.

Graph learning models have been empirically proven to be vulnerable to backdoor threats, wherein adversaries submit trigger-embedded inputs to manipulate the model predictions. Current graph backdoor defenses manifest several limitations: 1) dependence on model-related details, 2) necessitation of additional fine-tuning, and 3) reliance on extra explainability tools, all of which are infeasible under stringent privacy policies. To address those limitations, we propose GraphProt, a certified black-box defense method to suppress backdoor attacks on GNN-based graph classifiers. Our GraphProt operates in a model-agnostic manner and solely leverages graph input. Specifically, GraphProt first introduces designed topology-feature-filtration to mitigate graph anomalies. Subsequently, subgraphs are sampled via a formulated strategy integrating topology and features, followed by a robust model inference through a majority vote-based subgraph prediction ensemble. Our results across benchmark attacks and datasets show GraphProt effectively reduces attack success rates while preserving regular graph classification accuracy.

Malicious attacks frequently exploit encrypted traffic as a covert channel for intrusion, rendering the accurate identification of malicious encrypted traffic essential for early threat detection. Existing encrypted traffic classification methods primarily focus on low-order IP topological graph and single flow features. However, malicious IPs usually send encrypted flows mixed attack flows with benign traffic to hide themselves, while attack flows have high-order relations, leaving complex interactions between IP nodes and traffic flows. To address these limitations, we propose a novel Hyper-Bipartite Graph Fusion (HyBiGraph) framework for malicious encrypted traffic classification that integrates a bipartite graph for modeling low-order relationships and a hypergraph for propagating higher-order structural information. HyBiGraph constructs an IP-Flow bipartite graph with trainable IP embeddings updated via flow features, which enables the model to efficiently capture the contextual relationships between source and target IP. It further employs hypergraph attention with learnable hyperedges to power precise modeling of higher-order interactions among flows. Finally, residual fusion of hypergraph and bipartite graph offers a robust and efficient mechanism for integrating structural representations, enhancing classification performance. HyBiGraph was evaluated on benchmark encrypted malicious traffic datasets—USTC-TFC2016, CICIoT2023, and CICAndMal2017—attaining accuracy improvements of 2.51%, 23.93%, and 51.97% and requiring only approximately 10% training cost of baselines. Also, ablation studies validate that integrating hypergraph and bipartite graph promotes accuracy gains between 1.27% and 53.98%.

Static analysis presents significant challenges in alarm handling, where probabilistic models and alarm prioritization are essential methods for addressing these issues. These models prioritize alarms based on user feedback, thereby alleviating the burden on users to manually inspect alarms. However, they often encounter limitations related to efficiency and issues such as false generalization. While learning-based approaches have demonstrated promise, they typically incur high training costs and are constrained by the predefined structures of existing models. Moreover, the integration of large language models (LLMs) in static analysis has yet to reach its full potential, often resulting in lower accuracy rates in vulnerability identification. To tackle these challenges, we introduce BinLLM, a novel framework that harnesses the generalization capabilities of LLMs to enhance alarm probability models through rule learning. Our approach integrates LLM-derived abstract rules into the probabilistic model, using alarm paths and critical statements from static analysis. This integration enhances the model’s reasoning capabilities, improving its effectiveness in prioritizing genuine bugs while mitigating false generalizations. We evaluated BinLLM on a suite of C programs and observed 40.1% and 9.4% reduction in the number of checks required for alarm verification compared to two state-of-the-art baselines, Bingo and BayeSmith, respectively, underscoring the potential of combining LLMs with static analysis to improve alarm management.

Currently, the security of the control logic of Programmable Logic Controllers (PLCs) is facing a serious threat, significantly impacting industrial production. Consequently, ensuring the security of PLC control logic becomes imperative. Formal verification emerges as a promising methodology for verifing PLC security through behavioral modeling and security testing. However, existing formal verification approaches primarily focus on modeling the PLC source code, overlooking the identification of compile-time errors and real-time runtime logic checks. Therefore, it is essential to apply formal verification to PLC control logic at the binary level. In this study, we introduce VoICS, a system designed to facilitate binary-level formal verification. Using reverse engineering, VoICS automatically parses PLC programs written by various programming languages at the binary level and constructs control flow graphs (CFGs). Furthermore, we use an algorithm combining two model optimization methods (i.e., trim invalid states and unnecessary states compression) to convert the reversed PLC assembly program into nuXmv format model. Lastly, VoICS establishes the corresponding constraints and performs formal verification on the model using nuXmv. The evaluation results demonstrate the capability of VoICS in identifying instances of unreliable control logic within PLC control programs, thus reinforcing the dependability of the industrial automation system.

Proof-of-work (PoW) blockchain relies on incentive mechanisms to ensure the security and correctness of its underlying consensus protocol. Most research about it, based on a static model only considering coin-base rewards and transaction fee rewards, fails to accurately describe the complex real-world blockchain ecosystem. We propose a generic selfish mining attack applicable to arbitrary PoW blockchain systems and introduce a dynamic PoW blockchain incentive model. This model takes into account static basic rewards, dynamic whale rewards related to network protocol, and expenditures tied to players’ strategies. Unlike traditional incentive models that assume players continuously mine by default, we find players prefer to halt mining at the beginning of each mining cycle to reduce operational expenses and then resume mining at an appropriate time to enhance their rewards. We further prove players’ optimal strategy exists and it is determined by reward parameters. We implement a modified PoW blockchain system simulator and comprehensively validate these results using 256 full nodes in it of three mainstream PoW blockchains: Bitcoin, Ethereum 1.x, and Bitcoin Cash. We finally discuss the impact of different parameters on the security of PoW blockchain systems and propose practical mitigating measures for the mining halt.

To improve communication efficiency and handle data heterogeneity challenges in federated learning (FL), fine-tuning the pre-trained large models rather than training neural networks from scratch has received increasing attention in recent years, especially under cross-domain settings. However, such a cross-domain federated fine-tuning scenario opens up a broader attack surface for new threats, especially backdoors, posing significant security risks. Existing backdoor attacks mainly focus on label shift scenarios and use explicit triggers, which lack transferability and effectiveness in cross-domain settings, thereby exhibiting significant weaknesses. In this paper, we propose Silent Penetrator, an innovative penetration scheme tailored for cross-domain federated fine-tuning, which exploits a feature shift-induced backdoor to elicit specific symptoms in the trusted private data of targeted victims. In Silent Penetrator, the attacker can obtain a high-quality poisoned dataset by leveraging the available domain information as the text prompts for Stable Diffusion, and inject a domain-sensitive backdoor that can be unconsciously triggered by unmodified private data of the victims. To achieve stronger and more persistent penetration, we thoroughly explore the adversary’s configurable space and enhance our backdoor injection utilizing contrastive-enhanced boundary deviation and cross-domain predictive confrontation. Extensive experiments on three cross-domain datasets and four state-of-the-art federated fine-tuning frameworks validate the effectiveness of Silent Penetrator in successfully compromising target clients. Furthermore, our backdoor enhancement strategy improves the penetration accuracy by over 10% in most scenarios and significantly enhances the durability of the penetration compared to four state-of-the-art backdoor enhancement techniques.

With increasing cyber attacks over the Internet, network intrusion detection systems (NIDS) have been an indispensable barrier to protecting network security. Taking advantage of automatically capturing topology connections, recent deep graph learning approaches have achieved remarkable performance in distinguishing different types of malicious flows. However, there remain some critical challenges. 1) previous supervised learning methods rely heavily on abundant and high-quality annotated samples, while label annotation requires abundant time and expert knowledge. 2) Centralized methods require all data to be uploaded to a server for learning behavior patterns, which results in high detection latency and critical privacy leakage. 3) Diverse attack scenarios exhibit highly imbalanced distribution, making it hard to characterize abnormal behaviors. To address these issues, we proposed FeCoGraph, a label-aware federated graph contrastive learning framework for intrusion detection in few-shot scenarios. The line graph is introduced to directly process flow embeddings, which are compatible with diverse GNNs. Furthermore, We formulate a graph contrastive learning task to effectively leverage label information, allowing intra-class embeddings more compact than inter-class embeddings. To improve the scalability of NIDS, we utilize federated learning to cover more attack scenarios while protecting data privacy. Experiment results show that FeCoGraph surpass E-graphSAGE with an average 8.36% accuracy on binary classification and 6.77% accuracy on multiclass classification, demonstrating the efficiency of our approach.

Graph Neural Networks (GNNs) have demonstrated remarkable potential in various downstream tasks by effectively capturing the relational dependencies among nodes in graphs. However, this capability also brings significant privacy risks: when GNNs encode topological information and node features into their output, sensitive information can be inadvertently exposed, leading to severe privacy breaches. Existing privacy-preserving GNNs primarily focus on protecting the existence of individual nodes or edges, overlooking practical scenarios where nodes and edges are often publicly accessible and only specific sensitive attributes require protection, resulting in a lack of consideration for attribute sensitivity and challenges in balancing privacy and utility. In this paper, we study the problem of hiding sensitive information during GNN training and limiting its exposure in the outputs, while better defending against attribute inference attacks (AIAs) and achieving improved performance. To achieve this, we propose an attribute-oriented differentially private graph neural network (AODP-GNN) that enforces attribute-specific privacy guarantees through dynamic privacy budgets and relevance-aware noise injection, optimizing the balance between privacy and utility. Specifically, we design a neighborhood-aware private embedding generation mechanism and a mutual information minimization-based optimization strategy that operate before the deep interactions of feature interaction and model optimization to strengthen defense against AIAs. To enhance the balance between privacy and utility, we further develop a relevance-grained noise adaptation technique that dynamically allocates higher noise to less relevant attributes. Theoretical analysis shows that the AODP-GNN satisfies privacy guarantees. Extensive experiments conducted on four real-world datasets demonstrate that our approach can achieve up to around 10.04% and 9.21% higher accuracy compared to the state-of-the-art centrally differentially private GNN ProGAP and DPDGC, and also shows a higher defense capability against AIAs.

In the context of escalating advancements in AI generative technologies, Deepfakes, the sophisticated face forgeries created using deep learning methods, have emerged as a significant security threat. The predominant countermeasures are Deepfake detectors based on deep learning (DL). However, due to the opaque nature of DL-model, they struggle to offer understandable explanations for their predictive decisions, which undermines their reliability and effectiveness in real-world applications. Existing mainstream DL-oriented interpretation approaches, the feature attribution methods, struggle to work on Deepfake detectors due to issues of low interpretation fidelity, poor intelligibility, and limited applicability across different types of detectors. This paper addresses these critical challenges by proposing the Deepfake Detector Lens (DDL), a novel framework designed to enhance the interpretability of diverse architectural Deepfake detectors, encompassing those based on image, frequency domain, and video.DDLemploys a heuristic algorithm to enhance interpretation efficacy and incorporates image segmentation and face parsing techniques to bridge the gap between the machine-generated interpretation saliency map and human understanding. Comprehensive evaluations ofDDLdemonstrate its superiority over existing feature attribution methods in terms of fidelity, intelligibility, and applicability. The proposedDDLsignificantly advances the interpretability of Deepfake detection technology, offering a more reliable and understandable tool for combating AI-generated face forgeries.

Graph-based semi-supervised learning (GSSL) has long been a research focus. Traditional methods are generally shallow learners, based on the cluster assumption. Recently, graph convolutional networks (GCNs) have become the predominant techniques for their promising performance. However, a critical question remains largely unanswered: why do deep GCNs encounter the oversmoothing problem, while traditional shallow GSSL methods do not, despite both progressing through the graph in a similar iterative manner? In this article, we theoretically discuss the relationship between these two types of methods in a unified optimization framework. One of the most intriguing findings is that, unlike traditional ones, typical GCNs may not effectively incorporate both graph structure and label information at each layer. Motivated by this, we propose three simple but powerful graph convolution methods. The first, optimized simple graph convolution (OGC), is a supervised method, which guides the graph convolution process with labels. The others are two "no-learning" unsupervised methods: graph structure preserving graph convolution (GGC) and its multiscale version GGCM, both aiming to preserve the graph structure information during the convolution process. Finally, we conduct extensive experiments to show the effectiveness of our methods.

Ransomware has evolved into a more organized attack threat with stronger anti detection and analysis capabilities, resulting in significant global losses. However, traditional methods separate the external and internal behaviors of ransomware infiltration into attack targets, making it difficult to discover the complex and covert evolution and iteration characteristics of advanced ransomware. The main contribution of this study lies in three aspects: a) The integration of Command-and-control (C&C) traffic behavior analysis and local API call operation analysis can effectively discern and capture the concealed characteristics of ransomware; b) The non-IID problem in aggregating ransomware features using federated learning can be resolved using dynamic regularization methods and penalty terms; c) By preprocessing the original data of ransomware traffic through one-dimensional convolution, the structural characteristics of network traffic in the process of attack operation can be retained to the greatest extent. Comprehensive experiments are conducted to validate the effectiveness of this model, specifically, the heterogeneous federated learning-empowered ransomware detection (HFL-RD) scheme outperformed existing methods, the experimental dataset gathered runnable ransomware from three public websites, including 300 ransomware samples from 30 families and 200 benign software samples from 7 categories. HFL-RD obtained a high accuracy over 95%. In terms of detecting unknown ransomware variants, it has demonstrated superior detection capabilities in terms of detection time and number of file corruption.

The integration of covert communication in vehicle-to-everything (V2X) network has recently shown great potential to improve efficiency and reliability of data transmission under adversarial eavesdropping scenarios. In this paper, we propose a covert and reliable communication (CRC) framework for V2X networks, where the legitimate transmitter (Alice) attempts to communicate with a mobile receiver (Bob) in the presence of the location uncertainties of the eavesdropper (Willie). Specifically, the Bob adjusts the artificial noise power and position dynamically to communicate with Alice aided by full duplex antenna. In this context, we derive two key performance indicators of covert communication, namely the detection error probability and the effect covert throughput (ECT). Subsequently, we consider the worst case of CRC in the presence of single uncertain Willie, and derive the approximate maximum ECT expression by two-stage robust optimization. Building on this foundation, for more complex CRC scenario with multi uncertain Willies exist, we propose a deep reinforcement learning-empowered adaptation (DRLA) algorithm to maximize accumulated ECT. Extensive experiments compared to benchmarks (including stochastic selection, TD3 and DDPG) demonstrate the superiority of CRC. Specially, the designated DRLA algorithm not only can achieve a higher accumulated ECT but also can converge quickly compared with the benchmark schemes.

Nowadays, collaborative federated learning (CFL) is developing rapidly in the Internet of Things (IoT), which allows clients to jointly train models without compromising private data. The existing research has studied alone either trust enhancement or privacy preservation issues in CFL. Due to the highly coupled nature of trust and privacy in a collaborative environment, it is worth investigating how to balance appropriate trust and privacy tradeoffs for realizing high-quality CFL. In this paper, we come up with the idea of "trading Trust for Privacy", and propose a novel socially-motivated personalized privacy-preserving federated learning (SP-PFL) framework, which aims to realize social trust-grained privacy protection. First, we design a social trust evaluation method among CFL clients, which is based on topological relation and attribute similarity. Based on the obtained trust value, we then propose a trust-grained privacy budget allocation strategy for SP-PFL, which could further adaptively adjust the differential privacy (DP) noise perturbation. Besides, we provide an analysis of privacy and convergence for our SP-PFL. Finally, we experiment with different models and parameter settings on different datasets. Extensive experimental results show that our method maintains personalized privacy and effectively improves the accuracy by 6.11% on the CNN model and MNIST dataset.

