Person re-identification (ReID) is a fundamental task in many real-world applications such as pedestrian trajectory tracking. However, advanced deep learning-based ReID models are highly susceptible to adversarial attacks, where imperceptible perturbations to pedestrian images can cause entirely incorrect predictions, posing significant security threats. Although numerous adversarial defense strategies have been proposed for classification tasks, their extension to metric learning tasks such as person ReID remains relatively unexplored. Moreover, the several existing defenses for person ReID fail to address the inherent unique challenges of adversarially robust ReID. In this paper, we systematically identify the challenges of adversarial defense in person ReID into two key issues: model bias and composite generalization requirements. To address them, we propose a debiased dual-invariant defense framework composed of two main phases. In the data balancing phase, we mitigate model bias using a diffusion-model-based data resampling strategy that promotes fairness and diversity in training data. In the bi-adversarial self-meta defense phase, we introduce a novel metric adversarial training approach incorporating farthest negative extension softening to overcome the robustness degradation caused by the absence of classifier. Additionally, we introduce an adversarially-enhanced self-meta mechanism to achieve dual-generalization for both unseen identities and unseen attack types. Experiments demonstrate that our method significantly outperforms existing state-of-the-art defenses.

In backup storage, delta compression successfully achieves a much higher data reduction ratio than chunk-level deduplication by applying a finer granularity in redundancy detection and elimination. However, it introduces additional, intensive computation overhead and results in a 50%–70% worsening backup throughput.

A primary challenge in network defense is to mine potential attack campaigns from massive, continuously arriving alerts and telemetry data in real time. This paper presents HoSig-Align I, an edge side unsupervised method designed for network streams to discover homologous blocks. Our approach innovatively fuses heterogeneous features like Internet Protocol (IP) and Payload into a unified representation while strictly excluding temporal information from it, only incorporating time via a dual time scale decay model during graph construction to capture temporal proximity. A density robust similarity is computed using an isolation style random partition forest, leading to a sparse k-Nearest Neighbors (k-NN) graph. The stream is then accurately segmented into internally cohesive and mutually isolated homologous blocks through spectral ordering and contrastive change point detection. Each block is encoded into a lightweight HoSig signature, forming the basis for cross organizational collaboration. Experiments on real network streams show that HoSig-Align I identifies coherent attack campaign blocks and improves separation and boundary clarity over baselines, while meeting low-latency and low-overhead requirements for edge processing.

Extracting cohesive subgraphs from complex networks is a fundamental task in graph analytics and is essential for understanding biological, social, and web graphs. The edge-based γ-quasi-clique model offers a flexible alternative by identifying subgraphs whose edge densities exceed a specified threshold γ. However, finding the exact maximum edge-based quasi-clique is computationally challenging, as the problem is NP-hard and lacks the hereditary property. These characteristics limit the effectiveness of conventional pruning methods and the development of efficient reduction rules. As a result, existing algorithms, such as QClique and FPCE, struggle to scale to large graphs. In this paper, we revisit the problem and propose a novel iterative framework that reformulates the problem as a sequence of hereditary subproblems, enabling more effective pruning and reduction strategies and improving the worst-case time complexity. Furthermore, we redesign the iterative process and introduce a novel heuristic to further improve practical efficiency. Extensive experiments on 253 large-scale real-world graphs demonstrate that our proposed algorithm EQC-Pro outperforms existing methods by up to four orders of magnitude.

Encrypted traffic classification (ETC) serves as a pivotal research for network measurement and Quality of Service (Qos) management. However, most current approaches rely on statically frozen parameters after training, which remain vulnerable to performance degradation under real-world distribution shifts caused by dynamic network conditions, protocol evolution, and adversarial obfuscation techniques. To tackle this issue, we propose NetTTT, an online self-adaptation framework that, for the first time, performs continuous representation refinement directly at inference time without retraining. NetTTT leverages a hierarchical feature encoder that constructs structured dual-modal representations from raw traffic sessions, coupled with a pre-trained generic representation extractor to powerfully capture transferable contextual features, and incorporates a lightweight test-time adapter, which performs unsupervised, layer-wise parameter optimization during inference to dynamically align traffic representations with test-time traffic distributions, followed by a compact classifier that efficiently maps adapted representations to traffic categories. Experimental results reveal the superiority of NetTTT across diverse ETC tasks and few-shot scenarios with accuracy improvements ranging from 0.08% to 9.34% over state-of-the-art baselines. By enabling dynamic adaptation without labeled data or full model retraining, NetTTT bridges the gap between static pre-trained models and evolving encrypted traffic, highlighting its potential to provide a practical solution for real-world scenarios.

Provenance graphs have emerged as a promising approach for detecting host-based attacks, particularly on Internet of Things (IoT) endpoints where audit logs are available but resources are constrained. Such graphs integrate fine-grained system audit logs and formulate the attack detection process as subgraph matching within the provenance graph. Existing methods relying on exact subgraph matching are computationally intensive and time-consuming, while supervised learning for approximate subgraph matching, although a potential alternative, requires extensive labeled data that is prohibitively costly for annotating provenance graphs at scale. To tackle these problems, we propose ContrastMatcher, a self-supervised adaptive graph contrastive learning for provenance subgraph matching which needs no label for training. ContrastMatcher consists of three modules: reduction, adaptive self-supervised learning, and lightweight detection. By graph reduction and process-centric ego partitioning, the reduction module identifies likely regions where attacks may occur, substantially reducing computational complexity and runtime. The adaptive self-supervised learning module selects effective augmentations to learn the subgraph representations with a carefully designed self-attention encoder, enabling ContrastMatcher to work without any label. Finally, the lightweight detection module classifies the attack relations between each query and the candidates from the learned representations automatically, which avoids setting the approximation manually. Experiments on the DARPA Engagement 3 datasets show that ContrastMatcher can reduce irrelevant host-based log events by 80% − 90%. In addition, without any label in the provenance graph, it achieves a competitive performance regarding the detection accuracy with only 1% training time compared to the existing supervised learning methods.

Industrial Cyber-Physical Systems (ICPS) are increasingly vulnerable to sophisticated attacks and operational disturbances that induce subtle and hard-to-detect anomalies, particularly in industrial edge environments. Existing anomaly detection methods often rely on sufficient labeled data and involve excessive computational overhead, hindering real-time detection and lightweight deployment. To address these challenges, we propose a Multimodal Feature fusion-enhanced time series Anomaly Detection framework (MFAD) in ICPS. MFAD enhances the representation of subtle anomalies by jointly modeling temporal dynamics and industrial characteristics through a unified multimodal feature fusion mechanism. Moreover, MFAD adopts a three-stage detection strategy with adaptive thresholding, which further improves robustness under varying operating conditions, while its lightweight overall architecture supports edge deployment. In addition, we provide the Industrial Gas Cyber-Physical System (IGCPS) dataset collected from real-world industrial operations. Experiments on ICPS benchmark datasets of varying scales, including IGCPS, PUMP, WADI, and SWaT, demonstrate that MFAD achieves an F1 score exceeding 96.7% with efficient resource utilization, validating its effectiveness for real-time detection and lightweight deployment in resource-constrained industrial edge environments. Note to Practitioners—This paper is motivated by the increasing need for reliable and efficient anomaly detection in Industrial Cyber-Physical Systems (ICPS), particularly deployed in resource-constrained industrial edge environments. Existing approaches often treat temporal and industrial features separately, rely on sufficient labeled data, and require substantial computational resources, which limits their applicability in real-world industrial settings. In contrast, the proposed MFAD provides a lightweight and practical solution that integrates multimodal feature fusion with robust semi-supervised detection mechanisms to effectively capture subtle anomalies in time series industrial data. The framework is designed with deployment feasibility that it offers strong detection accuracy, low latency, and efficient resource consumption suitable for industrial edge devices. The methods presented here can inform practitioners seeking to enhance the reliability and real-time performance of ICPS anomaly detection systems. Future extensions may focus on expanding MFAD for broader online industrial applications, integrating it with more edge platforms, and enabling large-scale distributed deployment.

Ensuring data confidentiality while achieving efficient deduplication is critical for cloud storage services. Existing works on encrypted data deduplication operate under a deduplication-before-storage (DbS) model, identifying duplicates only during the data uploading phase. They require the owners of duplicate data or additional key servers to assist in the data uploading phase for enabling encrypted deduplication. This makes it hard to balance data security and deduplication effectiveness. This paper introduces a new paradigm named deduplication-while-storage (DwS), which differs from the existing DbS model by enabling dynamic deduplication throughout the entire storage lifecycle, activated whenever owners of potential duplicate data are available. This paradigm aims to significantly boost deduplication ratio without compromising the security of outsourced data. To instantiate the DwS paradigm, we develop SGX-DwS, a shielded storage system that utilizes Intel SGX to support encrypted data deduplication. SGX-DwS builds on an ownership-based encryption mechanism that enables secure equality checks on ciphertexts with the cooperation of owners of duplicate data. It adopts a two-phase deduplication strategy. The first phase processes duplicates during uploading if a previous uploader of duplicate data is online and willing to assist (with incentives provided for participation), while the second phase addresses remaining duplicates during the storage phase when owners of the potential duplicates are simultaneously online and cooperative. Our experimental results demonstrate that SGX-DwS achieves high deduplication ratio with modest overhead, even in large-scale cloud storage environments. Specifically, the improvement in deduplication ratio achieved by SGX-DwS is up to 92.49% as compared to that in the DbS model.

Current adversarial attacks pose a serious threat to the robustness of visual-language models (VLMs), including vision-language pre-trained models (VLPMs) and multimodal large language models (MLLMs). Traditional adversarial attacks are example-specific and rely on specific datasets. This practice suffers from low transferability and additional computation cost, while universal adversarial perturbations (UAPs) offer example-agnostic solutions by generalizing across inputs. However, current UAP methods mainly target VLPMs, demonstrating limited transferability and effectiveness in MLLMs. To bridge this gap, we propose the Recursive Perturbation Attack (RPA), a novel black-box UAP method for both VLPMs and MLLMs. RPA employs a recursive perturbations strategy, utilizing token filtering and polynomial sampling methods to generate perturbations, thereby achieving incremental disruption and enhancing the transferability of the attack. To further enhance the effectiveness of the attack, RPA integrates a three-tier modality decoupling strategy, disentangling intra-modal, cross-modal, and fusion-modal features to effectively disrupt feature alignment and interactions. Extensive experiments validate that RPA achieves superior attack performance compared to existing UAP approaches. This work highlights new security concerns in multimodal AI systems and provides insights into the design of more robust models. Code is available at https://github.com/chilljudaoren/RPAttack.

The growing popularity of blockchain technology has underscored the need for robust network security. However, public blockchain networks remain vulnerable to attacks in which adversaries exploit numerous nonfunctional peer connections to disrupt block propagation across the entire network. In this article, we propose a practical nontargeted delay attack method and validate its feasibility, scalability, and significant impacts on blockchain networks of varying sizes, including EthereumPoW (ETHW) and premerge Ethereum Mainnet. In the ETHW network with 95 nodes, our adversarial peers introduce delays ranging from 0.33 to 2.8 s for half the nodes, with nearly one-third experiencing delays exceeding 5.9 s, derived from the 90th percentile of delay times. When in the premerge Ethereum network with 5739 nodes, over 80% of peers experience prolonged block propagation, resulting in a 77% increase in delay time, underlining the attacks' scalability and efficacy in large-scale environments. We also optimize the Ethereum client Geth by relaxing certain connection restriction, significantly reducing attack costs. Delving deeper, we analyze the implications of delay attacks on proof-of-work (PoW) and proof-of-stake (PoS) consensus mechanisms, illustrating how attackers can gain extra revenues through such attacks. Specifically, we propose a novel combined strategy to facilitate reorganization attacks under PoS. These findings highlight the urgent need to strengthen network-layer defenses and reinforce peer-to-peer (P2P) network protocol security against real-world delay exploits.

With the rapid development of Large Language Models (LLMs), an increasing number of Large Visual-Language Models (LVLMs) have achieved unprecedented performance in response generation. Recent work shows that LVLMs are vulnerable to adversarial attacks. However, many existing methods tend to overfit to the source model by overemphasizing specific features, which compromises their transferability. Other approaches suffer from reduced attack effectiveness due to insufficient differentiation between features. In this paper, we propose a novel transfer-based black-box untargeted attack—Shared Adversarial Feature (SAF) dynamic attack. By exploring the feature extraction patterns of LVLMs, we identify the features shared among various models that are most susceptible to adversarial attacks and disrupt them. Moreover, due to the powerful attention mechanisms of LVLMs, they are still able to extract similar semantics from perturbed images, even when primary features are disrupted. We design a dynamic update strategy to address this challenge. Finally, from the perspective of SAF, we conduct an in-depth analysis of vulnerabilities in the vision encoder and projector within LVLMs and find that attacking the projector exhibits stronger transferability across heterogeneous model architectures. Extensive experiments show that our method exhibits superior attack performance compared to existing methods across different models, datasets, and tasks. The code will be publicly available after publication.

Deep-neural-network-based autonomous driving perception systems in the Industrial Internet of Things remain vulnerable to adversarial attacks despite their critical role in Industry 4.0. While numerous studies have investigated adversarial attacks on individual perception tasks such as monocular depth estimation or object detection in static situations, real-world complex industrial scenarios introduce two understudied challenges: 1) dynamic scenarios with moving objects and changing viewpoints and 2) simultaneous attacks across multiple perception tasks. In this article, we make three key contributions to address these challenges. First, we introduce a systematic study of environment-aware adversarial attacks in dynamic scenarios by introducing three adversarial attack tasks: object away attack, object close attack, and object creation attack. Second, we propose a novel dynamic scene-oriented adversarial road patch generation framework that accounts for real-world environmental variations. Third, we develop a comprehensive technical framework featuring: improved adversarial loss functions, a dynamic optimization architecture, and an integrated approach combining expectation over transformation with advanced physical augmentation optimization. Extensive experimental results demonstrate the robustness of our proposed method in digital, simulation, and real-world physical domains, as well as under different weather conditions.

Vision-Language Pretrained (VLP) models exhibit strong multimodal understanding and reasoning capabilities, finding wide application in tasks such as image-text retrieval and visual grounding. However, they remain highly vulnerable to adversarial attacks, posing serious reliability concerns in safety-critical scenarios. We observe that existing adversarial examples optimization methods typically rely on individual features from the other modality as guidance, causing the crafted adversarial examples to overfit that modality's learning preferences and thus limiting their transferability. In order to further enhance the transferability of adversarial examples, we propose a novel adversarial attack framework, I&CA (Individual & Common feature Attack), which simultaneously considers individual features within each modality and common features cross-modal interactions. Concretely, I&CA first drives divergence among individual features within each modality to disrupt single-modality learning, and then suppresses the expression of common features during cross-modal interactions, thereby undermining the robustness of the fusion mechanism. In addition, to prevent adversarial perturbations from overfitting to the learning bias of the other modality, which may distort the representation of common features, we simultaneously introduce augmentation strategies to both modalities. Across various experimental settings and widely recognized multimodal benchmarks, the I&CA framework achieves an average transferability improvement of 6.15% over the state-of-the-art DRA method, delivering significant performance gains in both cross-model and cross-task attack scenarios.

Encrypted traffic classification has become a crucial task for network management and security with the widespread adoption of encrypted protocols across the Internet and the Internet of Things. However, existing methods often rely on discrete representations and complex models, which leads to incomplete feature extraction, limited fine-grained classification accuracy, and high computational costs. To this end, we propose TrafficAudio, a novel encrypted traffic classification method based on audio representation. TrafficAudio comprises three modules: audio representation generation (ARG), audio feature extraction (AFE), and spatiotemporal traffic classification (STC). Specifically, the ARG module first represents raw network traffic as audio to preserve temporal continuity of traffic. Then, the audio is processed by the AFE module to compute low-dimensional Mel-frequency cepstral coefficients (MFCC), encoding both temporal and spectral characteristics. Finally, spatiotemporal features are extracted from MFCC through a parallel architecture of one-dimensional convolutional neural network and bidirectional gated recurrent unit layers, enabling fine-grained traffic classification. Experiments on five public datasets across six classification tasks demonstrate that TrafficAudio consistently outperforms ten state-of-the-art baselines, achieving accuracies of 99.74%, 98.40%, 99.76%, 99.25%, 99.77%, and 99.74%. Furthermore, TrafficAudio significantly reduces computational complexity, achieving reductions of 86.88% in floating-point operations and 43.15% of model parameters over the best-performing baseline.

Reversible data hiding over encrypted images (RDH-EI) is an important technique for secure cloud image management but existing schemes often exhibit high computational complexity, low embedding rates, and excessive data expansion. This article addresses these issues by analyzing block-based secret sharing, revealing significant intra-block data redundancy. Based on this observation, we propose two space-preserving methods: the direct space-vacating method and the image-shrinking-based space-vacating method. Using these techniques, we design two novel RDH-EI schemes: a high-capacity RDH-EI scheme and a size-reduced RDH-EI scheme. The high-capacity RDH-EI scheme directly creates embedding space in encrypted images, eliminating the need for complex space-vacating operations and achieving higher and more stable embedding rates. In contrast, the size-reduced RDH-EI scheme minimizes data expansion by discarding unnecessary shares, resulting in smaller encrypted images. Experimental results show that the high-capacity RDH-EI scheme outperforms existing methods in terms of embedding capacity, while the size-reduced RDH-EI scheme achieves strong performance in minimizing data expansion. Both schemes offer effective solutions for RDH-EI challenges.

The rapid growth of container images leads to heavy storage pressures on image registries. Fine-grained deduplication, such as at the file and chunk levels, is a promising technique for reducing the storage space in image registries, compared to Docker's native coarse-grained image layer deduplication. However, fine-grained deduplication often incurs significant image restoration latency due to fragmented I/Os, resulting in up to 8× restoration I/O slowdowns. Moreover, existing restore-optimized deduplication techniques are not tailored to the characteristics of the container image, resulting in low deduplication ratios of images. Consequently, restoring performance remains a critical barrier to the practical adoption of fine-grained deduplication in container image registries. To address this challenge, we propose MiDedup, a restore-friendly fine-grained deduplication approach tailored for container image registries. MiDedup is built on an underexplored observation: the redundancy across layers of images follows a non-uniform distribution. Based on this insight, MiDedup introduces three core techniques: ① Across-layer-aware reorganization, which reorganizes deduplicated data into a compact, sequential layout, significantly reduces fragmented I/Os during image restore. ② Popularity-aware rewriting, which selectively rewrites hot image layers to further improve the balance between deduplication ratio and restore performance. ③ Hybrid-granularity deduplication, which combines file-level and chunk-level deduplication to reduce metadata and fragmented data. Experiments on the real-world Docker image dataset show that MiDedup reduces the 50–80% restore I/O overhead of fine-grained deduplication, which is comparable to the restore performance of native layer-level deduplication, while achieving 1.2–7.3× higher deduplication ratios.