Large language models (LLMs) bring great potential for next-generation malware family recognition with their capacity to understand complex code semantics by integrating multi-dimensional data features. However, existing fine-tuning methods still rely on well-labelled datasets and powerful computation resources, which is particularly challenging when the variety and amount of malware grow in real-time. To more effectively recognize unknown malware varieties based on LLMs, a novel multi-dimensional knowledge-embedded prompt learning (MKPL) framework is proposed, in which prompts are generated through two main steps: 1) cross-linguistic prompt paraphrasing (CPP) for embedding multi-dimensional knowledge into templates, and 2) prompt scoring for selecting the most effective prompt templates. Moreover, to reduce feature loss during prompt tuning, a sampling-infer-concatenation pipeline is designated to process these long API malware sequences. Specifically, a single-sentence template can be upgraded to a multi-sentence template by integrating statistic features into CPP, which is essential to improve the robustness of recognition results. Comprehensive experiments across eight malware families in few-shot scenarios demonstrate the proposed method’s superior performance in all metrics.

Web 3.0 and Edge computing are inherently compatible, making them an ideal combination for building a secure and efficient distributed service platform to support decentralized applications (DApps). This paper investigates an elastic hybrid computing architecture in Edge Web 3.0, allowing DApp tasks to be executed in a hybrid manner by integrating on-chain and off-chain execution. The principle is to transfer a portion of DApp to an off-chain execution environment, along with an appropriate result verification process, to enhance computing efficiency and reduce blockchain overhead. We formulate a DApp task scheduling problem that jointly optimizes the execution pattern and offloading decision of user tasks. A learning-based DApp task scheduling scheme is designed based on Proximal Policy Optimization (PPO) to minimize the gas cost and service delay of DApps. Particularly, we tailor PPO to handle the hard constraints of service delay, gas consumption, and computing capacity in Edge Web 3.0 by adding regularization terms in the learning objective function. We establish an Edge Web 3.0 testbed based on Goerli, ZkSync, and Ethereum to evaluate the proposed method. The experimental results show that our method outperforms state-of-the-art benchmarks.

Swarm Learning (SL) provides a secure distributed learning environment to edge computing (EC) networks by leveraging blockchain technology for certified participation, encrypted information transmission, and immutable data storage. However, vanilla SL faces scalability limitations due to system-wide model aggregation, which bottlenecks its communication and blockchain efficiency. This paper presents a novel framework called Peer-to-peer Swarm Learning Over consOrtium blOckchain$(\mathbf{PSLO}_{3})$to enhance the scalability of SL over EC networks. PSLO3proposes a peer-to-peer swarm learning (P2P-SL) mechanism that only requires local communications for P2P model aggregation, thereby reducing the communication overhead of vanilla SL for system-wide model aggregation. Furthermore, PSLO3delivers P2P-SL over consortium blockchain and strategically organizes the edge servers into subchains to minimize the overhead of P2P-SL over consortium blockchain. A subchain formation scheme is designed based on graph partitioning by jointly analyzing the topological property of EC networks, message-passing patterns of P2P-SL, and the overhead of cross-/intra-chain interactions. An evaluation environment is built based on the Wecross platform to evaluate the performance of PSLO3. Experimental results demonstrate that PSLO3provides a reduction of 81.1% in communication overhead and a reduction of 26.03% in blockchain cost compared to vanilla swarm learning while demonstrating comparable learning performances.

PLC control programs are vulnerable to real-time threats, where attackers can disrupt the backhaul/front-end network of industrial production by creating numerous loops or I/O operations, leading to severe consequences. Therefore, formal verification of PLC control logic at the binary level is essential. In this study, we introduce a framework designed for formal verification of PLC control logic at the binary level. Our verification framework is based on simulation execution, which extracts the core control logic from PLC binary code. Initially, we develop an efficient framework for automating the parsing of PLC programs at the binary level and constructing their control flow graphs (CFGs). Next, we devise an algorithm to transform the reversed PLC assembly program into an smv model, a widely accepted formal verification tool. Subsequently, we generate real-time requirements relevant to industrial production and perform formal verification on the constructed models. To assess the real-time performance of our framework in safeguarding PLC systems, we implement a prototype and evaluated it across various representative ICS scenarios. The evaluation results demonstrate the capability of our proposed approach to effectively detect synchronization threats in PLC logic control programs.

Backdoor attacks can mislead deep bug search models by exploring model-sensitive assembly code, which can change alerts to benign results and cause buggy binaries to enter production environments. But assembly instructions have strict constraints and dependencies, and these additional model-sensitive assembly codes destroy semantics and syntax and are easily detected by dynamic analysis or context-based detection. To escape from the dynamic analysis-based detection, we propose a novel latent backdoor attack (LateBA) scheme based on the locality principle of program execution, which only poisons a few of infrequent execution codes, minimizing the effects on the original code logic. In LateBA, a progressive seed mutating strategy is designated to change the American Fuzzy Lop (AFL)-based path search tool to pay more attention to infrequent execution codes. With this strategy, the optimal range to positions in the whole program is determined. Subsequently, triggers are target model-sensitive assembly instructions, and try to minimize the variables that have been called in the context instructions in the trigger. Finally, we employ code semantic feature comparisons to select precise trigger injection positions within these ranges. The selection criteria of the trigger injection position is whether the corresponding code segments in this position have a data dependency relationship with other code segments. We evaluate the performance of LateBA over 7 deep bug search tasks. The results demonstrate the attack success rate of the proposed LateBA is considerable and competitive against the baselines.

Recently, Peer-to-Peer Semantic Network (P2PSN) has significantly boosted transmission efficiency among humans, machine agents, and smart devices. Despite these enhancements, the intelligent components within P2PSN pose vulnerabilities to backdoor attacks, where adversaries introduce specific pattern triggers to poison the training set, which prompts the well-trained P2PSN system to generate targeted malicious predictions when inputted with trigger-embedded data. Current backdoor methodologies exhibit several deficiencies: 1) pattern-based trigger lacking physical meaning and explainability; 2) visible trigger design that can be easily detected by defenders; 3) unstable attack performance resulting from communication interference. To overcome these shortcomings, we propose a novel invisible instruction backdoor attack scheme on Peer-to-Peer Semantic Networks: InviINS. The proposed method embeds text instructions on partial training samples as invisible triggers instead of pattern triggers, thereby poisoning the training set of P2PSN model before learning without visually discernible changes in data, and subsequently backdooring the model via training. In InviINS, adversaries can directly set instructions based on practical scenarios to launch attacks. Meanwhile, to accelerate backdoor convergence, a contrastive backdoor training methodology is presented to enhance the model’s sensitivity to instruction triggers and bolster its prediction performance on normal samples. Experiments with different poisoning-rates, signal-to-noise ratios, channel usages, and trigger types demonstrate that the InviINS can achieve a high attack success rate (~ 100%) while preserving the model performance on main tasks (accuracy drop < 3%).

Edge-Level Graph Learning System (EGLS) exhibits diverse applicability in management of distributed vehicular networks, e.g., flow prediction, route planning, and accident forecasting. For the EGLS training, expensive hardware resource consumption, traffic data collection, and dedicated training procedures make the learning algorithms become valuable intellectual property (IP) for the EGLS owner (e.g., Uber and Lyft), and they cannot tolerate the infringement act of their models’ intellectual property. To enhance its IP protection, we present ActIPP, the first active IP protection methodology for EGLS, which incorporates a built-in access control function in the model to safeguard against unauthorized queries. Specifically, it is achieved via a creative edge backdoor mechanism, wherein the edge training samples are poisoned via user-specific access tokens to induce legal outputs from a well-trained EGLS model for authorized users. Moreover, related token regulating strategies were proposed to dynamically realize the addition and revocation of user tokens by model retraining to guarantee access control in EGLS. Additionally, a Graph Mutual Information-based adaptive token generation method is presented to augment the access control embedding. Based on experiments with various real-world datasets, ActIPP demonstrates high success rates of IP protection (accuracy drop < 4%) under various scenarios and efficiently prevents unauthorized access (unauthorized access accuracy < 6%).

Semantic communication has experienced considerable growth and advancement due to its potential to support future intelligent applications (e.g., augmented reality). The realization of the above potential superiority depends on the construction and synchronization of semantic knowledge base among multiple ends. However, existing methods for constructing semantic knowledge base fail to adhere to the zero-trust architecture, where all communication ends can act as knowledge contributors without rigorous authentication. Motivated by this insight, we propose a novel zero-trust knowledge synchronization (ZeroTKS) scheme for secure semantic communication based on federated fine-tuning. In the proposed scheme, we firstly explore to introduce homomorphic encryption into federated fine-tuning of large models to securely synchronize the semantic knowledge base against privacy leakage risks. And also, to prevent from malicious model tampering attacks, a novel Age-of-Update-based access control mechanism is designated, in which only nodes with high AoU values can be authorized to participate in updating the semantic knowledge base by combining with hash-based message authentication code. Extensive experiments based on four different datasets in the GLUE benchmark show that our proposed scheme can securely synchronize distributed semantic knowledge base with maintaining acceptable performance.

By leveraging multi-view inputs to synthesize novel-view images, Neural Radiance Fields (NeRF) have emerged as a prominent technique in the realm of 3D object reconstruction. However, the input images of NeRF transmitted through high-interference wireless environment (HIWE) leads to negatively impact the accuracy of 3D reconstruction, thereby limiting its scalability. Fortunately, semantic communication has been proved a effective method to solve the above problem. In this paper, we propose a novel NeRF based 3D semantic communication (NeRF-3DSC) system, which offers robust 3D reconstruction in HIWE. Within NeRF-3DSC, semantic encoder and decoder are employed to extract and recover core semantic features of task-specific specific object (TSO) from the collected images, channel encoder and decoder ensure robust transmission of compressed semantic information in HIWE, lightweight renderer based on NeRF is employed for fast and efficient 3D reconstruction. Moreover, a semantic control unit (SCU) is introduced to guide above components, thereby enhancing the efficiency of reconstruction. Demonstrative experiments demonstrate that the proposed NeRF-3DSC enables robust object reconstruction in HIWE, surpassing the performance of state-of-the-art (SOTA) methods in terms of reconstruction quality.

Semantic communication has emerged as a revolutionary paradigm within wireless edge networks, showcasing remarkable communication efficiency. In contrast to traditional bit-level communication systems, semantic communication systems exhibit superior effectiveness and precision, particularly in scenarios characterized by low signal-to-noise ratios (SNR). Nonetheless, the privacy of semantic communication poses a critical challenge that demands attention. Once the attacker intercepts the semantic information through continuous eaves-dropping, the private data would be leaked under adversarial environment. Moreover, in low SNR scenario, joint optimization of anti -eavesdropping and privacy reconstruction has not yet been studied, coupled with the intricate nature of designing a cross-layer semantic protection strategy. To address this concern, this paper presents a covert and reliable semantic communication (CRSC) framework via full-duplex receiver to counter continuous eavesdropper by concealing the entire transmission process. Furthermore, a newly-defined metric, namely covert semantic throughput (CST), is introduced to quantify the system's performance. Furthermore, we formulate the maximization of average CST during the semantic transmission period as a multi-constraint optimization problem. Subsequently, we propose a reinforcement learning (RL)-empowered adaptation algorithm to address the formulated problem. Through simulation results, the effectiveness and feasibility of proposed CRSC framework are demonstrated, with an observed maximum average CST improvement of up to 42% compared to conventional communication systems in the low SNR scenario.

Differential-Private Graph Neural Networks (DP-GNNs) have generated remarkable research results, enabling them to effectively tackle the privacy leakage problem in graph learning. However, most DP-GNNs do not consider the temporal-dimensional scenarios. In tasks involving spatiotemporal graph training, such as wireless social networks analysis, the sensitive interactive information in each time graph should be protected. Therefore, we propose spatio-temporal dynamic graph learning with enhanced local differential privacy (LDP-STG). First, we design a weighted graph perturbation encoder based on the Bernoulli distribution and Laplace mechanism, which protects the structures of weighted graphs in time series under edge-level local differential privacy. Second, we employ an attention mechanism to learn dynamic graph node embedding from spatial and temporal dimensions. The theoretical analysis proves that our LDP-STG realizes differential privacy guarantees. We conduct experiments mainly on two communication datasets (i.e., Enron and UCI), which shows that our LDP-STG can achieve better privacy utility tradeoffs compared with traditional mechanisms in terms of snatiotemnoral dimensions.

Federated learning (FL) protects data privacy by sharing gradients across clients rather than local training data. However, malicious clients (e.g., attackers and stragglers) hiding in large-scale FL will severely reduce the learning performance. Thus, how to efficiently detect and defend Byzantine attacks in large-scale FL remains an urgent issue. This article proposes a reputation-aware mean-field-game-based FL (FedMFG) framework, which aims to defend against Byzantine attacks and improve learning performance. Precisely, we first model the process of large-scale FL as a mean-field game problem across clients and prove the existence and uniqueness of mean-field FL gradients (i.e., Nash equilibrium). We then design a mean-field FL gradient calculation algorithm based on stochastic differential equations, i.e., Hamilton-Jacobi–Bellman and Fokker-Planck–Kolmogorov equations. Based on comparing the cosine similarity of obtained mean-field and individual FL gradients, we build reputation-aware malicious client detection and defense mechanism, which improves the Byzantine robustness of FL with the global learning performance guarantee. Finally, experimental results show that our proposed framework outperforms the baseline algorithms in realizing Byzantine robustness and improving learning performance. Specifically, our algorithm improves the model accuracy by 10% and 72.7% for the no-attacker and attacker scenarios, respectively.

The technology of the Internet of Vehicles (IoV) and digital twins (DTs) is driving deeper connectivity between vehicles and road infrastructure. Through the data exchange of IoV and the simulation of DT technology, vehicle driving decisions, traffic management, and road planning are optimized. However, DT models contain a large amount of private vehicle data, causing the risk of privacy leakage. Distributed artificial intelligence (AI) methods, particularly federated learning (FL) algorithms, ensure data security and privacy by sharing data models rather than sharing private data. Current mainstream algorithms use FL and local differential privacy (LDP) or blockchain approaches to protect data security at the cost of lower model accuracy and larger computation time. In the vehicle road cooperation, we designed a three-layer DT-driven personalized privacy-preserving framework, which includes a physical layer, a DT layer, and an application layer. In our proposed framework, to improve the security and performance of DT models, a time-sensitive PLDP-based FL (TimeSenFLDP) mechanism is proposed to achieve different privacy levels of the DT model of vehicles over sharing time steps. Compared with the mainstream algorithm (e.g., DP-SGD), the experiments prove that our proposed algorithm has 18.07%, 16.32%, and 7.5% accuracy improvement in FedAvg, FedProx, and FedDyn, respectively.