Graph Retrieval Augmented Generation (GRAG) is a novel paradigm that takes the naive RAG system a step further by integrating graph information, such as knowledge graph (KGs), into large-scale language models (LLMs) to mitigate hallucination. However, existing GRAG still encounter limitations: 1) simple paradigms usually fail with the complex problems due to the narrow and shallow correlations capture from KGs 2) methods of strong coupling with KGs tend to be high computation cost and time consuming if the graph is dense. In this paper, we propose the Fast Think-on-Graph (FastToG), an innovative paradigm for enabling LLMs to think ``community by community" within KGs. To do this, FastToG employs community detection for deeper correlation capture and two stages community pruning - coarse and fine pruning for faster retrieval. Furthermore, we also develop two Community-to-Text methods to convert the graph structure of communities into textual form for better understanding by LLMs. Experimental results demonstrate the effectiveness of FastToG, showcasing higher accuracy, faster reasoning, and better explainability compared to the previous works.

The rapid growth of Docker images in cloud infrastructures has intensified storage and network demands, posing challenges to QoS for registries and deployments. This paper systematically evaluates four data reduction methods—file-level and block-level deduplication, delta compression, and local compression—on 18 representative images. We quantify their tradeoffs in compression, computation, I/O overhead, and restore performance, revealing that (1) filtering large files ($>64 \text{KB}$) preserves 80% of redundancy elimination at half the cost, (2) category-based reorganization reduces restore latency by 83%, and (3) fixed-size chunking with optimized blocks balances memory and compression. Based on these insights, we propose adaptive strategies for efficient container storage.

Abstract In cyberspace, intrusion and detection constitute dynamic and continuous game processes, where data streams are generated incrementally during intrusion. To mitigate intrusions and safeguard assets effectively, it is imperative to take prompt actions based on real-time detection and analysis of the currently available data streams. However, existing approaches that rely on complete and clean data struggle to keep pace with the continuous real-time flow of new network data. To address this issue, we introduce IL-IDS (Incremental Learning for Intrusion Detection Systems), a novel intrusion detection approach that utilizes incremental learning to enable accurate and timely detection of intrusions in real-world scenarios, where the need for real-time processing and learning from newly generated traffic data is paramount. IL-IDS performs in scenarios with limited data availability, where it initially transforms textual data streams into vectorized representations and leverages a variation autoencoder (VAE) to compress these vectors, efficiently extracting their latent features. Then a classifier is trained to distinguish attack and normal behaviors, and a three-way decision method is employed to establish a boundary for ambiguous data that pose challenges in direct classification. Concurrently, threat intelligence is integrated into this process to enhance the accuracy of decision-making. We validate the effectiveness and efficiency of IL-IDS with experiments on real-world deployments during an international activity, highlighting its robustness and reliability in intrusion detection applications, especially under conditions of confined data streams. Notably, IL-IDS has exhibited comparable accuracy and recall results, and attains exceptional 99.93% precision and 96.83% F1-score, which demonstrates a notable improvement of 5.27% and 2.59% respectively in comparison to intrusion detection models trained on complete and readily available data.

Deep Neural Networks (DNNs) are notoriously susceptible to adversarial examples. To mitigate the impact of well-designed adversarial attacks on network models, researchers have developed various defense mechanisms, among which adversarial training has emerged as one of the most effective strategies to date. Adversarial training aims to augment training data with adversarial examples, thus giving DNNs a certain degree of robustness to defend against adversarial attacks. However, while obtaining adversarial robustness, this method comes at the cost of reducing the generalization performance, manifested in the reduced classification effect of clean test datasets. Researchers have been actively seeking to counter the balance between adversarial robustness and model generalization. We believe that the key to balancing these two aspects lies in identifying appropriate adversarial examples. Overly potent examples can lead to a decline in clean accuracy, whereas weaker examples may offer limited robustness. Based on our analysis, a new adversarial example generation algorithm called Denoising Projection Gradient Descent (DPGD) was proposed. DPGD adds a purification module and a constraint in generating adversarial examples, the former is used to limit the influence of too strong adversarial examples on model training and the latter is used to ensure the necessary attack intensity. Combining DPGD with the framework of traditional adversarial training, we obtain the Diffusion Adversarial Training (DifAT) approach. To verify the effectiveness of our proposed method, we conducted extensive experiments on benchmark datasets, including CIFAR-10, CIFAR-100, and Tiny-Imagenet. Our results demonstrate the effectiveness of DifAT in improving the robustness of DNNs while maintaining or even improving their generalization performance.

The widespread deployment of Internet of Things (IoT) devices has made them vulnerable targets for cyber attacks, highlighting the great significance of IoT device identification for network security management. Existing studies have primarily focused on either manual extraction of excessive network traffic features or heavy reliance on labeled data. To address these limitations, we propose a novel IoT device identification approach (named IoT-SCNet) via semi-supervised contrastive learning of network traffic visual representations. Specifically, IoT-SCNet converts two packet-level features and raw traffic into network traffic images of each device, and applies three augmentation strategies designed for network traffic to construct semantically meaningful positive/negative image pairs. By deep neural networks automatically capturing discriminative patterns and a contrastive task, IoT-SCNet achieves effective identification of diverse IoT device types. Comprehensive evaluations across three benchmark datasets demonstrate the superior performance of IoT-SCNet, achieving remarkable identification accuracies of 99.83% on UNSW, 97.50% on Yourthings, and 99.34% on CIC IoT datasets.

As Internet of Things (IoT) technologies continue to evolve and gain widespread integration across various sectors, IoT devices have become increasingly interconnected and ubiquitous. However, this surge has also led to more sophisticated, diverse, and covert attacks, posing severe security challenges to IoT ecosystems and their defense mechanisms. Intrusion Detection Systems (IDS) are instrumental in securing the IoT by uncovering and mitigating malicious behaviors in real-time. Nevertheless, the growing complexity of network traffic presents significant challenges for IDS, often resulting in a higher false alarm rate and a lower detection rate, due to the intricate characteristics of space and notable temporal dependencies present in network traffic. We conduct an in-depth dimensionality reduction analysis using the t-distributed Stochastic Neighbor Embedding methodology to examine the relationships among attack examples. Then, to enhance the generalization and robustness of IDS, we propose GBFKAN, a novel interpretable three-layer network architecture. The structured design of GBFKAN can comprehensively extract and perceive key information from multiple abstract levels in the raw traffic, enhancing the architecture’s generalization ability and robustness. Furthermore, we utilize the SHapley Additive ExPlanations method to provide detailed explanations of the detection outcomes, augmenting its credibility. Building on this, we successfully identify the erroneous decision-making paradigms within GBFKAN by incorporating empirical knowledge. Extensive simulation experiments conducted on the four widely recognized intrusion detection datasets have conclusively demonstrated that our proposed GBFKAN model surpasses existing methods in terms of intrusion detection performance.

Community search has widespread applications in areas such as advertising, friend recommendation, and protein complex identification. In this paper, we revisit the Size-bounded Community Search (SCS) problem, which aims to identify a connected subgraph containing a query vertex q and between l and h vertices, while maximizing the minimum degree of the subgraph. Existing state-of-the-art exact solutions for SCS face challenges in practical efficiency due to ineffective strategies for searching candidate solutions and suboptimal method for finding optimal solution. To address these challenges, we propose a novel branch-and-bound algorithm that efficiently locating a subset of candidate solutions with favorable structural properties, forming the basis for three progressively refined frameworks to determine the optimal solution. Furthermore, we enhance practical performance through a new heuristic, two reduction rules, and a query decomposition technique. Extensive experiments on 12 large real-world graphs demonstrate that our most efficient framework significantly outperforms state-of-the-art methods, achieving an average speedup of two orders of magnitude while consistently identifying communities with higher cohesion.

Vision-language pretraining (VLP) models have demonstrated outstanding performance in image-text understanding tasks but remain highly susceptible to transferable adversarial attacks. While ensemble-based guided attacks improve adversarial transferability by increasing the diversity of image-text pairs, they primarily rely on spatial-domain data augmentation, which can lead to model overfitting to image details and limit the generalization capability of attacks. To address this limitation, this study proposes a frequency-domain adjustment-based adversarial attack method that modifies specific frequency components of input images to reduce detail interference and enhance the stability of adversarial examples. Additionally, a fine-grained feature extraction technique is introduced to optimize image-text alignment, further improving the transferability of cross-modal attacks. Experimental results demonstrate that the proposed method achieves superior attack transferability and generalization performance across two major VLP architectures, fusion models and alignment models, as well as multiple tasks on the Flickr30 K and MSCOCO datasets.

Deep learning is promising in open-world network intrusion detection, but current deep learning-based methods mainly focus on open recognition with properties that may not always hold and significantly neglect the inspection of unknown samples, increasing open space risks and manual inspection overhead for deployed models. To address these challenges in real-world environments, we propose a novel system, ORI, designed to tackle two critical tasks: 1) open recognition, including classifying known class samples while recognizing unknown ones, and 2) inspection, involving further inspecting samples recognized as unknown. Specifically, we reformulate open recognition as a binary classification task and propose a density-based method to recognize low-density samples as unknown while classifying known class samples with a closed-world classifier, thereby minimizing the risk associated with open spaces. To reduce the inspection overhead of samples recognized as unknown, we treat unknown sample inspection as a constrained clustering task, using a few manually inspected samples as constraints, and then assign labels to the remaining unknown samples via clustering. We evaluate our system against established open recognition and unknown sample inspection baselines through extensive experiments on three public datasets. Additionally, we simulated a security analyst inspecting unknown samples labeled by ORI. The experimental results demonstrate that ORI accurately classifies known class samples, recognizes unknown samples, and effectively labels samples recognized as unknown, enhancing both open recognition and inspection capabilities.

Deep neural networks (DNNs) are vulnerable to adversarial examples crafted by well-designed perturbations. This could lead to disastrous results on critical applications such as self-driving cars, surveillance security, and medical diagnosis. At present, adversarial training is one of the most effective defenses against adversarial examples. However, in traditional adversarial training, it is still difficult to achieve a good trade-off between clean accuracy and robustness since DNNs still learn spurious features. The intrinsic reason is that traditional adversarial training makes it difficult to fully learn core features from adversarial examples when noise and examples cannot be disentangled. In this paper, we disentangle the adversarial examples into natural and perturbed patterns by bit-plane slicing. We assume the higher bit-planes represent natural patterns and the lower bit-planes represent perturbed patterns, respectively. We propose Feature-Focusing Adversarial Training (F$^{2}$AT), which differs from previous work in that it enforces the model to focus on the core features from natural patterns and reduce the impact of spurious features from perturbed patterns. The experimental results demonstrated that the clean accuracy and adversarial robustness with our F$^{2}$AT can be significantly improved.

Convolutional neural networks, in which each layer receives features from the previous layer(s) and then aggregates/abstracts higher level features from them, are widely adopted for image classification. To avoid information loss during feature aggregation/abstraction and fully utilize lower layer features, we propose a novel decision fusion module (DFM) for making an intermediate decision based on the features in the current layer and then fuse its results with the original features before passing them to the next layers. This decision is devised to determine an auxiliary category corresponding to the category at a higher hierarchical level, which can, thus, serve as category-coherent guidance for later layers. Therefore, by stacking a collection of DFMs into a classification network, the generated decision fusion network is explicitly formulated to progressively aggregate/abstract more discriminative features guided by these decisions and then refine the decisions based on the newly generated features in a layer-by-layer manner. Comprehensive results on four benchmarks validate that the proposed DFM can bring significant improvements for various common classification networks at a minimal additional computational cost and are superior to the state-of-the-art decision fusion-based methods. In addition, we demonstrate the generalization ability of the DFM to object detection and semantic segmentation.

Knowledge graph (KG) is universally recognized for Knowledge representation. However, valuable information is usually distributed across multi-source heterogeneous data, especially in cyberspace security, which increases the difficulty of mining latent risk in KG. In this paper, we define four kinds of knowledge patterns to form cyberspace security data, and use KG embedding methods to enable the computation and reasoning ability in low-dimensional vector space. Based on our method, we can enable more effective risk detection than common KG embedding methods. We conduct experiments on the Peng Cheng Cyber Range, the experimental results validate the higher prediction accuracy and detection performance. The method can greatly improve the cyberspace security defense capabilities for networked system.

Deduplication-based techniques are popular in backup storage systems for reducing data volume. To maximize data reduction, existing fine-grained deduplication approaches not only eliminate duplicate chunks but also delta-compress non-duplicate chunks as delta relative to their similar (base) chunks. However, each chunk may have multiple similar chunks, and delta compression usually only selects one of them as the base chunk, i.e., a one-to-one scheme. This scheme benefits the restore performance because it needs to read only one (instead of multiple) base chunk in decompressing delta chunks, while it also wastes the potential compressibility among other similar chunks.In this paper, we propose SuperDelta to further exploit compressibility across multiple similar chunks and to preserve the restore performance advantage of the one-to-one scheme as much as possible. It is based on three techniques. (1) To further eliminate redundancy among similar chunks, SuperDelta applies a "Multiple Referenced Base Chunks" (MRBC) scheme instead of the one-to-one scheme. It combines several similar pairs of chunks in delta encoding to recover possibly lost compressibility in "boundary shift" problems. (2) To avoid the negative side effects of MRBC on restore performance, SuperDelta introduces a rebase scheme to rebuild simple reference paths among duplicate and similar chunks. It significantly simplifies the restore workflow, but also costs slightly more storage space because of impacting the workflow of redundancy detection. (3) To compensate for the additional storage cost, SuperDelta applies a space-recycle scheme to remove derived data when they become old while ensuring the optimized restore performance of the latest backups.Experiments on four real-world backup datasets show that SuperDelta increases the overall compression ratio by 1.05~2.40 times than the traditional one-to-one fine-grained deduplication without significantly affecting the backup and restore throughput.

Federated learning (FL) is widely studied for local privacy protection, and it involves exchanging model parameters rather than raw data among clients. However, gradient attacks (GAs) make a malicious client or parameter server of FL infer the local data of other clients only based on the model parameters exchanged. In FL frameworks and processes, it is important to understand the features that provide heuristic information for inferring raw data, as well as how best to defend against GAs. The academic community is currently investigating this problem. In this study, we demonstrate that the labels of input samples play a key role in the success of GAs. We analyze the rank of the coefficient matrix of the nonhomogeneous linear equation of gradients and input samples and propose an approach that performs special operations on the repetition and order of labels. The approach achieves a better defense effect against GAs without using a differential privacy (DP) framework. Our experimental results show that GAs fail (i.e., without leaking any valid information about local data) during the entire training process of a deep convolutional network in FL, and the accuracy of the network is less affected than that of DP. The code is available athttps://github.com/zhaohuali/Label-based-Defense.

The recent advancement of the Internet of Things (IoT) and information technology has led to the rapid expansion of interconnectivity among a billion devices across various applications. The advent of massive data has resulted in greater computational dependence, posing obstacles to applying security policies in energy-sensitive devices. However, public-key-based encryption algorithms are impractical or impossible to execute on these resource-limited terminals. In this paper, we propose a lightweight framework called STBCIoT based on a visual cryptography (VC) scheme to achieve low-latency encryption for large-scale data like biometric images. To reduce noise in encryption, we utilize central recognition and gray-level features of QR codes to integrate the visually friendly feature of QR into VC. we further propose a high-quality image generation model with the halftoning effect of VC to improve the quality of decrypted images. The experimental results demonstrate that our proposed method achieves high recognition performance on lossy decrypted images, effectively overcoming the performance limitations of traditional public key encryption methods for largescale images.

Intrusion detection is a crucial aspect of modern cybersecurity, aimed at identifying and responding to potential security threats within computer systems , networks, and applications. One of the major challenges faced by intrusion detection systems is the accurate detection and response to attack traffic, which is typically much lower in volume compared to normal traffic. This challenge becomes even more pronounced when the attack traffic is further classified into different attack categories, which may suffer from more severe data imbalance. To address these challenges, we present a novel approach to intrusion detection using a Multi-task Feature Fusion Pseudo-Siamese Network (MF2POSE) and a Category-distance Promotion Loss (CP Loss). The proposed MF2POSE leverages the Pseudo-Siamese Network (POSE-Net), which incorporates both a main network and a siamese network, to simultaneously perform binary-class and multi-class classification of the traffic. Furthermore, the Multi-task Feature Fusion (MF2) module enhances the multi-class classification performance of the main network by incorporating multi-scale internal features from the siamese network. Additionally, the CP Loss is introduced to aggregate the internal feature from the main network belonging to the same category, and separate the internal feature belonging to the different categories. We have conducted comprehensive experiments across two popular intrusion detection datasets, and the experimental results demonstrate the superior performance of our MF2POSE compared to existing state-of-the-art techniques, particularly in multi-class classification scenarios.

Continuously monitoring structural patterns in streaming graphs is a critical task in many real-time graph-based applications. In this paper, we study the problem of time-constrained continuous subgraph matching (shorted as TCSM) over streaming graphs. Given a query graph Q with timing order constraint and a data graph stream G , TCSM aims to report all incremental matches of Q in G for each update of G , where a match should obey both structure constraint (i.e., isomorphism) and timing order constraint of Q. Although TCSM has a wide range of applications, such as cyber-attack detection and credit card fraud detection, we note that this problem has not been well addressed. The state-of-the-art bears the limitations of high index space cost and intermediate result maintenance cost. In this paper, we propose TC-Match, an effective approach to TCSM. First, we design a space and time cost-effective index CSS, which is essentially a k -partite graph structure where a node corresponds to an edge in G. By carefully creating links between nodes, we can encapsulate into CSS the partial embedding and timing order information between edges in G. We theoretically show that CSS has polynomial space and construction time complexities. Second, based on the property of CSS, we develop an efficient incremental matching algorithm with an effective node merging optimization. Extensive experiments show that TC-Match can achieve up to 3 orders of magnitude query performance improvement over the baseline methods, and meanwhile the memory consumption is reduced by 48.7%-86.7%.