The Internet of Vehicles (IoV) connects a massive amount of smart vehicles for inter/intra-vehicle information sharing. Data privacy issues, such as privacy leakage and privacy cost are the key challenges that hinder vehicle operators from sharing their data safely. Traditional privacy-preserving techniques, including Federated Learning (FL) and Differential Privacy (DP) techniques, can protect data privacy and security, but the high privacy cost severely limits learning performance. In addition, the IoV services place high demands on low communication latency, which can be obtained by reducing the communication bits, but it also limits the learning performance. Thus, how to solve the communication-privacy-accuracy tradeoffs to achieve low latency, high privacy preservation and model performance has been a complicated issue in IoV. In this paper, a privacy-enhancement differentially private federated learning framework (FedSDP) is proposed based on the shuffle model to ensure secure and efficient data sharing under the constraint of low latency in IoV. In our proposed framework, four privacy enhancement methods are proposed, including data subsampling, vehicle sampling, shuffle model and dummy points, to amplify the privacy and obtain higher learning performance. Then, a Top-K sparsification mechanism of the vehicle training process is proposed to reduce communication bits. Finally, the experimental results indicate that our approach can reduce the communication latency by 31.66%, enhance the privacy ϵc by 30.77% and improve the test accuracy by 48.56%, compared with the traditional SDP mechanism.

To reduce attack risks in Internet of Things (IoT), many security vendors conduct software security analysis on IoT devices all the time. However, how to build an ultralow-latency threat alerting strategy using software vulnerability information still faces challenges. First, existing terminal threat detection methods for IoT systems relying on Indicators of Compromise (IoC) threat intelligence can only cover limited software vulnerabilities so the alert validity rate is still very low. Second, most users lack security knowledge and cannot proactively distinguish high-risk vulnerabilities, resulting in untimely reporting. In this article, a novel hierarchical software execution state representation (HSESR) scheme is proposed for ultralow latency threat alerting over IoT systems based on Beyond 5G. In HSESR, function call graphs are recorded and delivered to edge servers for swiftly identifying suspicious threat behaviors based on deep graph representation, while corresponding instruction sequences are delivered to the cloud data center for further matching the vulnerability information via recurrent semantic representation. To improve the effectiveness of HSESR, the graph representation is also actively encapsulated into the corresponding semantic representation, together acting as an implicit threat behavior signature, which is essential to associate with a security patch. Moreover, to accelerate the detection of suspicious behaviors, we also propose a deep reinforcement learning-based graph searching (DRL-GS) strategy to crop the huge function call graph of the entire software to timely report high-risk threat behaviors with minimized resource consumption. By instancing 1-day attacks on a simulated beyond 5G IoT system, the performance of HSESR is trustfully competitive against existing baselines, and the efficiency of threat detection was increased by 21.63%.

Industrial Control System (ICS) depends on the underlying Programmable Logical Controllers (PLCs) to run. As such, the security of the internal control logic of the PLCs is the top concern of ICS. Reversing analysis and forensic work against PLC require extracting control logic from the control application running inside PLC, which is still an unresolved problem. To address the challenge, we propose a PLC decompile framework named CLEVER, which can analyze the control application and extract the control logic. First, we propose a simulation execution based code extraction method, which is utilized to filter the control logic related data. Then, to normalize the control application decompile process, an intermediate representation (IR) is designed, which can simplify the analysis process and enhance the extensibility of CLEVER. Finally, a heuristic data flow analysis algorithm is proposed to find variable dependency, and a sequential parsing method is utilized to reconstruct the source code from the control application. To evaluate our work, real world PLC hardware and programming software are used for the experiment. We use 22 real-world, 58 hand-written, and 150 auto-generated control applications to demonstrate the usability, correctness, and operational efficiency of CLEVER.

As a promising technology, intelligent reflecting surface (IRS) enables future communications and networks to realize programmable data transmissions. Due to the untrustworthiness of the communication environment and the selfishness of wireless devices, secure and intelligent IRS resource management is still an open issue. In this paper, we aim to implement IRS resource scheduling with properties of security, intelligence, efficiency, and fairness. To realize the above goals, we propose the blockchain and multi-agent learning empowered incentive scheduling system for tamper-proof and undeniable IRS resource management. To overcome the low throughout and intensive computation issues of blockchain, we devise a hybrid framework combining traditional Satoshi-style and directed acyclic graph blockchain for IRS resource scheduling. Due to the storage limitation of wireless devices, an intelligent blockchain storage reduction mechanism is proposed, where a multi-dimensional multi-hierarchy feature-based scheme is designed to determine block storage priority. Based on this storage priority and device states, the selection of storage-reduction devices is formulated as a cooperative multi-agent decision problem. Then, a multi-agent deep reinforcement learning-driven scheme is proposed to determine reduction strategies. To facilitate IRS providers/subscribers participating in the proposed system and maintain the efficiency of resource scheduling, an auction-based incentive mechanism is devised. In this mechanism, we propose the IRS resource allocation scheme and the payment scheme to achieve economic robustness and high efficiency. Finally, security analysis and experiment analysis indicate the feasibility and effectiveness of the proposed IRS resource scheduling in intelligent reconfigurable networks.

Generative AI has garnered substantial attention due to the limited defect samples in the industrial Internet of Things (IIoT). However, addressing the challenge of few-shot defect detection in industrial edge networks remains a key issue. In this paper, we propose ABEL, a novel AI-generated content (AIGC)-based edge learning framework for fast and efficient few-shot defect detection. This framework facilitates fast few-shot defect detection by harnessing the capabilities of realistic sample synthesis and edge-based AIGC task execution. Specifically, we propose an energy-based model (EBM)-guided Langevin Markov chain Monte Carlo (L-MCMC) image generation algorithm, synthesizing high-resolution industrial defect samples for efficient few-shot defect detection. Then, we formulate a large-scale mixed cooperative-competitive AIGC computation offloading problem and propose an attention and memory-based multi-agent reinforcement learning (AMMARL) algorithm to ensure fast edge execution of heterogeneous defect samples generative tasks. Particularly, the challenges of partial observability and high-dimensional state space are addressed by introducing multi-head attention mechanisms and long-term memory modules. Comprehensive synthesis experiments are conducted utilizing real-world industrial datasets NEU-CLS and DeepPCB. The experimental results demonstrate the effectiveness of our framework and algorithm's effectiveness in efficiently synthesizing realistic industrial defect images and optimizing edge-based AIGC task execution.

Prompting collaboration is a promising choice to enhance the performance of Vehicle Trajectory Prediction (VTP) in the Internet-of-Vehicles (IoV). This paper designs an edge-assisted collaborative VTP framework that employs Roadside Units (RSU) to associate vehicles and encourages RSUs to collaborate to realize VTP. A novel decentralized machine learning method, called Hierarchical Swarm Learning (HierSL), is proposed to improve efficiency and security in the collaborative learning process. HierSL is particularly suited for large-scale edge-assisted IoV systems with its two-layer hierarchical learning framework. HierSL allows nearby RSUs to form local organizations and performs lower-layer learning (local level) for knowledge integration within each organization. Over local organizations, an upper layer is constructed to integrate the knowledge of local organizations, thereby generating well-performed global models. Compared to vanilla Swarm Learning (SL), HierSL not only reduces the reliance on global communications but also cuts the cost of blockchain for collaborative VTP. Experiments are conducted on a real-world NGSIM US-101 data set, and the results show that the proposed method outperforms vanilla SL and is comparable to centralized learning.

In-network content caching is a fundamental functionality in Information Centric Network (ICN), which facilitates content distribution with reduced bandwidth consumption, lower network congestion, and faster content retrieval. However, traditional heuristic caching strategies often fail to handle dynamic ICN environments, and most leaning-based strategies cannot realize secured content caching for distributed ICN. In observation of these challenges, this paper investigates collaborative content caching in ICN, and proposes a novel algorithm, called Swarm Reinforcement Learning (SRL), for designing a secured caching mechanism in distributed ICN caching platform. SRL inherits salients features from both Swarm Learning (SL) and Deep Reinforcement Learning (DRL): it enables a fully decentralized learning process and strictly guarantees the privacy and security of data and models during collaborative caching by leveraging the blockchain technique; SRL also lets local ICN router interact with the local environment and integrate the learned knowledge with other ICN routers for constructing a collaborative caching strategy that maximizes the long-term reward of the entire ICN caching platform. We carry out systematic experiments to evaluate the performance of the proposed method. The results show that SRL-based collaborative caching outperforms state-of-the-art caching strategies in terms of cache hit rate and content retrieval delay, and also improves the stability of the ICN caching platform.

Federated semi-supervised learning (FSSL) has been perceived as a promising approach that leverages semi-supervised learning and federated learning (FL) to provide powerful privacy preservation while reducing the burden on human supervision. However, due to the lack of strict participant identification and the significant proportion of unlabeled samples, FSSL is more susceptible to covert backdoor attacks than traditional machine learning. To validate this speculation, a novel semantic-perturbed stealthy backdoor attack (SemSBA) scheme is proposed for FSSL-based systems. In SemSBA, we select original natural semantic features in the unlabeled training samples as backdoor triggers and then generate poisoned samples by adding adversarial perturbations that move them across the model decision boundary. With SemSBA, the adversary can trigger the hidden backdoor in the victim model during the inference stage without any deliberate modifications on testing samples. To further improve the strength and robustness of the attack, a pseudo label steering enhancement strategy is also designed to perturb the weakly-augmented version of unlabeled samples to induce target pseudo label allocations. Additionally, to improve the attack success rate, we amplify the weight of the local backdoored model during FSSL’s model aggregation process to manipulate the game between benign clients and malicious clients. Extensive experiments based on two benchmark datasets demonstrate that the proposed SemSBA scheme can achieve comparable stealthiness against existing attacks.

Differentially private distributed machine learning protects privacy by injecting artificial noise to the computing results. To further improve energy efficiency, the natural noise in the wireless environment can be used to protect privacy. In this paper, we study the problem of coded distributed machine learning over Gaussian multiple-access wireless channels to achieve differential privacy by exploiting the natural noise. Firstly, we propose an aggregation scheme using differentially private Lagrange encoding in a wireless environment, where the local computing results are uploaded to the master through orthogonal channels. Then, we develop an achievable privacy protection level to illustrate the impact of transmit power and power allocation on privacy. Additionally, we establish a theoretical convergence upper bound of the proposed scheme, providing a clear understanding of the potential limitations and capabilities of the system. Finally, we demonstrate a trade-off between system resource settings, convergence, and privacy protection levels through experiments. Specifically, increasing the signal-to-noise ratio (SNR) and power allocated for gradient computation leads to a decrease in the privacy protection level of the system and an increase in training accuracy. Moreover, reducing the dataset partitions results in better training accuracy.

The existing Artificial Intelligence (AI)-based industrial defect detection methods have received extensive attention in the industrial Internet of Things (IoT). However, due to the limited defect samples, it is difficult for discriminative models to achieve better performance in imbalanced learning. In addition, the privacy concerns surrounding sensitive information hinder the sharing of synthetic industrial data. In this paper, we propose a novel framework called the Differentially Private Generative AI-empowered Digital Twin (DPG-DT) framework, aiming to synthesize realistic samples while satisfying differential privacy and empowering the construction of digital space and its connection with physical space. Specifically, the core of the DPG-DT framework is the proposed Private Synthetic Industry Energy-guided model (PSIE), in which we privatize the energybased model-empowered Langevin Markov Chain Monte Carlo (MCMC) sampling method with Gaussian noise and random response. Our method could replace the conventional generator while guaranteeing privacy. Extensive experiments on real-world industrial datasets NEU-CLS and DeepPCB demonstrate that the proposed framework is capable of generating synthetic industrial images with both high fidelity and differential privacy. Moreover, the achieved downstream accuracy outperforms baselines by 23.9 % in industrial scenarios.

Today, with more and more devices in the industrial control system (ICS), the risk becomes higher and brings more attack surfaces. The need for reliable anomaly detection systems is increasing. Traditional SCADA-based detection systems deployed are difficult to assess large-scale control systems accurately, and novel AI-based technologies struggle to ensure timely response. In this paper, we propose an edge computing assisted delay-aware anomaly detection (ECADA) scheme for ICS, which considers both the accuracy and timeliness, and ensures that abnormal conditions can be accurately detected and handled in a short time. First, we model the components in ICS as three layers, taking network resources, delay, and reliability into consideration. Second, we convert the anomaly detection procedure into a decision making problem. By dividing the warning capabilities into various levels, the flexibility of the anomaly detection system is enhanced. Third, we cast a mixed-integer linear programming (MILP) problem to find the efficient anomaly detection mechanism, so that it can be dynamically scheduled to achieve the tradeoff between reliability and timeliness. We use an real-world industrial system dataset for experimental evaluation. By comparing with various traditional anomaly detection methods, it is proved that ECADA can always ensure reliable response of anomaly detection system in various network environments.

The emerging federated learning (FL) enables distributed data mining for Internet of Things (IoT) big data while avoiding data outsourcing privacy risks via local data training and knowledge (i.e., model) sharing. However, only simplified local knowledge sharing will also cause user privacy leaks due to advanced attacks (e.g., model inversion or gradient leakage). Further, how to realize fine-grained and personalized privacy protection for IoT users is still a challenge. In this paper, we first propose a hierarchical cloud-edge orchestrated federated learning architecture for IoT, named HCE-FL, which aims to provide intelligent and distributed data analysis for IoT users. To address the FL privacy issues, we then design a multi-level access control encryption-based IoT knowledge sharing approach for HCE-FL. In our approach, IoT users could be classified into different levels according to their individual privacy requirements. In addition, the proposed multi-level access control encryption algorithm could ensure the confidentiality of the IoT knowledge flow, which runs through local clients, edge sanitizers, and cloud servers in HCE-FL. Moreover, security theoretical analysis shows that our HCE-FL could satisfy“no read” and no write” security rules for the mandatory IoT knowledge access control. Finally, we conduct experiments based on classic MNIST and CIFARIO datasets to evaluate our HCE-FL. The experimental results demonstrate that our solution can achieve personalized privacy-preserving FL without losing IoT data availability and users can obtain better model accuracy and convergence rate through secure IoT knowledge access and sharing.

With increasingly strict data privacy regulations, federated learning (FL) has become one of the most often heard machine learning techniques due to its privacy-preserving trait. To efficiently implement the FL intelligence, researchers recently resort to a newly emerged computing paradigm, mobile-edge computing (MEC), and bring about a burst of works. However, most existing works neglect practical issues in MEC systems, e.g., device heterogeneity, unstable channel conditions, and unknown user mobility. Any of them, if not handled properly, can cause fatal failures to FL. This article proposed a novel FL framework, called multicore FL (MC-FL), to help FL intelligence land successfully on realistic MEC systems. A distinct feature of MC-FL is maintaining and training multiple global models (GMs) that exhibit different tradeoffs between learning performances and computational complexity. While this modification seems simple, it can effectively handle the device heterogeneity and device status variations, and improve the compatibility and robustness of FL. Furthermore, MC-FL employs a partial client participation scheme that allows participating clients to vary across time. This enables MC-FL to function under uncertain mobile environments. We rigorously prove the convergence of the designed MC-FL framework. In particular, we propose an online client scheduling scheme for MC-FL to judiciously schedule clients for training multiple GMs in a manner that minimizes the completion time of MC-FL. We also provide a service provisioning scenario with MC-FL to show how service subscribers could benefit from multiple GMs and improve their Quality of Experience (QoE). We evaluate our method on real-world data sets, and the results show that MC-FL outperforms state-of-the-art benchmarks.