Nested named entity recognition (Nested NER) in knowledge graph (KG) aims at obtaining all meaningful entities, including nested entities for sentences in longer text region. Those obtained entities are to facilitate downstream applications, such as relation extraction, entity resolution, and coreference resolution. This task, however, is challenging not only because of the demand to detect the boundary of the entity but also due to the complexity of those hierarchically nested entities. Since a substantial amount of work has been made to Flat NER (or Nested NER), a few of them can explicitly acquire the position of the entity and utilize the grammatical construction of text. In this work, we propose PANNER, a POS-aware Nested NER model, to solve all the above issues. Specifically, we first construct a heterogeneous graph by introducing the part-of-speech (POS) information of the word. Second, we design a dilated random walk (DRW) algorithm based on a grammatical path to sample a fixed size of neighbors for each node. Third, we aggregate the message from different types of neighbors through an attention mechanism. Finally, we use a bidirectional decoding module to recognize and categorize all the flat and nested entities based on the node embedding in a layer-wise manner. Our extensive experiments show the effectiveness of PANNER in both flat and nested NER.

Recent studies have shown that Deep Neural Networks (DNNs) are easily deceived by adversarial examples, revealing their serious vulnerability. Due to the transferability, adversarial examples can attack across multiple models with different architectures, called transfer-based black-box attacks. Input transformation is one of the most effective methods to improve adversarial transferability. In particular, the attacks fusing other categories of image information reveal the potential direction of adversarial attacks. However, the current techniques rely on input transformations in the spatial domain, which ignore the frequency information of the image and limit its transferability. To tackle this issue, we propose Mixed-Frequency Inputs (MFI) based on a frequency domain perspective. MFI alleviates the overfitting of adversarial examples to the source model by considering high-frequency components from various kinds of images in the process of calculating the gradient. By accumulating these high-frequency components, MFI acquires a more steady gradient direction in each iteration, leading to the discovery of better local maxima and enhancing transferability. Extensive experimental results on the ImageNet-compatible datasets demonstrate that MFI outperforms existing transform-based attacks with a clear margin on both Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), which proves MFI is more suitable for realistic black-box scenarios.

In this paper, we study the problem of maximal biclique enumeration on large signed bipartite graphs. Given a signed bipartite graph$G=(U,V,E,s)$, a parameter$\theta \in [0.5, 1.0]$, our goal is to efficiently enumerate all maximal$\theta$-bicliques in$G$, where a maximal$\theta$-biclique$B(L,R)$is a complete subgraph of$G$with (1) the proportion of positive neighbors for each vertex in$B$is at least$\theta$, and (2)$B$is not contained in another biclique$B^{\prime }$, while$B^{\prime }$also satisfies (1). This problem has many applications, such as biclustering for genes, recommendation of similar groups, collaboration in communities, etc. However, it is computationally challenging due to its #P-completeness. Besides, we prove that even determining the maximality of a$\theta$-biclique is NP-hard. To the best of our knowledge, there is no efficient and scalable solution to this problem in the literature. In this paper, we first propose a branch-and-bound framework, namely${\sf MSiBE}$, which enumerates all maximal$\theta$-bicliques in a depth-first manner. Then, we develop three effective optimizations to improve the performance of${\sf MSiBE}$. (1) The local information of each search space is utilized to enhance the pruning capacity. (2) When expanding the partial biclique, we always focus on the side with fewer candidates first, by which fruitless search branches can be skipped early. (3) We implement${\sf MSiBE}$with efficient array reordering techniques and set intersection strategy. To further accelerate the computation, we introduce useful graph reduction techniques. Comprehensive performance studies on 10 real datasets demonstrate that our proposals can significantly outperform the baseline methods by up to 3 orders of magnitude.

Convolutional neural networks (CNNs) have been successfully applied to various fields. However, CNNs' overparameterization requires more memory and training time, making it unsuitable for some resource-constrained devices. To address this issue, filter pruning as one of the most efficient ways was proposed. In this article, we propose a feature-discrimination-based filter importance criterion, uniform response criterion (URC), as a key component of filter pruning. It converts the maximum activation responses into probabilities and then measures the importance of the filter through the distribution of these probabilities over classes. However, applying URC directly to global threshold pruning may cause some problems. The first problem is that some layers will be completely pruned under global pruning settings. The second problem is that global threshold pruning neglects that filters in different layers have different importance. To address these issues, we propose hierarchical threshold pruning (HTP) with URC. It performs a pruning step limited in a relatively redundant layer rather than comparing the filters' importance across all layers, which can avoid some important filters being pruned. The effectiveness of our method benefits from three techniques: 1) measuring filter importance by URC; 2) normalizing filter scores; and 3) conducting prune in relatively redundant layers. Extensive experiments on CIFAR-10/100 and ImageNet show that our method achieves the state-of-the-art performance on multiple benchmarks.

With the rapid development of information technologies, the security of cyberspace has become increasingly serious. Network intrusion detection is a practical scheme to protect network systems from cyber attacks. However, as new vulnerabilities and unknown attack types are constantly emerging, only a few samples of such attacks can be captured for analysis, which cannot be handled by the existing detection methods deployed in real systems. To handle this problem, we propose a few-shot class-incremental learning method called Branch Fusion Strategy based Network Intrusion Detection (BFS-NID for short), which can continuously learn new attack classes with only a few samples. BFS-NID includes a feature extractor module and a branch classifier learning module. The feature extractor module uses a vision transformer to learn better feature representations in a self-supervised manner, and the parameters of the feature extractor are fixed to avoid catastrophic forgetting when the model learns incrementally. The branch classifier learning module sets re-projection for different branch sessions to enhance the feature representation ability between classes and employs a branch fusion strategy to associate the context of learned attack classes with new classes in different sessions. We conducted extensive experiments on two popular network intrusion detection benchmark datasets (CIC-IDS2017 and CSE-CIC-IDS2018) and the results demonstrate that BFS-NID surpasses the baselines and achieves the best performance.

The widespread dissemination of Deepfake in social networks has posed serious security risks, thus necessitating the development of an effective Deepfake detection technique. Currently, video-based detectors have not been explored as extensively as image-based detectors. Most existing video-based methods only consider temporal features without combining spatial features, and do not mine deeper-level subtle forgeries, resulting in limited detection performance. In this paper, a novel spatiotemporal trident network (STN) is proposed to detect both spatial and temporal inconsistencies of Deepfake videos. Since there is a large amount of redundant information in Deepfake video frames, we introduce convolutional block attention module (CBAM) on the basis of the I3D network and optimize the structure to make the network better focus on the meaningful information of the input video. Aiming at the defects in the deeper-level subtle forgeries, we designed three feature extraction modules (FEMs) of RGB, optical flow, and noise to further extract deeper video frame information. Extensive experiments on several well-known datasets demonstrate that our method has promising performance, surpassing several state-of-the-art Deepfake video detection methods.

Delta encoding is a data reduction technique capable of calculating the differences (i.e., delta) among very similar files and chunks. It is widely used for various applications, such as synchronization replication, backup/archival storage, cache compression, and so on. However, delta encoding is computationally costly due to its time-consuming word-matching operations for delta calculation. Existing delta encoding approaches either run at a slow encoding speed, such as Xdelta and Zdelta, or at a low compression ratio, such as Ddelta and Edelta. In this article, we propose Gdelta, a fast delta encoding approach with a high compression ratio. The key idea behind Gdelta is the combined use of five techniques: (1) employing an improved Gear-based rolling hash to replace Adler32 hash for fast scanning overlapping words of similar chunks, (2) adopting a quick array-based indexing for word-matching, (3) applying a sampling indexing scheme to reduce the cost of traditional building full indexes for base chunks’ words, (4) skipping unmatched words to accelerate delta encoding through non-redundant areas, and (5) last but not least, after word-matching, further batch compressing the remainder to improve the compression ratio. Our evaluation results driven by seven real-world datasets suggest that Gdelta achieves encoding/decoding speedups of 3.5X∼25X over the classic Xdelta and Zdelta approaches while increasing the compression ratio by about 10%∼240%.

Adversarial attack on point clouds plays a vital role in evaluating and improving the adversarial robustness of 3D deep learning models. Current attack methods are mainly applied by point perturbation in a non-manifold manner. In this paper, we formulate a novel manifold attack, which deforms the underlying 2-manifold surfaces via parameter plane stretching to generate adversarial point clouds. First, we represent the mapping between the parameter plane and underlying surface using generative-based networks. Second, the stretching is learned in the 2D parameter domain such that the generated 3D point cloud fools a pretrained classifier with minimal geometric distortion. Extensive experiments show that adversarial point clouds generated by manifold attack are smooth, undefendable and transferable, and outperform those samples generated by the state-of-the-art non-manifold ones.

Large-scale matrix multiplication is a computational bottleneck in various applications including artificial intelligence and machine learning. Given the time complexity of O(n3) for matrix multiplication, large matrix computation is exceedingly time-consuming for the client-side user. By outsourcing this task to cloud servers with substantial computational resources, we can significantly reduce the client-side computational time. This paper presents a parallel matrix multiplication outsourcing scheme based on Cannon’s algorithm. By distributing the matrix across multiple cloud servers for parallel computation, we can get a significant efficiency speedup. Our scheme employs multiple cloud servers to perform parallel matrix computation, reducing the computational load by 89-97% when utilizing 4-16 servers as opposed to using a single server. We provide a comprehensive analysis of the scheme’s correctness, security, and verifiability, substantiating the benefits of our approach through the experimental data.

Hyperbolic embeddings excel in encoding hierarchical data, while Euclidean embeddings are more adept at capturing complex semantic relationships through rotation operations. However, advanced Euclidean embedding models face the problem of high dimensionality, while advanced hyperbolic embedding models struggle to accommodate multiple relations and different semantics. To address this problem, we introduce LMH-PKE, a novel model that leverages the Poincaré to embed the hierarchical structure of the relationship graph data. It captures rich semantic relationships in Euclidean and models multiple relations using learnable entity decision boundary parameters. By transforming entities embedded in hyperbolic through Möbius matrix vector multiplication and Möbius addition, and encoding multiple relations in Euclidean space using cosine functions, Our experiments demonstrate that LMH-PKE achieves state-of-the-art results in hyperbolic embedding models while maintaining low dimensional simplicity.

Automated code vulnerability detection has gained increasing attention in recent years. The deep learning (DL)-based methods, which implicitly learn vulnerable code patterns, have proven effective in vulnerability detection. The performance of DL-based methods usually relies on the quantity and quality of labeled data. However, the current labeled data are generally automatically collected, such as crawled from human-generated commits, making it hard to ensure the quality of the labels. Prior studies have demonstrated that the non-vulnerable code (i.e., negative labels) tends to be unreliable in commonly-used datasets, while vulnerable code (i.e., positive labels) is more determined. Considering the large numbers of unlabeled data in practice, it is necessary and worth exploring to leverage the positive data and large numbers of unlabeled data for more accurate vulnerability detection. In this paper, we focus on the Positive and Unlabeled (PU) learning problem for vulnerability detection and propose a novel model named PILOT, i.e., Positive and unlabeled Learning mOdel for vulnerability deTection. PILOT only learns from positive and unlabeled data for vulnerability detection. It mainly contains two modules: (1) A distance-aware label selection module, aiming at generating pseudo-labels for selected unlabeled data, which involves the inter-class distance prototype and progressive fine-tuning; (2) A mixed-supervision representation learning module to further alleviate the influence of noise and enhance the discrimination of representations. Extensive experiments in vulnerability detection are conducted to evaluate the effectiveness of PILOT based on real-world vulnerability datasets. The experimental results show that PILOT outperforms the popular weakly supervised methods by 2.78%-18.93% in the PU learning setting. Compared with the state-of-the-art methods, PILOT also improves the performance of 1.34%-12.46 % in F1 score metrics in the supervised setting. In addition, PILOT can identify 23 mislabeled from the FFMPeg+Qemu dataset in the PU learning setting based on manual checking.

Adversarial attacks have been successfully extended to the field of point clouds. Besides applying the common perturbation guided by the gradient, adversarial attacks on point clouds can be conducted by applying directional perturbations, e.g., along normal and along the tangent plane. In this article, we first investigate whether adversarial attacks with these two orthogonal directional perturbations are more imperceptible than that with the gradient-aware perturbation. Second, we investigate the deeper difference between adversarial attacks with these two directional perturbations, and whether they are applicable to the same scenarios. Third, based on the verification results that the above two directional perturbations have different sensitiveness to curvature, we devise a novel normal-tangent attack (NTA) framework with a hybrid directional perturbation scheme that adaptively chooses the direction according to the curvature of the local shape around the point. Extensive experiments on two publicly available data sets, e.g., ModelNet40 and ShapeNet Part, with classifiers in three representative networks, e.g., PointNet++, DGCNN, PointConv, validate the effectiveness of NTA, and the superiority to the state-of-the-art methods.

A plethora of healthcare data is produced every day due to the proliferation of prominent technologies such as Internet of Medical Things (IoMT). Digital-driven smart devices like wearable watches, wristbands and bracelets are utilized extensively in modern healthcare applications. Mining valuable information from the data distributed at the owners' level is useful, but it is challenging to preserve data privacy. Federated learning (FL) has swiftly surged in popularity due to its efficacy in dealing privacy vulnerabilities. Recent studies have demonstrated that Gradient Inversion Attack (GIA) can reconstruct the input data by leaked gradients, previous work demonstrated the achievement of GIA in very limited scenarios, such as the label repetition rate of the target sample being low and batch sizes being smaller than 48. In this paper, a novel method of End-to-End Gradient Inversion (E2EGI) is proposed. Compared to the state-of-the-art method, E2EGI's Minimum Loss Combinatorial Optimization (MLCO) has the ability to realize reconstructed samples with higher similarity, and the Distributed Gradient Inversion algorithm can implement GIA with batch sizes of 8 to 256 on deep network models (such as ResNet-50) and ImageNet datasets. A new Label Reconstruction algorithm is developed that relies only on the gradient information of the target model, which can achieve a label reconstruction accuracy of 81% in one batch sample with a label repetition rate of 96%, a 27% improvement over the state-of-the-art method. This proposed work can underpin data security assessments for healthcare federated learning.

Adversarial training is by far one of the most effective methods to improve the robustness of deep neural networks against adversarial examples. However, the trade-off between robustness and accuracy is still a challenge in adversarial training. Previous methods used adversarial examples with a fixed perturbation budget or specific perturbation budgets for each example, which is inefficient in improving the trade-off and lacks the ability to control the trade-off flexibly. In this paper, we show that the largest element of logit, [Formula: see text], can roughly represent the minimum distance between an example and its neighboring decision boundary. Thus, we propose group adaptive adversarial training (GAAT) that divides the training dataset into several groups based on [Formula: see text] and develops a binary search algorithm to determine the group perturbation budgets for each group. Using the group perturbation budgets to perform adversarial training can fine-tune the trade-off between robustness and accuracy. Extensive experiments conducted on CIFAR-10 and ImageNet-30 show that our GAAT can achieve a more perfect trade-off than TRADES, MMA, and MART.

Summary With advanced information technologies and industrial intelligence, Industry 4.0 has been witnessing a large scale digital transformation. Intelligent transportation plays an important role in the new era and the classic vehicle routing problem (VRP), which is a typical problem in providing intelligent transportation, has been drawing more attention in recent years. In this article, we study multidepot VRP (MDVRP) that considers the management of the vehicles and the optimization of the routes among multiple depots, making the VRP variant more meaningful. In addressing the time efficiency and depot cooperation challenges, we apply the artificial bee colony (ABC) algorithm to the MDVRP. To begin with, we degrade MDVRP to single‐depot VRP by introducing depot clustering. Then we modify the ABC algorithm for single‐depot VRP to generate solutions for each depot. Finally, we propose a coevolution strategy in depot combination to generate a complete solution of the MDVRP. We conduct extensive experiments with different parameters and compare our algorithm with a greedy algorithm and a genetic algorithm (GA). The results show that the ABC algorithm has a good performance and achieve up to 70% advantage over the greedy algorithm and 3% advantage over the GA.

Edge intelligence has played an important role in constructing smart cities, but the vulnerability of edge nodes to adversarial attacks becomes an urgent problem. A so-called adversarial example can fool a deep learning model on an edge node for misclassification. Due to the transferability property of adversarial examples, an adversary can easily fool a black-box model by a local substitute model. Edge nodes in general have limited resources, which cannot afford a complicated defense mechanism like that on a cloud data center. To address the challenge, we propose a dynamic defense mechanism, namely EI-MTD. The mechanism first obtains robust member models of small size through differential knowledge distillation from a complicated teacher model on a cloud data center. Then, a dynamic scheduling policy, which builds on a Bayesian Stackelberg game, is applied to the choice of a target model for service. This dynamic defense mechanism can prohibit the adversary from selecting an optimal substitute model for black-box attacks. We also conduct extensive experiments to evaluate the proposed mechanism, and results show that EI-MTD could protect edge intelligence effectively against adversarial attacks in black-box settings.

Overconfident predictions on out-of-distribution (OOD) samples is a thorny issue for deep neural networks. The key to resolve the OOD overconfidence issue inherently is to build a subset of OOD samples and then suppress predictions on them. This paper proposes the Chamfer OOD examples (CODEs), whose distribution is close to that of in-distribution samples, and thus could be utilized to alleviate the OOD overconfidence issue effectively by suppressing predictions on them. To obtain CODEs, we first generate seed OOD examples via slicing&splicing operations on in-distribution samples from different categories, and then feed them to the Chamfer generative adversarial network for distribution transformation, without accessing to any extra data. Training with suppressing predictions on CODEs is validated to alleviate the OOD overconfidence issue largely without hurting classification accuracy, and outperform the state-of-the-art methods. Besides, we demonstrate CODEs are useful for improving OOD detection and classification.