Cloud and edge computing are gradually used to achieve complex energy operation control and massive information processing in conventional power grid. Meanwhile, with the tremendous number of distributed energy resources and power equipment integrated into the smart grid enabled by cloud and edge computing, the centralized distribution network cannot realize the flexible and realtime energy supply due to the unpredictable and wide distribution of distributed energy resources, which will further deteriorate the stability of smart grid. To solve those problems, this article proposes energy centric smart grid to achieve power dispatching with the help of cloud-edge computing. Our solution uses energy caching and energy multiple addressing of the edge router to eliminate the intermittency of renewables and speed up energy response. For the stability of energy market and to encourage users to participate in power dispatching, a cloud-edge computing-driven energy cache orchestration mechanism is designed. The empirical results show that the response time is greatly reduced to meet the stringent quality of service requirement in smart grid integrated with distributed energy resources.

Differential-private federated learning (DP-FL) has emerged to prevent privacy leakage when disclosing encoded sensitive information in model parameters. However, the existing DP-FL frameworks usually preserve privacy homogeneously across clients, while ignoring the different privacy attitudes and expectations. Meanwhile, DP-FL is hard to guarantee that uncontrollable clients (i.e., stragglers) have truthfully added the expected DP noise. To tackle these challenges, we propose a heterogeneous differential-private federated learning framework, named HDP-FL, which captures the variation of privacy attitudes with truthful incentives. First, we investigate the impact of the HDP noise on the theoretical convergence of FL, showing a tradeoff between privacy loss and learning performance. Then, based on the privacy-utility tradeoff, we design a contract-based incentive mechanism, which encourages clients to truthfully reveal private attitudes and contribute to learning as desired. In particular, clients are classified into different privacy preference types and the optimal privacy-price contracts in the discrete-privacy-type model and continuous-privacy-type model are derived. Our extensive experiments with real datasets demonstrate that HDP-FL can maintain satisfactory learning performance while considering different privacy attitudes, which also validate the truthfulness, individual rationality, and effectiveness of our incentives.

In order to fulfill the requirements of Intelligent Transportation Systems (ITS) on ultra-delay service response, task scheduling for trajectory analysis is being shifted from the data center into the network edge of ITS. Such a decentralized paradigm motivates the computing power of the edge device and makes traditional analysis tasks open to the users around ITS. However, since these ITS users have differentiated identities and roles with differentiated security demands and privacy protection, assigning tasks for different users requires identifying and assessing the vulnerability of edge intelligence entities (EIEs). Otherwise, sensitive tasks assigned to the vulnerable EIEs will extremely increase the security risks of industrial control networks. To solve these problems, this paper proposes a vulnerability-aware task scheduling (VATS) mechanism, which integrates vulnerability assessment and access control. With VATS, secure EIEs can obtain more permissions and join in the privacy-sensitive trajectory analysis task, which is essential to enhance privacy protection at edges and ultimately improve the efficiency of task scheduling. The simulation results demonstrate the validity of the proposed scheme to defend insecure task scheduling like trajectory analysis.

The emergence of Digital Twin Edge Networks (DTENs) achieves the mapping of real physical entities to digital models of cyberspace. By offloading real-time mobile data to Mobile Edge Computing (MEC) servers for processing and modeling, communication-efficient Digital Twin (DT) services could be achieved. However, the spatio-temporal dynamic DT service demand stochastically generated by mobile users easily causes service congestion, which challenges the long-term DT service stability. Meanwhile, current DT services still lack long-term effective incentive designs for participants. To solve these issues, we design an incentive-based congestion control scheme for stochastic demand response in DTENs. First, we adopt the Lyapunov optimization theory to decompose the long-term congestion control decision into a sequence of online edge association decisions, with no need for future system information. We then present a contract-based incentive design to optimize the long-term profit of the DT service provider, comprehensively considering the delay sensitivity, incentive compatibility, and individual rationality. Finally, experimental simulations are carried out to verify the superiority of the proposed scheme with the base station dataset of Shanghai Telecom. Theoretical and simulation analysis demonstrates that compared with benchmarks, our scheme could effectively avoid long-term service congestion with an arbitrarily near-optimal profit.

Recently, wireless edge networks have realized intelligent operation and management with edge artificial intelligence (AI) techniques (i.e., federated edge learning). However, the trustworthiness and effective incentive mechanisms of federated edge learning (FEL) have not been fully studied. Thus, the current FEL framework will still suffer untrustworthy or low-quality learning parameters from malicious or inactive learners, which undermines the viability and stability of FEL. To address these challenges, the potential social attributes among edge devices and their users can be exploited, while not included in previous works. In this paper, we propose a novelSocialFederatedEdgeLearning framework (SFEL) over wireless networks, which recruits trustworthy social friends as learning partners. First, we build a social graph model to find like-minded friends, comprehensively considering the mutual trust and learning task similarity. Besides, we propose a social effect based incentive mechanism for better personal federated learning behaviors with both complete and incomplete information. Finally, we conduct extensive simulations with the Erdos-Renyi random network, the Facebook network, and the classic MNIST/CIFAR-10 datasets. Simulation results demonstrate our framework could realize trustworthy and efficient federated learning over wireless edge networks, and it is superior to the existing FEL incentive mechanisms that ignore social effects.

Adaptive traffic control systems are gaining attention in recent years as traditional hand-crafted traffic control experiences performance fall-offs with increasingly complicated metropolitan traffic patterns. This paper studies a learning automata (LA)-based traffic signal control scheme that adapts to real-time traffic patterns and optimizes traffic flows by dynamically changing the green split timings. A novel LA algorithm, called K-Neighbor Multi-Agent Learning Automata (KN-MALA), is proposed to learn the optimal decision online and adjust the traffic light accordingly in an attempt to minimize the overall waiting time at an intersection. In particular, KN-MALA employs an online distributed learning framework that integrates the traffic condition of neighboring intersections to efficiently learn and infer optimal decisions for large-scale traffic signal systems. Furthermore, a parameter insensitive update mechanism is designed for KN-MALA to overcome the instability caused by initialization variations. Experiments are conducted on real-world traffic patterns of Sioux Falls City and the performance of the proposed algorithm is compared with the pre-timed traffic light control scheme and an adaptive traffic light control scheme based on single-agent learning automata. The results show that the proposed algorithm outperforms the other schemes in terms of quick traffic clearance under various traffic patterns and initial conditions.

The traffic anomaly analysis in IoT-based intelligent transportation system (ITS) is crucial to improving public transportation safety and efficiency. The issue is also challenging due to the unbalanced distribution of anomaly data in IoT-based ITS, which may cause overfitting or underfitting in the training phase. However, some research on traffic anomaly analysis injected limited data to address the shortage of anomaly samples or even neglects this issue, which overlooks the potential representation of nodes in graph neural networks. In this paper, we propose an improved contrastive GNN-based learning framework for traffic anomaly analysis that alleviates the problem of imbalanced datasets in the training phase. In this framework, we provide a graph augmentation approach with coupled features to learn different views of graph data. Besides, we design an effective training method based on the contrastive loss for our framework, which can learn the better performance of latent representations utilized in the downstream tasks. Finally, we conduct extensive experiments to evaluate the performance of our proposed frame-works based on real-world datasets. We demonstrate that our framework achieves as high as 6.45% precision improvement compared to the state-of-the-art.

The development of backhaul/fronthaul networks can enable low latency and high reliability, but nodes in future networks like Internet of Things (IoT) can conduct malicious activities like flooding attack and DDoS attack, which can decrease QoS of smart backhaul/fronthaul network. Timely detection of malicious nodes in future networks is significant for low-latency backhaul/fronthaul networks. However, conventional supervised learning-based detection models require abundant malicious training samples, while capturing adequate malicious samples can not meet the requirement of timely detection. In this paper, we propose a novel few-shot malicious node detection system for improving QoS of IoT backhaul/fronthaul network, which can detect malicious nodes with unknown malicious activities through a limited number of network traffic samples. In our proposed system, we first design a fresh IoT traffic sample processing approach, which integrates normal activity samples and known malicious activity samples to generate training pairs. Then, we design a metric learning-based malicious node detection model training method, which employs a contrastive loss over distance metric to distinguish between similar and dissimilar pairs of samples. Besides, the trained model can detect nodes with unknown malicious activities by comparing real-time samples with few-shot samples of malicious nodes. Finally, the proposed system is evaluated on a real-world IoT network dataset named N-BaIoT. The exhaustive experiment results show that our model can achieve an average accuracy around 97.67 % when detecting malicious nodes with unknown malicious activities, which is comparable to state-of-the-art supervised learning models while our model only needs 5-shot samples of malicious node.

The existing Underwater Internet of Things (UIoT) is based on the IP architecture, which is not conducive to the efficient storage and distribution of huge amounts of content generated in underwater. Actively pushing all content to users causes much unnecessary resource consumption in the UIoT. The information-centric networking (ICN) architecture opens new horizons up for these challenges. However, the slowness of underwater propagation speed makes traditional ICN not suitable for UIoT, especially considering about delay time. In this article, we propose an information-centric wireless sensor networking scheme with water-depth-aware content caching (ICWSN-WDA) to solve the above challenges. First, we design a naming scheme and a hybrid communication mode suitable for ICWSN-WDA. The communication mode in underwater we design is divided into push and pull traffic, which balances energy consumption and delay time. Second, we define a push level to decide which water depth the content actively pushes to, finding a suitable junction point of two modes. Third, as the water depth is deeper, it becomes more difficult to replace the sensor battery. To save energy consumption of deep-water sensors, the water-depth-aware caching mechanism is proposed based on water depth, popularity, and senor energy. Our extensive evaluation confirms the effectiveness of our proposed scheme, and it balances energy consumption constraints and latency.

Recently, edge artificial intelligence techniques (e.g., federated edge learning) are emerged to unleash the potential of big data from Internet of Things (IoT). By learning knowledge on local devices, data privacy preserving and Quality of Service (QoS) are guaranteed. Nevertheless, the dilemma between the limited on-device battery capacities and the high energy demands in learning is not resolved. When the on-device battery is exhausted, the edge learning process will have to be interrupted. In this article, we propose a novel wirelessly powered edge intelligence (WPEG) framework, which aims to achieve a stable, robust, and sustainable edge intelligence by energy harvesting (EH) methods. First, we build a permissioned edge blockchain to secure the peer-to-peer (P2P) energy and knowledge sharing in our framework. To maximize edge intelligence efficiency, we then investigate the wirelessly powered multiagent edge learning model and design the optimal edge learning strategy. Moreover, by constructing a two-stage Stackelberg game, the underlying energy-knowledge trading incentive mechanisms are also proposed with the optimal economic incentives and power transmission strategies. Finally, simulation results show that our incentive strategies could optimize the utilities of both parties compared with classic schemes, and our optimal learning design could realize the optimal learning efficiency.

In the era of the sixth generation (6G), the deployment of massive Internet of Things (IoT) generates and processes large amounts of data, resulting in high energy demand and huge challenges to the energy-limited IoT devices. To achieve green and sustainable communication, energy harvesting is a feasible technology to prolong the lifetime of IoT. However, the existing energy harvesting architecture cannot guarantee the privacy of energy users while improving the intelligence and effectiveness of energy transmission. To solve these issues, we propose a differential privacy and intelligent reflecting surface empowered privacy-preserving energy harvesting framework for 6G-enabled IoT. First, a secure and intelligent energy harvesting framework is designed, which includes an intelligent reflecting surface-aided radio frequency power transmission mechanism and a differential privacy-based energy harvesting mechanism. Second, an exponential mechanism-based privacy-preserving energy harvesting scheme is established, where we analyze the adversary mode, propose the differential privacy-enabled location-preserving algorithm, and provide security analysis and proof. Third, we quantify the user satisfaction for energy harvesting and propose a deep reinforcement learning empowered resource allocation scheme to maximize the weighted satisfaction of all system users. Finally, simulation results show the effectiveness of the proposed secure and intelligent energy harvesting architecture for 6G IoT.

Accessibility to smart devices provides opportunities for side-channel attacks (SCAs) on artificial intelligent (AI) models in the intelligent Internet of Things (IoT). However, the existing literature exposes some shortcomings: 1) incapability of quantifying and analyzing the leaked information through side channels of the intelligent IoT and 2) inability to devise efficient and accurate SCA algorithms. To address these challenges, we propose a side-channel fuzzy analysis-empowered AI model extraction attack in the intelligent IoT. First, the integrated AI model extraction framework is proposed, including power trace-based structure, execution time-based metaparameters, and hierarchical weight extractions. Then, we develop the information theory-based analysis for the AI model extraction via SCA. We derive a mutual information-enabled quantification method, theoretical lower/upper bounds of information leakage, and the minimum number of attack queries to obtain accurate weights. Furthermore, a fuzzy gray correlation-based multiple-microspace parallel SCA algorithm is proposed to extract model weights in the intelligent IoT. Based on the established information-theoretic analysis model, the proposed fuzzy gray correlation-based SCA algorithm obtains high-precision AI weights. Experimental results, consisting of simulation and real-world experiments, verify that the developed analysis method with the information-theoretic perspective is feasible and demonstrate that the designed fuzzy gray correlation-based SCA algorithm is effective for AI model extraction.

Energy harvesting (EH) is a promising and critical technology to mitigate the dilemma between the limited battery capacity and the increasing energy consumption in the Internet of everything. However, the current EH system suffers from energy-information cross threats, facing the overlapping vulnerability of energy deprivation and private information leakage. Although some existing works touch on the security of energy and information in EH, they treat these two issues independently, without collaborative and intelligent protection cross the energy side and information side. To address the aforementioned challenge, this article proposes a joint protection framework of energy security and information privacy for EH with an incentive federated learning approach. First, we design a federated-learning-based malicious energy user detection method according to energy status and behaviors to provide energy security protection. Second, a differential-privacy-empowered information preservation scheme is devised, where sensitive information is perturbed and protected by the customized demand-based noise. Third, a noncooperative-game-enabled incentive mechanism is established to encourage EH nodes to participate in the joint energy-information protection system. The proposed incentive mechanism derives the optimal energy-information security strategy for EH nodes and achieve a tradeoff between the protection of energy security and information privacy. Evaluation results have verified the effectiveness of our proposed joint protection mechanism.

Digital Twin (DT) has become the key technology in the Intelligent Transportation Systems (ITS) in smart cities to keep the health and reliability of various DT requesters, such as private vehicles, public transportation, energy systems, etc. The combination of DT and ITS can further release the potential of participants in smart cities and guarantee their efficiency and reliability. Despite the advantages of DT-enabled ITS, not all requesters need the same level of DT service due to the highly dynamic nature of ITS. Safe and reliable matching between DT and ITS still needs to be resolved. To address these issues, we propose the blockchain-enabled Digital Twin as a Service (DTaaS) for ITS. First, we propose an on-demand DTaaS architecture to fully utilize the sensing capabilities of ITS and the macro perspective of DT. Second, a double-auction model and a price adjustment algorithm are proposed to realize the optimal DT matching for ITS requesters and ensure the benefits of participants. Third, a permissioned blockchain and a novel DT-DPoS consensus mechanism are established to enhance the security and efficiency of DTaaS. Simulation shows that the proposed DTaaS and double-auction can efficiently stimulate and facilitate DT transactions. The proposed DT-DPoS also has obvious advantages.