Many studies have revealed the vulnerability of deep neural networks (DNNs) in the face of adversarial attacks. By adding a small perturbation to the input, adversarial attacks could fool many advanced models for computer vision, speech recognition and natural language processing tasks, posing severe security threats to DNNs. In this paper, we proposed a gradient-based word-level attack method in the embedding space to attack text classification models. This method computes the significance of each word and chooses the optimal word for substitution after word embedding; the generated adversarial texts have little semantic changes but could successfully fool classification DNNs. Through extensive experiments, we confirmed that the generated adversarial texts could achieve a success rate approaching 100% with a very low word substitution rate in attacking the WordCNN and LSTM models on three datasets. By human evaluation, the adversarial texts evaded human notice which implied little semantic changes were made. Experiments on different models also confirmed the transferability of the adversarial texts. Finally, we adopted adversarial training, and this improved the models' generalization capacity and robustness.

Cognitive radio networks (CRNs) have been proposed to solve the spectrum scarcity problem. One of their fundamental procedures is to construct a communication link on a common channel for the users, which is referred to asrendezvous. In reality, the capability to sense the spectrum may vary from user to user. We study distributed rendezvous for heterogeneous sensing capabilities in this paper. The licensed spectrum is divided into$n$channels,$U = \lbrace 1,2,\ldots,n\rbrace$. We denote the sensing capability of user$i$as$C_i \subseteq U$and the set of available channels (i.e., the channels not occupied by paying users) as$V_i \subseteq C_i$. Due to hardware differences, the users may have different sensing capabilities:$C_i \ne C_j$, and this is calledheterogeneous sensing capability. In this paper, we propose efficient algorithms for two scenarios: the fully available scenario where$V_i = C_i$and the partially available scenario where$V_i \subseteq C_i$. Our idea is to utilize two ‘pointers’ to traverse the sensing capability set, which sets our algorithms apart from the extant rendezvous algorithms. Considering any two neighboring users$a, b$, we propose the Traversing Pointer (TP) algorithm that guarantees rendezvous in$O(\max \lbrace |C_a|,|C_b|\rbrace \log \log n)$time slots for the fully available scenario. This result is only$O(\log \log n)$larger than the theoretical lower bound. Moreover, it removes an$O(\min \lbrace |C_a|,|C_b|\rbrace)$factor when compared to the state-of-the-art result ($O(|C_a||C_b|)$in S.-H. Wuet al.For the partially available scenario, we propose the Moving Traversing Pointers (MTP) and Prime based Moving Traversing Pointers (P-MTP) algorithms that can guarantee rendezvous within$O((\max \lbrace |V_a|,|V_b|\rbrace)^2\log \log n)$and$O(|V_a||V_b|\log \log n)$time slots respectively, where the latter one combines the pointers and a common technique of plugging in a prime number. The proposed algorithms work more efficiently than the previous best result ($O(|C_a||C_b|)$in C.-C. Wuet al.under various circumstances. We also conduct extensive simulations and the results corroborate our analyses.

Visual cryptography scheme (VCS) is a secret‐sharing scheme which encrypts images as shares and can decrypt shares without digital devices. Although a participant can reveal the secret image by merely stacking a sufficient number of shares, the visual quality of recovered images is reduced, and malicious adversaries can cheat participants by giving faked shares. The paper presents a novel VCS called T‐VCS (trusted VCS) which consists of two main components: a high‐quality VCS and an enhanced verification scheme of shares based on the emerging Intel Software Guard eXtensions (SGX). While providing high‐quality recovery, T‐VCS keeps the size of the shares the same as the original secret image. We use SGX to act as a trusted third party (TTP) to verify the validity of the shares in an attested enclave without degrading the image quality. The experimental results show that T‐VCS can achieve a balance among contrast, share size, and verification efficiency.

Rendezvous is a fundamental building block in distributed cognitive-radio networks (CRNs), in which pairs or groups of users must find a jointly available channel. Research on the rendezvous problem has focused so far on minimizing the time to rendezvous (to find a suitable channel) or on maximizing the rendezvous degree (percentage of channels on which rendezvous can take place). In this paper, we model the rendezvous problem in a more realistic way that acknowledges the fact that available channels may suffer from interference which varies from location to location as well as over time. In other words, channels are influenced by heterogeneous interference. In this setting, CRNs benefit from rendezvous methods that find a quiet channel, which supports high symbol rates and does not suffer much from dropped packets. In this paper, we propose three important rendezvous design disciplines to achieve bounded rendezvous time, full rendezvous degree, and to rendezvous on quiet channels that suffer little interference. We first present the Disjoint Relaxed Difference Set (DRDS) based rendezvous algorithm as a cornerstone, which ensures rendezvous on every channel (full rendezvous degree) in bounded time. When channels suffer from heterogeneous interference, we propose the Interference based DRDS (I-DRDS) algorithm which ensures rendezvous on channels with less interference, incorporating the interference normalization and interference mapping methods. We conduct extensive simulations to evaluate the proposed algorithms; compared with the state-of-the-art algorithms, the results show that I-DRDS has the best rendezvous performance on less interfered channels, with slightly larger rendezvous time.

In resource exchange networks, nodes or agents may cooperate or compete with each other to maximize their own profits. In competitive networks, they may determine their exchange strategy selfishly, which makes it a challenge problem to design fair and efficient cooperation strategy. A popular resource exchanging mechanism called Max-Min Fairness-Based Coalitional (MMFC) is proposed in [1] to solve the problem in competitive networks, however it is still an open problem whether the mechanism is trustful if an agent lies about its resource information. In this paper, we demonstrate the trustfulness of the MMFC mechanism; combining theoretical analyses and numerical examples, we show that an agent cannot gain more benefit by misreporting its resource information.

Accurate and robust localization is crucial for wireless ad-hoc and sensor networks. Among the localization techniques, component-based methods advance themselves for conquering network sparseness and anchor sparseness. But component-based methods are sensitive to ranging noises, which may cause a huge accumulated error either in component realization or merging process. This paper presents three results for robust component-based localization under ranging noises. (1) For a rigid graph component, a novel method is proposed to evaluate the graph's possible number of flip ambiguities under noises. In particular, graph's \emph{MInimal sepaRators that are neaRly cOllineaR (MIRROR) } is presented as the cause of flip ambiguity, and the number of MIRRORs indicates the possible number of flip ambiguities under noise. (2) Then the sensitivity of a graph's local deforming regarding ranging noises is investigated by perturbation analysis. A novel Ranging Sensitivity Matrix (RSM) is proposed to estimate the node location perturbations due to ranging noises. (3) By evaluating component robustness via the flipping and the local deforming risks, a Robust Component Generation and Realization (RCGR) algorithm is developed, which generates components based on the robustness metrics. RCGR was evaluated by simulations, which showed much better noise resistance and locating accuracy improvements than state-of-the-art of component-based localization algorithms.

Software defined networks (SDNs) have emerged as a promising paradigm by separating control plane from data plane. In this paper, we study the problem of achieving realtime quality of services in a SDN for both centralized control plane and distributed control plane. In the first place, we propose a system prototype that handles real-time end to end services. Then, we formulate the problem for a centralized control plane as a linear programming and apply prime-dual technique to derive an efficient algorithm. We also discuss about the method to achieve load balancing between link resources and node resources. Finally, we extend the algorithm to a distributed control plane and present efficient algorithms to achieve good competitive ratio.

Neighbor discovery is a fundamental step in constructing wireless sensor networks and many algorithms have been proposed aiming to minimize its latency. Recent developments of intelligent devices call for new algorithms, which are subject to energy restrictions. In energy-restricted large-scale networks, a node has limited power supply and can only discover other nodes that are within its range. Additionally, the discovery process may fail if excessive communications take place in a wireless channel. These factors make neighbor discovery a very challenging task and only a few of the proposed neighbor discovery algorithms can be applied to energy-restricted large-scale networks. In this paper, we propose Alano, a nearly optimal algorithm for a large-scale network, which uses the nodes' distribution as a key input. When nodes have the same energy constraint, we modify Alano by the Relaxed Difference Set (RDS), and present a Traversing Pointer (TP) based Alano when the nodes' energy constraints are different. We compare Alano with the state-of-the-art algorithms through extensive evaluations, and the results show that Alano achieves at least 31.35% lower discovery latency and has higher performance regarding quality (discovery rate) and scalability.

Rendezvous is a fundamental building block in distributed cognitive radio networks (CRNs), where users must find a jointly available channel. Research on the rendezvous problem has focused so far on minimizing the time to rendezvous (to find a suitable channel) or on maximizing the degree (number of channels on which rendezvous can take place). In this paper, we model the rendezvous problem in a more realistic way that acknowledges the fact available channels may suffer from interference, and interference may vary among users in different locations over time. In this setting, CRNs benefit from rendezvous methods that find a quiet channel, which supports high symbol rates and does not suffer much from dropped packets. We propose algorithms that achieve this goal for both initial rendezvous problem (users share no prior information) and continuous rendezvous problem (users who have already established a link must vacate the channel and seek another). We propose both deterministic and randomized methods based on mapping the channel set to a larger set in a way that gives preference to quiet channels. This technique allows us to add interference-awareness to existing rendezvous algorithms. We analyze the new algorithms and substantiate our analyses through extensive simulations.