The ever-increasing traffic, various delay-sensitive services, and energy consumption-constrained requirements have brought huge challenges to the current communication networks in the vehicular ad-hoc networks (VANETs). These challenges motivate academia and industry to investigate novel architectures with powerful data transmission and processing capabilities for low-latency and high energy-efficiency vehicular communication. In this paper, we propose an artificial intelligence (AI) and intelligent reflecting surface (IRS) empowered energy-efficiency communication system for VANETs. First, we design a smart and efficient hybrid vehicular communication framework, where IRS-aided dedicated short-range communication and long term evolution-based cellular communication are combined for data transmission in VANETs. Secondly, an IRS-aided data transmission is proposed to improve vehicular communication, in which the head vehicles selection method is designed. Based on the direct and IRS-reflecting signal propagation, fine-grained beamforming is achieved for directional vehicular transmission. Thirdly, a deep reinforcement learning (DRL) empowered network resource control and allocation scheme is proposed. In this scheme, we formulate an energy efficiency-maximizing model under the given transmission latency for VANETs and jointly optimize the settings of all participants to achieve efficient and low-latency communication. Finally, experimental results verify the effectiveness of our proposed communication system for VANETs.

Currently, the highly dynamic fog computing resource requirements introduced by the diverse services of the Internet of Things (IoT) result in an imbalance between computing resource providers and consumers. However, current computing resource scheduling schemes cannot cognize the dynamic resources available and do not possess decision-making or management capabilities, which leads to inefficient use of computing resources and a decreased quality of service (QoS). Balancing computing resources cognitively at the IoT edge remains unresolved. In this paper, a cognition-centric fog computing resource balancing (CFCRB) scheme is proposed for edge intelligence-enabled IoT. First, we propose a cognitive balance architecture with a cognition plane, which includes service demand monitoring, policy processing and knowledge storage of cognitive fog resources. Second, we propose the fog functions structure with sensing, interaction and learning functionalities, realizing the knowledge-based proactive discovery and dynamic orchestration of resource sharing nodes. Finally, a distributed edge learning algorithm is proposed to construct knowledge of the balance between computing resource helpers and requesters in cognitive fogs, which is further proved with mathematics. The simulation results indicate the efficiency of the proposed scheme.

The number of malware attempts that try to bypass the existing Network Intrusion Detection System (NIDS) is increasing. To detect illegal access to servers, deep analysis of the server-side network traffic has become increasingly important. However, the existing approaches have serious performance limitations in terms of real-time and accurate traffic detection. These limitations are mainly because of i) the rigid feature extraction and rule matching techniques of NIDS, which are insensitive to incremental network traffic, and ii) the strong correlation and coupling of malicious traffic to large normal traffic. To address these limitations, we propose a Few-shot Latent Dirichlet Generative Learning (FLAG) scheme for semantic-aware traffic detection in this paper. In FLAG, a Latent Dirichlet Allocation (LDA)-based pseudo samples generation algorithm is designated to augment the few-shot training data, which is essential to improve traffic classification accuracy. Furthermore, we propose a Fuzziness Recycle Method (FRM) to further improve the long short-term memory (LSTM)-based classifier’s robustness. Experimental results in real scenarios demonstrate that malicious traffic can be efficiently detected when only few-shot samples are learned. The results also reveal that the proposed scheme outperforms the state-of-the-art methods in detection accuracy.

As the security requirements increases, 5G slicing-enabled Internet of things needs to adopt end-to-end standalone networking, which limits the consensus convergence of mobile blockchain. Although the rise of FIBRE (Fast Internet Bitcoin Relay Engine) gives a huge promotion to block propagation, existing approaches cannot consider the influence of link outages among 5G slices on mobile blockchain. In this paper, we focus on decreasing the block propagation time among blockchain peers under the scenario where link outages among 5G slices exist. A predictive fast consensus convergence (PFCC) scheme is proposed for mobile blockchain over 5G slicing-enabled internet of things. In PFCC, federated semi-supervised learning is used to learn the features of withdraw packets, reroutes the packets of blockchain peers, and ultimately reduces the scale of link outages quickly. With PFCC, different blockchain peers located in standalone 5G slices of IoT can transact local sensing data more efficiently. To the best of our knowledge, this is the first work to improve the consensus convergence speed of mobile blockchain by optimizing communications between 5G slices. Experiments shows the feasibility of proposed scheme.

The evolution of deep learning has promoted the popularization of smart devices. However, due to the insufficient development of computing hardware, the ability to conduct local training on smart devices is greatly restricted, and it is usually necessary to deploy ready-made models. This opacity makes smart devices vulnerable to deep learning backdoor attacks. Some existing countermeasures against backdoor attacks are based on the attacker’s ignorance of defense. Once the attacker knows the defense mechanism, he can easily overturn it. In this paper, we propose a Trojaning attack defense framework based on moving target defense(MTD) strategy. According to the analysis of attack-defense game types and confrontation process, the moving target defense model based on signaling game was constructed. The simulation results show that in most cases, our technology can greatly increase the attack cost of the attacker, thereby ensuring the availability of Deep Neural Networks(DNN) and protecting it from Trojaning attacks.

Being the revolutionary future networking technology, 6G networks are expected to solve the issue of the drastic data demand and satisfy emerging applications and services. As one of the most feasible nanonetwork paradigms in 6G networks, molecular communication provides opportunities for targeted delivery, which solves nanodevice communication problems. The delivery communication is implemented by the biochemical reaction between the targeted molecule and the released specific Information Molecule (IM). The unprotected characteristic of this communication makes it vulnerable to be attacked on the biological process, adversely affecting the targeted delivery. In this paper, we propose the communication model for targeted delivery under the Bio-Denial of Service (Bio-DoS) attack. We first put forward the concept of the Bio-DoS attack and prove its feasibility. Moreover, we establish the diffusion-based multi-target model for the delivery process considering the combined characteristic of traditional communication and biology features. Simulation results show that the model accurately evaluate the signal receiving and processing in targeted delivery process under attack. This work is of a great significance to analysis the communication performance of the targeted delivery under attack.

The development of massive IoT has not only brought about a wealth of hardware resources but also brought about the problems of difficult data management, resource running and low efficiency. The emergence of sixth-generation (6G) network will not only provide faster data rates, more device connections but also bring ubiquitous virtual reality/augmented reality (VR/AR) services. In the 6G era, large-scale IoT devices will generate VR/AR service and resource requirements, and the network will also face unprecedented pressure to respond to the ubiquitous VR/AR requirements. To address the above issues, this article proposes the information-centric massive Internet of Things (IC-mIoT) suitable for 6G large-scale VR/AR content distribution to improve the efficiency of IC-mIoT and fully guarantee the Quality of Service (QoS) of users. First, this article introduces the blockchain for IC-mIoT nodes and proposes a new consensus mechanism Proof-of-Cache-Offloading (PoCO). Second, an architecture using blockchain-enabled IC-mIoT for VR/AR is proposed in this article. The massive IoT resources are fully integrated and scheduled to support large-scale VR/AR applications and IC-mIoT. Third, a Stackelberg game model and a cache index selection and calculation algorithm are formulated for blockchain-enabled cache offloading. The analysis and performance simulation results indicate the superiority and effectiveness of the proposed scheme.

The cybertwin and 6G-enabled Industrial Internet of Things (6G-IIoT) are the critical technologies that create the digital counterparts for physical systems and enable the near-instant interconnectivity in the industrial domain. It is in demand but challenging to conduct the integrated design for 6G-IIoT, which intertwines the cyber subsystems, such as control, communication, computing (3C), and the physical industrial factories and plants. Therefore, the cybertwin, which synchronizes between the digital counterparts and its physical entities during the system runtime, is the ideal proving ground for conducting the integrated design on the highly intertwined 3C of 6G-IIoT. However, the cybertwin lacks artificial intelligence to capacitate the automated integrated design for the 6G-IIoT. In this article, we first demonstrate the architecture of the machine-learning-based cybertwin for 6G-IIoT. Then, we leverage deep reinforcement learning (DRL) to conduct the integrated design via systematic trial and error in the cybertwin model, which is otherwise costly and dangerous in real industrial systems. Moreover, we invent the adaptive observation window for deep$Q$-network (AOW-DQN), which generates system states adaptive to the control system’s physical dynamics. Finally, the experimental results demonstrate the effectiveness and efficiency of our approach. To the best of our knowledge, we are the first to present the machine-learning-based cybertwin for carrying out the integrated design on the 3C for 6G-IIoT.

Nowadays the privacy issue arising in data mining applications has attracted much attention. In the context of distributed data mining, a major concern of the participant is that its privacy may be disclosed to other participants or a third party. To protect privacy, one can apply a differential privacy approach to perturb the data before sharing them with others, which generally causes a negative effect on the mining result. Thus there is a trade-off between privacy and the mining result. In this paper, we study a distributed classification scenario where a mediator builds a classifier based on the perturbed query results returned by a number of users. We propose a game theoretical approach to analyze how users choose their privacy budgets. Specifically, interactions among users are modeled as a game in satisfaction form. And an algorithm is proposed for users to learn the satisfaction equilibrium (SE) of the game. Experimental results demonstrate that, when the differences among users' expectations are not significant, the proposed learning algorithm can converge to an SE, at which every user achieves a balance between the accuracy of the classifier and the preserved privacy.

As major infrastructures are increasingly depending on electricity, the smart grid has become an important base for industrial manufacturing and residential living. Despite the benefits of smart grids, the reliability and continuity of power services are often threatened by severe nature disasters and human errors. In smart grids, the centralized and often large-sized grid equipment hinder the rapid recovery and flexible reconfiguration in an emergency. Meanwhile, the large amount of personal equipment and their invisibility make it difficult for the grid operators to utilize assets optimally and easily. In addition, since the power service is provided by multiple energy functions, which consists voltage transformation, transmission, and storage, only considering the restoration of power generation function will restrict the service capacity and lengthen the response time. To address these problems, this article proposes an energy function virtualization for smart grid to decouple the implementation of energy functions from the underlying physical infrastructure to speed up the deployment and test of energy functions. With the help of distributed infrastructure resources, manager can redeploy energy functions and accelerate the service response in smart grid. To motivate prosumers to contribute private function resources, an optimized network calculus performance assessment scheme and a game theory-based resource orchestration scheme are proposed. Simulation results show that proposed scheme can dynamically adjust the delay factor to shorten the emergency response time.

A large scale fast-growing data generated in intelligent transportation systems (ITS) has become a ponderous burden on the coordination of heterogeneous transportation networks, which makes the traditional cloud-centric storage architecture no longer satisfy new data analytics requirements. Meanwhile, the lack of storage trust between ITS devices and edge servers could lead to security risks in the data storage process. However, a unified data distributed storage architecture for ITS with intelligent management and trustworthiness is absent in the previous works. To address these challenges, this paper proposes a distributed trustworthy storage architecture with reinforcement learning in ITS, which also promotes edge services. We adopt an intelligent storage scheme to store data dynamically with reinforcement learning based on trustworthiness and popularity, which improves resource scheduling and storage space allocation. Besides, trapdoor hashing based identity authentication protocol is proposed to secure transportation network access. Due to the interaction between cooperative devices, our proposed trust evaluation mechanism is provided with extensibility in the various ITS. Simulation results demonstrate that our proposed distributed trustworthy storage architecture outperforms the compared ones in terms of trustworthiness and efficiency.

As increasingly advanced data collection and computing abilities are equipped by devices at the network edge, accompanying the vigorous development of machine learning, edge devices become both the consumer and provider of data. Due to the timeliness of some learning demands and the necessity of learning results, learning resources such as data collection, transmission, and learning should be unified and converged to meet timely learning needs. In this paper, we propose a framework to implement a distributed Learning-as-a-Service function by edge-cloud collaboratively integrating resources required by a learning task. First, the architecture of RALaaS and underlying information interaction are proposed. We then formulate the learning-resource allocation problem and propose a deep reinforcement learning based solution to minimize the required learning resource and achieve better accuracy. More precisely, an A3C algorithm is presented to schedule tasks among smart connected communities and aggregate models. Finally, evaluation results show that our proposed framework can improve the accuracy by 10% compared with conventional algorithms and save about 50% edge resources when 30 nodes participate in the learning task.

Standardization facilitates the management of Internet of Things (IoT) and expedites the generation of IoT big data. However, there is not yet a big data management architecture matching such IoT. Current methodologies, which mainly adopts Simple Network Management Protocol (SNMP), is defective in the following two aspects. First, facing ubiquitous sensor and actuator nodes, timeliness and scalability can hardly be assured by the centralized paradigm. Second, existing management infrastructure cannot perform data analysis and is thus not smart enough, which wastes the value of big data. To address these issues, we propose a big data management architecture for standardized IoT. First, we design a scalable and smart SNMP, which has a hierarchical and decentralized paradigm, and is embedded with edge MapReduce to perform distributed big data analysis. Second, we put forward an Edge MapReduce-based Random Matrix Model (RMM) algorithm for anomaly detection in IoT, which is parallelized and particularly suitable for high-dimensional big data. Third, we conduct a case study of smart grids, where the architecture is implemented using virtual machines and deployed to detect malfunctions in electrical grids. Experiment results demonstrate that the architecture has good performance in terms of timeliness and scalability.

Advanced driver-assistance systems (ADASs) have been proposed as an alternative to driverless vehicles to provide support for automotive vehicle decisions. As a significant driving force for ADASs, the augmented reality (AR) provides comprehensive location-based content services for in-vehicle consumers. With the increase in request for information sharing, the current standalone mode of ADASs needs a shift to the multiuser sharing mode. In this article, to address the high mobility and real time requirements of ADASs in 5G environments, and also to address the resource orchestration and service management of big data in intelligent transportation systems, we integrate the information-centric network (ICN) and the network function virtualization (NFV) with ADASs to support an efficient AR-assisted content sharing and distribution. This integration eliminates the imbalance between the content requests and the resource limitation by splitting the virtual resources and providing an on-demand network and resource slicing in ADASs. We propose an incentive trading model for assistance content caching services and also propose a novel mechanism for optimal content cache allocation. Our extensive evaluation confirms that our proposed mechanism outperforms the past literature in terms of the cache hit ratio and latency.

Individually reinforcing the robustness of a single deep learning model only gives limited security guarantees especially when facing adversarial examples. In this article, we propose DeSVig, a decentralized swift vigilance framework to identify adversarial attacks in an industrial artificial intelligence systems (IAISs), which enables IAISs to correct the mistake in a few seconds. The DeSVig is highly decentralized, which improves the effectiveness of recognizing abnormal inputs. We try to overcome the challenges on ultralow latency caused by dynamics in industries using peculiarly designated mobile edge computing and generative adversarial networks. The most important advantage of our work is that it can significantly reduce the failure risks of being deceived by adversarial examples, which is critical for safety-prioritized and delay-sensitive environments. In our experiments, adversarial examples of industrial electronic components are generated by several classical attacking models. Experimental results demonstrate that the DeSVig is more robust, efficient, and scalable than some state-of-art defenses.

The Internet of Multimedia Things (IoMT) has become the backbone of innumerable multimedia applications in various fields. The wide application of IoMT not only makes our life convenient but also brings challenges to service discovery. Service discovery aims to leverage location information and trust evidence scattered in a variety of multimedia applications to find trusted IoMT devices that can provide specific service in target areas. However, the eavesdropping and tampering to these sensitive IoMT data during the trust propagation process invalidate the service discovery process. To address these challenges, we propose Secure Service Discovery (SSD) for IoMT using cross-blockchain-enabled fog computing. To resist the tampering and eavesdropping during the trust propagation process, a scalable cross-blockchain structure consisting of multiple parallel blockchains is first proposed based on fog, in which different parallel blockchains can be orchestrated to propagate encrypted location information and trust evidence of different applications. Moreover, to enable a cross-blockchain structure to leverage encrypted location information and trust evidence to find trusted IoMT devices in preset areas, a novel privacy-preserving range query is proposed to query and aggregate trust evidence. Security analysis and simulations are carried out to demonstrate the effectiveness and security of the proposed SSD.

Intelligent embedded-enable smart manufacturing is an important infrastructure for future industries. Increasing security threats are disturbing the normal operations of smart manufacturing. As a novel type of threat, an advanced persistent threat (APT) has the novel features of strong concealment, latency, and long-term entanglement, which can penetrate the core systems of smart manufacturing, especially for intelligent embedded systems, and cause great destruction from the cyber side to physical side. However, the existing security schemes cannot provide sustainable resource management, which causes the core system in smart manufacturing not to perform sustainable secure detection and defense against APTs. To address this challenge, this paper proposes a sustainable secure management mechanism for smart manufacturing against APTs. The proposed mechanism includes two parts: sustainable threat intelligence analysis and sustainable secure resource management. Sustainable threat intelligence analysis provides sustainable discovery of the indications of potential APTs, which has features of a weak signal, low correlation, and slow time variation. The sustainable secure resource management provides deep and continuous protection for intelligent embedded systems in smart manufacturing. The evaluations show the defense capabilities and the feasibility of the proposed mechanism.

Network edge equipment has generated a large amount of fast- growing data, which has placed a heavy burden on the collaboration of heterogeneous networks. Due to the diversity of edge computing application scenarios, many new requirements are advocated for unified data storage management, such as latency and processing efficiency. Traditional centralized cloud storage can no longer meet the on- demand of edge computing in the case of a surge in data volume. Therefore, a unified storage architecture is required for the current improvements in computational offloading schemes and storage optimization algorithms. To solve these challenges and make data intelligent collaborative storable, this paper proposes a novel unified storage architecture for big data in the edge-cloud, which supports edge services in order to extend Hadoop at the edge. The functions of the edge nodes are proposed to synchronize the edge nodes of the same neighborhood and store data dynamically via Q- learning based on popularity, in order to mitigate network load pressure and improve the efficiency of edge services. An intelligent scheme that impacts the quality of service (QoS) through data marginal storage is proposed to improve the resource scheduling and to the distribution of storage space. Simulation results demonstrate the merits and efficiency of the proposed intelligent architecture is superior to the comparison schemes.

Due to the shortage and unbalance of medical resources, it is difficult for patients in the countryside to get high-quality and timely medical services from the central medical facility. Existing researches of fog e-health has the potential of providing real-time medical services for the countryside with body sensor networks (BSN), but there are two limitations. On one hand, because of the medical services requiring not only low-latency but also high-quality, constructing an AI e-health service on resource-constrained fog with edge AI is necessary but unsolved. On the other hand, because of the regional differences in disease risk, there is a lack of an effective mechanism to provide a customized fog AI e-health service for patients in different regions. To address these issues, a smart customized e-health (SCEH) framework is proposed in this paper to provide edge-intelligent and customized medical services for the countryside. Firstly, semantics-based lightweight and meticulous load management mechanism is designed to reduce data load and involve medical semantic. Secondly, model-ensemble based fog AI collaborative analysis mechanism is proposed for load balance and knowledge integration. Thirdly, an attention-weight based customized fog AI e-health generation mechanism is devised for regional medical model reconstruction. The simulation results demonstrate the effectiveness of SCEH which ensures both the accuracy and low latency of fog e-health with limited resource.

With the development of smart cities, the demand for artificial intelligence (AI) based services grows exponentially. The existing works just focus on cloud- edge or edge-device cooperative AI which suffers low learning efficiency of AI, while edge-to-edge cooperative AI is still an unresolved issue. Moreover, the existing researches concentrate on the computation offloading of the AI-based task, ignoring that it is a brain-like task performing sophisticated processing to raw data, which leads to the high latency and low quality of the learning services. To address these challenges, this paper proposes an on-demand learning offloading mechanism for edge-to-edge cooperative AI. Firstly, the principle of the learning capability and its offloading are proposed for the formal description of the learning resources migration. Secondly, the proposed mechanism realizes the bilateral learning offloading utilizing edge-to-edge and cloud-edge collaborations to handle AI-based tasks with high learning efficiency and resource utilization rate. Moreover, we model the edge-to-edge learning offloading allocation based on the concatenation of deep neural network (DNN) subtasks and their heterogeneous requirement of learning resources. Simulation results indicate the rationality and efficiency of the proposed mechanism.

Internet of Things(IoT) is an important part of the new generation of information technology and an important stage of development in the era of informatization. As a next generation network, Information Centric Network (ICN) has been introduced into the IoT, leading to the content independence of IC-IoT. To manage the changing network conditions and diagnose the cause of anomalies within it, network operators must obtain and analyze network status information from monitoring tools. However, traditional network supervision method will not be applicable to IC-IoT centered on content rather than IP. Moreover, the surge in information volume will also bring about insufficient information distribution, and the data location in the traditional management information base is fixed and cannot be added or deleted. To overcome these problems, we propose a name-based smart tracking system to store network state information in the IC-IoT. Firstly, we design a new structure of management information base that records various network state information and changes its naming format. Secondly, we use a tracking method to obtain the required network status information. When the manager issues a status request, each data block has a defined data tracking table to record past requests, the location of the status data required can be located according to it. Thirdly, we put forward an adaptive network data location replacement strategy based on the importance of stored data blocks, so that the information with higher importance will be closer to the management center for more efficient acquisition. Simulation results indicate the feasibility of the proposed scheme.

Software-defined networking (SDN) allows the smart grid to be centrally controlled and managed by decoupling the control plane from the data plane, but it also expands attack surface for attackers. Existing studies about the security of SDN-enabled smart grid (SDSG) mainly focused on static methods such as access control and identity authentication, which is vulnerable to attackers that carefully probe the system. As the attacks become more variable and complex, there is an urgent need for dynamic defense methods. In this paper, we propose a security function virtualization (SFV) based moving target defense of SDSG which makes the attack surface constantly changing. First, we design a dynamic defense mechanism by migrating virtual security function (VSF) instances as the traffic state changes. The centralized SDN controller is re-designed for global status monitoring and migration management. Moreover, we formalize the VSF instances migration problem as an integer nonlinear programming problem with multiple constraints and design a pre-migration algorithm to prevent VSF instances' resources from being exhausted. Simulation results indicate the feasibility of the proposed scheme.

Information-centric networking (ICN) is a novel networking architecture with subscription-based naming mechanism and efficient caching, which has abundant semantic features. However, existing defense studies in ICN fails to isolate or block efficiently novel content threats including malicious penetration and semantic obfuscation for the lack of researches considering ICN semantic features. More importantly, to detect potential threats, existing security works in ICN fail to use semantic reasoning to construct security knowledge-based defense mechanism. Thus ICN needs a smart and content-based defense mechanism. Current works are not able to block content threats implicated in semantics. Additionally, based on traditional computing resources, they are incompatible with ICN protocols. In this paper, we propose smart reasoning based content threat defense for semantics knowledge enhanced ICN. A fog computing based defense mechanism with content semantic awareness is designed to build ICN edge defense system. In addition, smart reasoning algorithms is proposed to detect implicit knowledge and semantic relations in packet names and contents with context communication content and knowledge graph. On top of inference knowledge, the mechanism can perceive threats from ICN interests. Simulations demonstrate the validity and efficiency of the proposed mechanism.

With the advent of 5G mobile communication systems, the evolution of the Internet of Vehicles (IoV) will be greatly promoted, and it will be able to meet people's demands extremely in terms of performance and functional requirements. Moreover, fog computing has been proposed at the edge of the 5G-enabled IoV architecture, to provide data storage, calculation, and interaction with vehicles. Furthermore, the Global Navigation Satellite Systems (GNSS) play a significant role in future 5G-enabled IoV environment. However, navigation systems usually lose effectiveness because of various factors and disturbances in the 5G-enabled IoV, such as severe weather, GNSS device failure, and skyscrapers that block GNSS signals. Meanwhile, since some measurements come from the NLOS path, this can result in very large positioning errors. To address these challenges, this paper proposes a GNSS-free emergency location-based service (LBS) using fog computing in 5G-enabled IoV. Firstly, we propose the topology structure of the 5G-enabled IoV to address the problem of GNSS failure. Then, the operation procedures of emergency location service and the hierarchical logical architecture of the fog based service networks are designed. Finally, a vehicle positioning algorithm is proposed based on semi-definite programming (SDP) for positioning errors in the 5G-enabled IoV environment. Simulation results verify that the method can achieve high-precision location estimation for the 5G-enabled IoV.

Mobile Edge Computing (MEC) is a novel platform to bring computation resources close to local users in vicinity constrained, obtaining the nickname of Cloudlet on the edge of the network. However, due to users' behaviors, computation resources demands show spatial and temporal dynamics among different Cloudlets, which is hardly to achieve on-demand computation workload balance management. While vehicles, unique for their mobility and powerful on- board equipments, could act as computation resources transporters breaking geographically restriction, which have potential to balance computation demands in the city. To address the issue above, in this paper, we design a novel computation demand response management (DRM) mechanism called Vehicle-to-Cloudlet (V2C), considering the mobility of vehicles, computation states of vehicles, and computation demands of Cloudlets. There exists two phases in V2C mechanism: cognitive phase and game phase, respectively. In cognitive phase, which Cloudlets are computation-scarce and which vehicles are potential computation resources can be cognized. Then, in game phase, to simulate computation resources trading process among Cloudlet service provider and individual vehicles, we formulate a price-based two-stage Stackelberg game, jointly maximizing the utility of the Cloudlet and the individual utility of each vehicles. We prove that unique Nash Equilibrium (NE) and Stackelberg Equilibrium (SE) exist in this game and propose a gradient iterative algorithm to obtain the optimal solution. Finally, numerical simulations show that our solution has good scalability and also encourages vehicles to trade their own computation resources to the Cloudlet.

Currently, blockchain technology has been widely used due to its support of transaction trust and security in next generation society. Using Internet of Things (IoT) to mine makes blockchain more ubiquitous and decentralized, which has become a main development trend of blockchain. However, the limited resources of existing IoT cannot satisfy the high requirements of on-demand energy consumption in the mining process through a decentralized way. To address this, we propose a decentralized on-demand energy supply approach based on microgrids to provide decentralized on-demand energy for mining in IoT devices. First, energy supply architecture is proposed to satisfy different energy demands of miners in response to different consensus protocols. Then, we formulate the energy allocation as a Stackelberg game and adapt backward induction to achieve an optimal profit strategy for both microgrids and miners in IoT. The simulation results show the fairness and incentive of the proposed approach.

Nowadays, benefit from more powerful edge computing devices and edge artificial intelligence (edge-AI) could be introduced into Internet of Things (IoT) to find the knowledge derived from massive sensory data, such as cyber results or models of classification, and detection and prediction from physical environments. Heterogeneous edge-AI devices in IoT will generate isolated and distributed knowledge slices, thus knowledge collaboration and exchange are required to complete complex tasks in IoT intelligent applications with numerous selfish nodes. Therefore, knowledge trading is needed for paid sharing in edge-AI enabled IoT. Most existing works only focus on knowledge generation rather than trading in IoT. To address this issue, in this paper, we propose a peer-to-peer (P2P) knowledge market to make knowledge tradable in edge-AI enabled IoT. We first propose an implementation architecture of the knowledge market. Moreover, we develop a knowledge consortium blockchain for secure and efficient knowledge management and trading for the market, which includes a new cryptographic currency knowledge coin, smart contracts, and a new consensus mechanism proof of trading. Besides, a noncooperative game based knowledge pricing strategy with incentives for the market is also proposed. The security analysis and performance simulation show the security and efficiency of our knowledge market and incentive effects of knowledge pricing strategy. To the best of our knowledge, it is the first time to propose an efficient and incentive P2P knowledge market in edge-AI enabled IoT.

Information-centric networking (ICN) is a significant next-generation network architecture with caching ability that greatly affects the network service performance. Because of limited caching space, on-demand cache configuration is an important issue to optimize redundant cache, shorten the time delay and reduce the traffic overhead. Existing works on caching mainly realize cache configuration in a single dimension such as content popularity or node weight, which cannot realize multilevel comprehensive decision-making for efficient on-demand caching services in complex ICN. Moreover, existing works just focus on centralized cache configuration, which causes low efficiency and high latency. To address aforementioned challenges, we propose an on-demand fog caching service for ICN, named FogCache, which is realized by perceiving synthetical content popularity, traffic and time cost, and node importance. Regional fog nodes are adapted to configure caching service at the edge of ICN, in which the additional complexity is low because just minimal byte information is added at the end of the data packets. FogCache achieves multiple dimensional synthesis of awareness unified by building a hierarchical factor structure into four layers: target layer, criteria layer, indicator layer, and observation layer. Simulation results show that the proposed FogCache gets better performances of caching than related schemes.

The diverse applications and high-quality services in satellite-enabled smart cities have led to geographical unbalance of computation requirements. Traditional centralized cloud services and massive migration of computing tasks result in the increase of network delay and the aggravation of network congestion. Deploying fog nodes at the network edge has become a way to improve the quality of service (QoS). However, the dynamic requirements and application in various scenarios still challenge the network, resulting in geographical unbalance of computing resource demands. Nowadays, computing resources of on-board computers and devices in the Internet of Vehicles (IoV) are abundant enough to mitigate the geographical unbalances in computing power demand. Efficient usage of the natural mobility of constantly moving vehicles to solve the problems above remains an urgent need. In this paper, vehicle mobility-based geographical migration model of vehicular computing resource is established for satellite-enabled smart cities. By using the road- status-awareness of fog nodes, the status of roads is precisely quantified as the basis for vehicle mobility- based resource migration. An incentive scheme that affects the vehicle path selection through resource pricing is proposed to balance the resource requirements and to geographically allocate computing resources. Simulation results indicate that the advantages and efficiency of the proposed scheme are significant.

As a next-generation networking architecture, information-centric networks (ICN) has strengthened the focus on physical location-independent content sharing, which introduces abundant semantic features and novel access approach. However, the semantic modeling of ICN is an unresolved problem, thus current ICN lacks the capabilities of smart content analysis and understanding to support the knowledge decision for optimized user experience. To address this issue, we propose a semantic ICN model, Sema-ICN, that can provide logically related information depending on content-relevance-based relationships extraction and name-based weight setting. Moreover, besides the great benefits brought into ICN by semantic features, Sema- ICN will also contribute to security protection against anomalous access, which usually the basis of further threats. In this paper, we additionally design a smart anomalous access detection scheme supported by Sema- ICN, in which semantic communities are partitioned utilizing spectral clustering according to content name with semantic attributes. And a forecast model is introduced to predict access situation based on triple exponential smoothing algorithm using historical request data, access traffic that is beyond the forecast results will be considered as anomalous. The simulation results demonstrate the efficiency of the proposed scheme. To the best of our knowledge, this work is the first to propose a novel semantic model for ICN.

Recently, an accelerating number of studies are dedicated to deploying various IoT applications in the information centric network paradigm which has lower system complexity than traditional network architectures. However, such a paradigm poses a number of security challenges especially when it is applied in real-time e-health applications. Firstly, it is difficult to ensure security of sensitive data in such a distributed data caching environment because after the data is published in the form of a packet to the information centric network (ICN), it is no longer controlled by the data publisher. Secondly, in some real-time e-health applications, terminal medical sensors are usually resource-constrained, limiting the direct adoption of expensive cryptographic primitives. In order to address these challenges, a resource-efficient secure data sharing scheme in information centric e-health system is proposed, one that utilizes ciphertext-policy attribute based encryption (CP-ABE) and adapts it to the above-mentioned system with respect to necessary security requirements. It also exploits computation resources of fog nodes and employs outsourcing cryptography to improve system efficiency.The evaluation demonstrates that the scheme can significantly reduce the computation overheads of the resource-constrained terminal medical devices, and can better support the real-time e-health applications.

Information Centric Network (ICN) is one of the promising architectures in the next generation networks. The content-based routing in ICN can satisfy the content distribution of large-scale data. For prompt content obtainment, it is important to realize the content analysis before the content reaches application layer. The novel characteristics of data naming in ICN make it possible to search and analyse content during the transmission of content, which can directly get the critical content without the process of the application layer. In this paper, we propose a MapReduce enabling content analysis architecture for ICN. MapReduce framework can realize the parallelization of content collection and analysis during the routing process. For more efficient content collection, we put forward an optimal selection for mapper nodes. Moreover, Convolutional Neural Network (CNN) is deployed in the MapReduce architecture providing further analysis for ICN content. The simulation result shows the advantages of the proposed architecture.

With integration between Internet-of-Things (IoT) and industrial systems, Industrial Internet of Things (IIoT) raises more efficiency and security access for monitoring and control. The IEEE 21451 standard, gradually accepted by manufacturers and users, aims to solve the problem of unified sensor communication standard for sensor networks, especially for wireless sensor networks. Although IEEE 21451-1-5 can realize the simple network management for wireless IIoT, efficient security access during the network management continues to be an unresolved problem. In this paper, we propose an efficient secure access scheme for IEEE 21451 based wireless IIoT using optimized Transducer Electronic Data Sheet (TEDS) and Management Information Base (MIB). First, MIB is adapted to index efficiently TEDS of IIoT, which is extended to record the parameters of network status. Second, optimized MIB is reconstructed to monitor and configure the attributes of IIoT management data. Third, MIB based Dynamic Role based Access Control (DRBAC) is proposed for flexible security access in wireless IIoT. Simulation results verify the advantages of the propose scheme. The proposed scheme is momentous to realize the simplifying, extensibility and security data access in wireless IIoT.

Extensive online knowledge sharing can be exploited to solve tasks better, faster and cheaper, and it garners considerable interest in institutional cooperation, learning communities, etc., while many technical problems, such as fair exchange in incentive design and security issues are waiting to be solved. Blockchain technique has high accountability and thus has potential to improve the transparency and security of knowledge sharing. In this paper, to address the above problems, first we propose a Reputation Based Knowledge Sharing system in blockchain, called RBKS. The aim of RBKS is to exploit the copyright protection of the knowledge owner using our proposed fine-grained access control system, and to achieve the paid-for content service which allows bystanders who are interested in the shared knowledge to pay a small fee for the access. Second, a reputation evaluation algorithm is introduced as the core of the incentive, and it may possibly be combined with stake in blockchain to form a hybrid stake for the RBKS system. Third, a blockchain and a trusted storage server are employed together for sharing and storing knowledge, and the main procedures are implemented with smart contract in blockchain to ensure secure execution and fairness. Finally, our analysis shows that the RBKS system is feasible and secure.

Domain-Flux malware is hard to detect because of the variable C&C (Command and Control) domains which were randomly generated by the technique of domain generation algorithm (DGA). In this paper, we propose a Domain-Flux malware detection approach based on DNS failure traffic. The approach fully leverages the behavior of DNS failure traffic to recognize nine features, and then mines the DGA-generated domains by a clustering algorithm and determinable rules. Theoretical analysis and experimental results verify its efficiency with both test dataset and real-world dataset. On the test dataset, our approach can achieve a true positive rate of 99.82% at false positive rate of 0.39%. On the real-world dataset, the approach can also achieve a relatively high precision of 98.3% and find out 197,026 DGA domains by analyzing DNS traffic in campus network for seven days. We found 1213 hosts of Domain-Flux malware existing on campus network, including the known Conficker, Fosniw and several new Domain-Flux malwares that have never been reported before. We classified 197,026 DGA domains and gave the representative generated patterns for a better understanding of the Domain-Flux mechanism.

With the growing popularity of mobile social networks, point-of-interest (POI) recommendation, which utilizes users' check-in data to suggest interesting places for users, has attracted much attention in recent years. The check-in data, containing time and location information, are closely related to the user's personal life. Due to privacy concerns, users are reluctant to share check-in data with the service provider (SP), which causes a negative effect on recommendations. It is important for the user to find a balance between privacy and recommendation quality. In this paper, we consider a POI recommendation scenario where an adversary can access the data that a user reports to the SP. The user sequentially decides whether to check in for the POI he has visited. A stochastic game model is proposed to analyze the interaction between the user and the adversary. To find a good policy for the user, two value iteration algorithms are applied. The proposed game has a large state set, which makes it difficult for policy learning. To deal with this problem, we use some tricks when implementing the minimax Q-learning algorithm, and a set of neural networks are trained to approximate the Q-functions. To evaluate the performance of the learning algorithms, we conduct a series of simulations by using real-world check-in data. Simulation results show that the proposed learning algorithms can help the user to make good decisions, in the sense that the user can get a high long-term return.

There is a wide range of interdisciplinary intersections between cyber security and artificial intelligence (AI). On one hand, AI technologies, such as deep learning, can be introduced into cyber security to construct smart models for implementing malware classification and intrusion detection and threating intelligence sensing. On the other hand, AI models will face various cyber threats, which will disturb their sample, learning, and decisions. Thus, AI models need specific cyber security defense and protection technologies to combat adversarial machine learning, preserve privacy in machine learning, secure federated learning, etc. Based on the above two aspects, we review the intersection of AI and cyber security. First, we summarize existing research efforts in terms of combating cyber attacks using AI, including adopting traditional machine learning methods and existing deep learning solutions. Then, we analyze the counterattacks from which AI itself may suffer, dissect their characteristics, and classify the corresponding defense methods. Finally, from the aspects of constructing encrypted neural network and realizing a secure federated deep learning, we expatiate the existing research on how to build a secure AI system.

Advanced communications and data processing technologies bring great benefits to the smart grid. However, cyber-security threats also extend from the information system to the smart grid. The existing security works for smart grid focus on traditional protection and detection methods. However, a lot of threats occur in a very short time and overlooked by exiting security components. These threats usually have huge impacts on smart gird and disturb its normal operation. Moreover, it is too late to take action to defend against the threats once they are detected, and damages could be difficult to repair. To address this issue, this paper proposes a security situational awareness mechanism based on the analysis of big data in the smart grid. Fuzzy cluster based analytical method, game theory and reinforcement learning are integrated seamlessly to perform the security situational analysis for the smart grid. The simulation and experimental results show the advantages of our scheme in terms of high efficiency and low error rate for security situational awareness.

Recently, fog computing has gained increasing attention in processing the computing tasks of the industrial Internet of things (IIoT) with different service popularity. In task-diversified fog computing-enabled IIoT (F-IIoT), the mismatch between expected computing efficiency and partitioned resources on fog nodes (FNs) may pose serious traffic congestion even large-scale industrial service interruptions. The existing works mainly studied offloading which type of computing tasks into FNs, but few studies enabled smart resource partitioning of FNs. In this paper, a service popularity-based smart resources partitioning (SPSRP) scheme is proposed for fog computing-enabled IIoT. We first exploit Zipf's law to model the relationship between popularity ranks and computing costs of IIoT services. Moreover, we propose an implementation architecture of the SPSRP scheme for F-IIoT, which decouples the computing control layer from data processing layer of IIoT through a specified SPSRP controller. Besides, a mobility and heterogeneity-aware partitioning algorithm is presented for extending SPSRP scheme to seamlessly support cross-domain resources partitioning. The simulations demonstrate that the SPSRP scheme can bring notable performance improvements on delay time, successful response rate and fault tolerance for fog computing to deal with the large-scale IIoT services.

In software-defined networks (SDNs), the abstracted control plane is its symbolic characteristic, whose core component is the software-based controller. The control plane is logically centralized, but the controllers can be physically distributed and composed of multiple nodes. To meet the service management requirements of large-scale network scenarios, the control plane is usually implemented in the form of distributed controller clusters. Cluster management technology monitors all types of events and must maintain a consistent global network status, which usually leads to big data in SDNs. Simultaneously, the cluster security is an open issue because of the programmable and dynamic features of SDNs. To address the above challenges, this paper proposes a big data analysis-based secure cluster management architecture for the optimized control plane. A security authentication scheme is proposed for cluster management. Moreover, we propose an ant colony optimization approach that enables big data analysis scheme and the implementation system that optimizes the control plane. Simulations and comparisons show the feasibility and efficiency of the proposed scheme. The proposed scheme is significant in improving the security and efficiency SDN control plane.

Information-centric networking (ICN) and software defined networking (SDN) has been perceived as a promising paradigm for integrating distributed generation (DG) into smart grid networks for flexibility and dynamic features. However, flexible, controllable and reliable time synchronization still remains an open issue for supervisory control and data sensing in smart grid. Firstly, for information-centric smart grid, smart grid entities may obtain available data from caching routers, and data delivery between caching routers with edge devices results in time synchronization requirements of on-path caching routers. Without on-path time synchronization of caching routers, it may lead to maliciously fluctuations if energy data for monitoring the energy supplying and consumption are collected and traversed at an inappropriate time. Secondly, as scale expanding and network environment of smart grid becomes complex and changeable, on-path time synchronization needs a unified and dynamic management and control. To address these issues, in this paper we propose a novel scheme of software- defined on-path time synchronization (SD-OPTS) scheme for information-centric smart grid. In proposed scheme, all on-path caching routers share the time stamps from master clock and synchronize the time of local clock during one-time synchronization process. Besides, SDN controller estimates on-path caching routers' sync error, before choosing the nearest nodes to implement accurate time synchronization. The simulation results demonstrate the efficiency of software-defined on-path time synchronization scheme. The SD-OPTS scheme supports the flexibility, controllability and reliability of time synchronization.

Fog computing, shifting intelligence and resources from remote cloud to edge networks, has the potential of providing low-latency for the end-to-end communication from data sources to users. However, it's hard to enhance resource-efficiency in the existing relatively static and proprietary framework of fog nodes due to the diversity of service requirements. With the growing deployment of fog computing, the overall resource consumption in fog will be huge without considering the efficient service provisioning in each fog node. On one hand, different fog users require diverse local services policies which are carried out within the fog nodes. Moreover, for one user, the requirements on services are time-varying. On the other hand, the processing strategies on different types of content (e.g. video, audio, etc.) are also distinct. These dynamic features impose the need for user-driven and content-based service reconstruction in order to achieve the high recycling utilization of resources of fog system. To this end, we propose a software-defined efficient service reconstruction (SDSR) scheme in fog using content awareness and weighted graph. Service reconstruction mechanism is devised to dynamically recycle modularized resources after mapping different contents to relevant operations. Weighted graph is introduced to schedule and optimize the services reconstruction in terms of resource saving during content-driven controlling. User-defined interfaces are designed to enable fog users to reconfigure the recyclable resource modules. Simulation results demonstrate that the service cost of each fog nodes is reduced significantly, thus promote efficient service provisioning for the whole fog system.

The privacy issue arising in data mining applications has attracted much attention in recent years. In the context of distributed data mining, the participant can employ data perturbation techniques to protect its privacy. Data perturbation generally causes a negative effect on the mining result, which means there is a trade-off between privacy and the mining result. In this paper, we study a distributed classification scenario where a number of users provide data to a mediator to train a classifier. Interactions among users are modeled as a game in satisfaction form. And an algorithm is proposed for users to learn the satisfaction equilibrium (SE) of the game. The basis idea is that the user gradually reduces the perturbation in data until it is satisfied with the classification accuracy. Experimental results based on real data demonstrate that, when the differences among users' expectations are not significant, the proposed learning algorithm can converge to an SE, at which every user achieves a balance between the classification accuracy and the preserved privacy.

E-health is one of the domains which will need more and more IoT solutions in the future, as it requires short delays for life-dependency situations and safe local storage spaces for privacy matters. The emergency context demands efficient communication and computing capacities, and the cloud vision as well as traditional TCP/IP way of communication cannot really suit these requirements. New approaches are currently used : the fog computing paradigm, which describe the IoT environment in a more relevant way than the cloud vision, provides new performances as low latency, local storage opportunities, end-to-user services. E-health fogs have attracted a lot of attentions. To realize the efficient communication, E-Health systems also introduce Content-Centric Network (CCN) approach, whose characteristics include quick response, content distribution, IoT oriented technology. Nowadays, the existing E-health fog or CCN are seen as separate and autonomous networks, but many applications could require that those E-health fog computing systems exchange data, share processing results, or subscribe to each other information flux. In this paper we propose a solution for E-health systems to communicate by combining fog computing and CCN to provide communication efficiency and local storage opportunities. Finally we use simulation to evaluate the delay performances of our proposal.

To enforce surrounding surveillance efficiently and reduce the heavy cost to deploy various infrastructures, mobile sinks are perceived to have potentials for utilization by wireless multimedia sensor networks (WMSNs). However, since high-mobility usually causes communication disconnections and the high re-transmission rate will consume more network resources, quality of experience (QoE) monitoring and control is a must that WMSNs with mobile sinks (MS-WMSNs) should provide satisfactory services with constrained resources. In this paper, we propose a novel QoE-named content-centric network paradigm for MS-WMSNs, which supports location independent networking and low redundancy data aggregation. Each network node constructs a hierarchical content naming tree (HCNT) negotiated by QoE parameters. The MS prioritizes the sensing data and caches them differentially by identifying these QoE parameters based content names. Simultaneously, to verify the feasibility, we design a stochastic network calculus model to analyse the performances of our proposed network paradigm at worst-case situation. Simulation results show that the proposed paradigm reduces end-to-end communication delay.

As a shared economy platform, Airbnb provides collaborative practices for customers and guides them to match with hosts' rooms. Based on the records and ratings, there is great significance attached to inferring the satisfaction between users and rooms. Several essential problems arise when evaluating satisfaction and matching. Data confidence and prediction bias influence the inference performance of the satisfaction. When two users stay in a room, the two users' joint satisfaction also deserves particular research because of the roommate effect. In this paper, the satisfaction is inferred considering confidence and prediction uncertainties. The satisfaction with the confidence uncertainty is modeled using a normalized variance of the Beta distribution. The algorithms for inferring satisfaction with the prediction uncertainties are divided into two parts: a weighted matrix factorization-based algorithm for individuals and a preference similarity-based algorithm for pairs. The problem can be reduced to a matching problem. Finally, extensive experiments show the effectiveness and accuracy of the proposed method.

As an extension for Internet of Things (IoT), Internet of Vehicles (IoV) achieves unified management in smart transportation area. With the development of IoV, an increasing number of vehicles are connected to the network. Large scale IoV collects data from different places and various attributes, which conform with heterogeneous nature of big data in size, volume, and dimensionality. Big data collection between vehicle and application platform becomes more and more frequent through various communication technologies, which causes evolving security attack. However, the existing protocols in IoT cannot be directly applied in big data collection in large scale IoV. The dynamic network structure and growing amount of vehicle nodes increases the complexity and necessary of the secure mechanism. In this paper, a secure mechanism for big data collection in large scale IoV is proposed for improved security performance and efficiency. To begin with, vehicles need to register in the big data center to connect into the network. Afterward, vehicles associate with big data center via mutual authentication and single sign-on algorithm. Two different secure protocols are proposed for business data and confidential data collection. The collected big data is stored securely using distributed storage. The discussion and performance evaluation result shows the security and efficiency of the proposed secure mechanism.

Recently, the conflict between exploiting the value of personal data and protecting individuals' privacy has attracted much attention. Personal data market provides a promising solution to this conflict, while determining the price of privacy is a tough issue. In this paper, we study the pricing problem in a setting where a data collector sequentially buys data from multiple data owners whose valuations of privacy are randomly drawn from an unknown distribution. To maximize the total payoff, the collector needs to dynamically adjust the prices offered to owners. We model the sequential decision-making problem of the collector as a multi-armed bandit problem with each arm representing a candidate price. Specifically, the privacy protection technique adopted by the collector is taken into account. Protecting privacy generally causes a negative effect on the value of data, and this effect is embodied by the time-variant distributions of the rewards associated with arms. Based on the classic upper confidence bound policy, we propose two learning policies for the bandit problem. The first policy estimates the expected reward of a price by counting how many times the price has been accepted by data owners. The second policy treats the time-variant data value as a context and uses ridge regression to estimate the rewards in different contexts. Simulation results on real-world data demonstrate that by applying the proposed policies, the collector can get a payoff which is close to that he can get by setting a fixed price, which is the best in hindsight, for all data owners.

With the rapid growth of the Internet, the current TCP/IP based network cannot well satisfy the requirements such as scalable content distribution, mobility, security and so on. The new networking architectures which aren't based on TCP/IP have been a trade of next generation networking such as Information-Centric Networking (ICN). In smart grid, parts of communication protocol in IEC 61850 are also not based on TCP/IP architecture such as Sampled Value (SV) and Generic Object-Oriented Substation Event (GOOSE). IEC 61850 based smart substation employing wireless network has significantly improved the interoperability and interconnection of substation devices. However, with the amount and openness growth of Intelligent Electronic Devices (IED) nodes, these changes introduce new efficiency, reliability and security challenges especially in wireless network. The strict time requirement has limited the use of heavyweight security protocols to fight against cyber- attacks. To address these issues, a name-based security mechanism using ICN is proposed for the non TCP/IP based SV and GOOSE communication. Publish/ subscribe (pub/sub) based access control and lightweight encryption algorithm are utilized to secure the decentralized large-scale smart grid data sharing. The results show the proposed mechanism is secure and efficient for IEC 61850 communication employing wireless network.

Software defined networks (SDN) is perceived to have specific capabilities for utilization by network infrastructures automatically. The success of OpenFlow protocol is to decouple control plane from data plane completely. However, current SDN still regards the network as a group of devices rather than a holistic resource, and traffic monitoring and control only relies on network states but not including traffic behaviours. Although speed of packet forwarding is improved significantly, QoS demands can not be satisfied when network congested, unavailability of SDN in some resource constrained scenes does not present well. To address this, we propose an application-aware traffic control scheme, in which both network states and traffic behaviours are exploited cooperatively. Deep Packet Inspection (DPI) is introduced into SDN controller. Meanwhile, a mechanism for packet classification and behaviour matching is designed. To perform information exchange between components, a publish/subscribe based middle ware is designed. Besides, mathematical models for analysing network throughput and latency are established. Simulation results show that proposed scheme can facilitate the improvement of throughput and reduce latency time of end-to-end communication.

The space information network (SIN) has attracted more and more attention due to its extensive applicability and great expanding access services. The complicated properties of SIN, such as the dynamic and unstable topology, the highly exposed links and so on, make it necessary to design an appropriate key management scheme to ensure the security of communication. In this paper we propose an anonymous and distributed certificate-less key management scheme (aCL-KMS) for SIN. It mainly adopts the strategy of the distributed key generation, update and agreement instead of the complex centralized key management. Based on the certificate-less public key cryptosystem (CL-PKC), this scheme not only avoids the high cost of complicated certificate management, but also overcome the key-escrow problem of the certificate-based or identity-based public key cryptosystem. Also, due to the fact that the anonymous authentication mechanism adopts the temporary identification of members, this scheme can efficiently protect the members' privacy and ensure the confidentiality of communications. The security properties discussion and the computational overhead analysis show that the proposed key management system is secure enough to meet the security requirements of SIN, and it is of less computing cost at the same time.

Software-defined Home Networks (SDHN) is a key development trend of smart home. Security is still an important issue in SDHN. In this paper, a multi-stage attack mitigation mechanism is proposed for SDHN using Software-Defined Networking (SDN) and Network Function Virtualization (NFV). Firstly, an evidence-driven security assessment method using SDN factors and NFV- based detection is designed to perform security assessment along with observed security events. Secondly, an attack mitigation countermeasure selection method is proposed. The evaluation shows that the proposed mechanism is effective for multi-stage attack mitigation in SDHN.

Industrial Wireless sensor networks (IWSNs) are emerging as a promising technique for industrial applications. With limited energy resources, prolonging the lifetime of IWSNs is a fundamental problem for industrial applications. At the same time, Software- Defined Networking (SDN) and Network Function Virtualization (NFV) are future network techniques which make the underlying networks and node functions programmable. SDN and NFV have inherent advantages to control topology and node mode in IWSNs. In this paper, we propose a mechanism improving energy efficiency in industrial wireless sensor networks using SDN and NFV named M-SEECH for industrial application. Firstly, we propose a new architecture based on traditional IWSNs and operation mechanism using SDN and NFV. In the architecture, the global view and central control properties of SDN are utilized to monitor IWSNs. Also, the programmability of SDN and instant deployment capability of NFV are utilized to control the topology of IWSNs and the modes of nodes in IWSNs. Thirdly, we propose advanced algorithms for controller in IWSNs taking the advantages of SDN and NFV. By this way, the average lifetimes of IWSNs are prolonged. Finally, the case study and evaluation show the advantages of the proposed energy efficient scheme comparing with traditional methods.

Social Peer-to-Peer (P2P) is a novel model to organize sensor networks, which can establish social relationships in an autonomous way with the benefits of extending the network boundaries and enhancing the network scalability. However, the complexity and time dependence characteristics introduced by social P2P model raise difficulties for assessing and selecting security services accurately and effectively in sensor networks. To address this, we propose a chance discovery based security service selection scheme for social P2P based sensor networks. We firstly establish the security assessment model for services in social P2P based sensor networks, regarding the security factors of exploitability, credibility, severity, confidentiality, integrality, availability and importance weight. More importantly, time dependence characteristics introduced by social P2P are considered during security assessment. Next, a security service selection scheme is proposed based on KeyGraph construction as well as the computation of its connection and tightness values. Finally, the service request forwarding model is established. The simulation results show the effectiveness and accuracy of the proposed security service selection scheme, which improves the feasibility and security of integrated sensor network and social networks.

As one of core technologies of smart grid, demand response (DR) helps improving electricity efficiency and optimizing power consumption way. In addition to having advanced technologies and applications of communication and computing infrastructures, the security of DR is still an issue deserving research to defend against evolving cyber attacks. In this paper, a security mechanism for DR is proposed to solve the security problem using Role Based Access Control (RBAC)and Publish/subscribe (Pub/sub). Different from traditional security schemes in DR, not only real existed entities such as the electricity suppliers and users but also logical subjects such as DR event are taken into consideration. It realizes the logical separation of users and permissions and grants role with secure analysis based on Support Vector Machines (SVM). A Pub/sub based communication scheme acquires greater network scalability and a more dynamic network topology with the help of group key distribution algorithm.

In this paper, a 2-D noncausal Markov model is proposed for passive digital image-splicing detection. Different from the traditional Markov model, the proposed approach models an image as a 2-D noncausal signal and captures the underlying dependencies between the current node and its neighbors. The model parameters are treated as the discriminative features to differentiate the spliced images from the natural ones. We apply the model in the block discrete cosine transformation domain and the discrete Meyer wavelet transform domain, and the cross-domain features are treated as the final discriminative features for classification. The support vector machine which is the most popular classifier used in the image-splicing detection is exploited in our paper for classification. To evaluate the performance of the proposed method, all the experiments are conducted on public image-splicing detection evaluation data sets, and the experimental results have shown that the proposed approach outperforms some state-of-the-art methods.

In a periodic K-times anonymous authentication system, user can anonymously show credential at most K times in one time period. In the next time period, user can automatically get another K-times authentication permission. If a user tries to show credential beyond K times in one time period, anyone can identify the dishonest user (the violator). But identifying violators is not enough for some systems, where it is also desirable to revoke violators' credentials for preventing them from abusing the anonymous property again. However, the problem of revoking credential without trusted third party has not been solved efficiently and practically. To solve it, we present an efficient scheme with efficient revocation of violator's credential. In fact, our method also solves an interesting problem-leaking information in a statistic zero-knowledge way, so our solution to the revocation problem outperforms all prior solutions. For achieving it, we use the special zero-knowledge proof with special information leak for revoking the violator's credential, but it can still be proven to be perfect statistic zero knowledge for guaranteeing the honest user's anonymity. Comparing with existing schemes, our scheme is efficient, and moreover, our method of revoking violator's credential is more practical with the least additional costs.

Many algorithms have been designed to detect community structure in social networks. However, most algorithms can only detect disjoint communities effectively. A new overlapping community structure detecting algorithm is proposed in this paper, which adopts modularity to community clustering. In order to evaluate the algorithm, Modularity by Newman and the NMI (Normalized Mutual Information) by Lancichinetti are used as the evaluation metrics. It is approved by the experiments that the proposed method works well to the real overlapping communities.

Detection of community structure in complex networks is a significant aspect in social network analysis. A novel fuzzy clustering method is proposed in this paper, by which the community structure can be divided. In contrast to previous studies, the proposed method processes similarity of connecting vertices with fuzzy relation. In our method, we globally consider the fuzzy relation between vertices and the similarity in network topology to divide vertices into communities. In addition, smaller grained communities can be detected by adjusting fuzzy parameter. In order to avoid subjectivity in the selection of cluster number, a new modularity is introduced to evaluate the effectiveness of the clustering analysis. It's proved by experiments that the method is efficient in detecting both good communities and appropriate number of clusters.

Advanced Metering Infrastructure (AMI) is the core component in a smart grid that exhibits a highly complex network configuration. AMI shares information about consumption, outages, and electricity rates reliably and efficiently by bidirectional communication between smart meters and utilities. However, the numerous smart meters being connected through mesh networks open new opportunities for attackers to interfere with communications and compromise utilities assets or steal customers private information. In this paper, we present a new DoS attack, called puppet attack, which can result in denial of service in AMI network. The intruder can select any normal node as a puppet node and send attack packets to this puppet node. When the puppet node receives these attack packets, this node will be controlled by the attacker and flood more packets so as to exhaust the network communication bandwidth and node energy. Simulation results show that puppet attack is a serious and packet deliver rate goes down to 20%-10%.

With the development of image tampering detection, more and more features are involved to improve the detection rate, and nowadays the high-dimensional features based methods get the state-of-the-art detection accuracies. However, the high dimensionality will cause excessive time cost in the classification phase, moreover it would probably introduce redundant features which will confuse the classifier. An effective scheme based on Distributed Local Margin Learning (D-LML) is proposed in this paper to solve the problems caused by the high-dimensionality of features in the image tampering detection work. Local Margin Learning algorithm distributed to different clients is employed to rank the importance of the original features, and we can get features with low dimensionality by preserving the important features while excluding the insignificant features. Experimental results show that the D-LML method could greatly reduce the dimensionality of the original features, and keep the detection rates fluctuating in a relatively small range.

In this paper, a noncausal Markov model is proposed for digital image splicing detection. Different from the traditional Markov model in image splicing detection, the proposed approach models an observation array as a 2-D noncausal signal and captures the underlying statistical characteristics. We give the solutions to the model and the model parameters are treated as discriminative features for classification (detection). To evaluate the generalization and effectiveness of the proposed method, we apply the model in the block DCT domain and discrete Meyer wavelet transform domain respectively and experimental results have shown that the proposed approach outperforms most of the state-of-the-art methods.

Wireless sensor networks (WSNs) are vulnerable to security attacks due to the broadcast nature of transmission and limited computation capability. After intrusion detection systems (IDSs) identifies an mobile intruder, IDS may broadcast the blacklist to all nodes in network. This method is energy inefficient because all nodes have to receive and forward the alarm packet so as to exhaust communication bandwidth and node energy, especially when there are a large number of sensor nodes in the network. This paper develops an energy efficient intrusion prevention mechanism in WSNs called green firewall. It can isolate an intruder with less overhead, and track the intruder to continually prevent the attack. The paper analyzes the overhead cost of the green firewall and compare it with the flooding broadcast method. Extensive analysis and simulations show that green firewall can prevent the attack and effectively reduce redundant alarm packet transmissions which results in less energy consumption.

The pixel-based contour map is one of the most common used shape representation methods for shape matching in object recognition field. However it is difficult to remain accurate and efficient at the same time when recognizing the objects with diversity of postures or different presence from different perspectives. To solve this problem, in this paper we propose an angle-based shape matching approach by introducing a new concept of angle-based features. Furthermore, the object recognition process adopting such angle-based shape matching approach is described in detail. With numerous experiments conducted on the Weizmann Horse dataset, we demonstrate that the proposed method is accurate, efficient and robust towards different poses and resolutions at the same time.

Privacy issues are concerned when data holders share their detected security data for correlation and analysis purpose. This paper proposes an approach to correlate and analyze intrusion alerts, while preserve privacy for alert holders. The raw intrusion alerts are protected by improved k-anonymity model, which preserves the alert regulation inside disturbed data records. With this privacy preserving technique, combing the typical FP-tree association rules mining algorithm, the approach provides the capacity of well balancing the alert correlation and the privacy preservation. Experimental results show that this approach works comparatively efficient and reaches a well balance between the alerts correlation and the privacy issues.

In this paper, we propose a new group key management scheme for wireless sensor networks in terms of the unreliable wireless channel and unsafe environment. Our proposed scheme implements node revocation through a broadcast polynomial to counteract the node compromise attack and inherits the idea of loss tolerance in LiSP to provide a reliable communication. The analysis shows that the proposed scheme can efficiently revoke the compromised sensor nodes, implicitly authenticate the updated group keys and tolerate the key-update message loss under the unreliable wireless communication channel.

Yang and Shieh proposed two password authentication schemes based on smart cards. The best merit of their schemes is that the remote server can verify a login user without any prior knowledge except a login request message. Unfortunately, some security weaknesses had been found and kinds of attacks were presented later. Although some improvements were proposed to fix those weaknesses, part of these improvements need the remote server to maintain verification tables and the other improvements were proved insecure either. In this paper, we will propose two improved schemes that can withstand all existed attacks while keeping the best merit of the original schemes. The remote server in our improved schemes is still able to verify a login user only by a request message.