Cognitive Radio Network (CRN) is a promising technique for solving the wireless spectrum scarcity problem. Rendezvous is the fundamental process of CRNs. We aim at designing faster rendezvous algorithms for CRNs. We find that local information such as user's ID and the label of an available channel is very useful for designing faster rendezvous algorithms. First, we propose the Sequence-Rotating Rendezvous (SRR) algorithm. The SRR algorithm can guarantee rendezvous for any two users i and j in (2P²+ 2P) timeslots, where P is the least prime not less than the total number of channels in the network. Second, we utilize the user's identifier (ID) to design an ID-based Rendezvous (IDR) algorithm. The IDR algorithm can guarantee rendezvous for any two users i and j in (l + 1)(P_i + 2)(P_j + 2) timeslots, where Pi and Pj are the smallest primes which are not less than the numbers of available channels of users i and j respectively. Third, we propose a Channel-Label- based Rendezvous (CLR) algorithm which can guarantee rendezvous for any two usersin ((P_i+2) (P_j+2)+PN)(⌈log_2N⌉+1) timeslots, where N is the total number of channels in the network and P_N is the least prime which is not less than N. The theoretical Maximum Time To Rendezvous (MTTRs) of the three algorithms we propose are less than those of the state-of-the-art algorithms in the corresponding categories respectively in certain scenarios. All of our algorithms can be used in multi-user scenarios. We conduct a number of experiments to compare our algorithms with state- of-the-art rendezvous algorithms in different scenarios, the results of which confirm our theoretical analysis.

Neighbor discovery is essential to construct wireless sensor networks. Most existing studies on neighbor discovery are designed on the assumption that either all nodes are within the radio range of each other, or only two nodes compose the network. However, networks are partially-connected in reality: some nodes are within the radio range of each other, while others are not. Low latency and energy efficiency are two common goals, which become even more challenging to achieve at the same time in partially-connected networks. We find the collision caused by simultaneous transmissions is the main obstruction of achieving the two goals. In this paper, by alleviating collisions, we present Panacea, the first low-latency, energy-efficient protocol for a partially-connected network. To begin with, we design Panacea-WCD for nodes that have collision detection mechanisms and Panacea-NCD for nodes that do not have. Then, we prove the discovery latency of Panacea is bounded by a low latency O(n·ln n), where each node has n neighbors on average. Finally, we implement Panacea for evaluations in complicated partially-connected networks. The evaluated results corroborate our analyses: compared with two notable protocols, i.e. PND and Coupon, Panacea achieves up to 5.14 times lower latency with the same energy consumption; and saves 30% energy for similar latency with Coupon.

Big-data frameworks (e.g., Spark) enable computations on tremendous data records generated by third parties, causing various security and reliability problems such as information leakage and programming bugs. Existing systems for big-data security (e.g., Titian) track data transformations in a record level, so they are imprecise and too coarse-grained for these problems. For instance, when we ran Titian to drill down input records that produced a buggy output record, Titian reported 3 to 9 orders of magnitude more input records than the actual ones. Information Flow Tracking (IFT) is a conventional approach for precise information control. However, extant IFT systems are neither efficient nor complete for big-data frameworks, because theses frameworks are data-intensive, and data flowing across hosts is often ignored by IFT.

In constructing the cognitive radio networks, a fundamental process is to establish a communication link on a same channel for every two users, which is referred to as rendezvous. Most studies focus on minimizing the time to rendezvous after they start the process synchronously or asynchronously. However, to the best of our knowledge, no work has considered the possibility that the users may achieve different utilities when they rendezvous on different channels for communication. The utility originates from the quality of the specific channel that two users rendezvous on, and it is influenced by the channel's bandwidth, transmission rate, stability, etc. In this paper, we formally formulate the problem of maximizing rendezvous utilities and propose a novel method by extending the channels to promote the users to achieve higher rendezvous utilities. We propose channel extension algorithms for both symmetric and asymmetric rendezvous scenarios, where the users may have the same or different sets of rendezvous channels respectively. These algorithms are built on the construction of Disjoint Relaxed Difference Set (DRDS) in [5], and we show the efficiency of achieving rendezvous on the channel with high utility theoretically. Moreover, we conduct thorough simulations to evaluate our algorithms and the results also corroborate our theoretical analyses.

Group recommendation has attracted significant research efforts for its importance in benefiting a group of users. This paper investigates the Group Recommendation problem from a novel aspect, which tries to maximize the satisfaction of each group member while minimizing the unfairness between them. In this work, we present several semantics of the individual utility and propose two concepts of social welfare and fairness for modeling the overall utilities and the balance between group members. We formulate the problem as a multiple objective optimization problem and show that it is NP-Hard in different semantics. Given the multiple-objective nature of fairness-aware group recommendation problem, we provide an optimization framework for fairness-aware group recommendation from the perspective of Pareto Efficiency. We conduct extensive experiments on real-world datasets and evaluate our algorithm in terms of standard accuracy metrics. The results indicate that our algorithm achieves superior performances and considering fairness in group recommendation can enhance the recommendation accuracy.

Rendezvous is a fundamental process in constructing cognitive radio networks (CRNs), in which two users find a common channel for communication. The licensed spectrum is assumed to be divided into n non-overlapping channels and the users can sense the spectrum by equipping with cognitive radios. Most of previous works assume that the user can find a set of available channels (the channels not occupied by the licensed users) after spectrum sensing stage and the status of all channels are stable all the time. However, this assumption may not be true in reality and we focus on designing efficient algorithms when the status of the channels varies dynamically. In this paper, we introduce two models to describe the dynamic rendezvous problem. Denote pij as the probability that channel j is available for user i. In the Independent model, assuming all pij variables are independently distributed and we propose efficient algorithms for both synchronous and asynchronous users, which guarantee rendezvous in O (log2 n) and O (log3 n) time slots with high probability respectively. In the Dependent model, two nearby users have relevant available probabilities and we introduce a sensing phase and an attempting phase to guarantee rendezvous in O (ε log3 n log log log n) time slots with high probability, where ε is a small constant. We also present an algorithm to increase rendezvous load in the long run, which guarantee rendezvous for at least 1/4 of all time slots.

Binary proximity sensors (BPS) is a generic model for many non- collaborative, presence detecting sensor. It outputs "1'' when one or more targets are presenting in its sensing range and "0" otherwise. It cannot tell the number of targets nor the targets' identities in its sensing range. But for its privacy protection and device-free properties, BPS-based tracking has attracted great attentions. However, multiple target counting and tracking (MTCT) by BPS network remains very challenging. Existing approaches generally rely on trajectory decomposition, which suffer association complexity issue and can hardly provide accurate results. To address these challenges, this paper presents an novel intensity-based counting and tracking approach, called IntenCT, which tracks the evolvement of the multi-targets' probabilistic density distribution overtime, without the complexity of enumerating the multiple targets' trajectories. Then, clustering algorithms on the density distribution are proposed to find the target groups, and count the targets in each group by calculating the integral of the density distribution in the group region. At last, the trajectories of the separable targets in each group are estimated using K-means and a motion consistency model. Extensive analysis and simulations show that IntenCT has quadratic complexity which is very efficient; provides the current best known multi-target counting lower bound; and tracks the multi-targets more accurately than the existing approaches.

Radio-map based method has been widely used for indoor location and navigation, but remaining key challenges are: 1) laborious efforts to calibrate a fine-grained radio-map, and 2) the locating result inaccuracy and not robust problems due to random signal strength (RSS) noises. An efficient way to overcome these problems is to collect RSS signatures along indoor paths and utilize sequence matching to enhance the location robustness. But, due to problems of indoor path combinational explosion, random RSS loss during movement, and moving speed disparity during online and offline phases, how to exploit sequence matching in radio-map remains difficult. This paper proposes WarpMap, an efficient sequence-type radio-map model and an accurate indoor location method by dynamic warping. Its distinct features include: 1) an undirected graph model (Trace-graph) for efficiently calibrating and storing sequence-type radio-map, which overcomes the path combinational explosion and RSS miss-of-detection problems; 2) an efficient sub-sequence dynamic time warping (SDTW) algorithm for accurate and efficient on-line locating. We show SDTW can tolerate random RSS disparities at discrete points and handle the moving speed differences in on-line and offline phases. The impacts of different warping distance functions, RSS preprocessing techniques were also investigated. Extensive experiments in office environments verified the efficiency and accuracy of WarpMap, which can calibrated within ten minutes by one person for 1100m2 area and provides overall nearly 20% accuracy improvements than the state-of-the-art of radio-map method.

Controlling a dynamic network is interesting and important in practical applications, which is to drive the network from any initial state to any desired state. Much research has been conducted in revealing the controllability and seeking the underlying correlations of the network. However, no existing works have considered the time needed to control the network, which we refer to as control latency. In this paper, we initiate the study of control latency of dynamic networks. First of all, we formulate the minimum control latency (MCL) problem for designing the controlling pattern with minimum number of controllers. We show that the MCL problem is NP-hard by reducing the multiprocessor scheduling problem to it. Then, we propose a greedy algorithm for designing a controlling pattern that can control the network within two times the minimum control latency. Moreover, when the control latency is bounded by a given value, we propose another constant approximation algorithm to design a controlling pattern which uses at most three times the minimum number of controllers. We conduct extensive simulations on both synthetic and real networks to corroborate our theoretic analysis.

Cognitive radio networks (CRNs) have been proposed to solve the spectrum scarcity problem. One of their fundamental procedures is to construct a communication link on a common channel for the users, which is referred as rendezvous. In reality, the capability to sense the spectrum may vary from user to user, and such users form what is known as a heterogeneous cognitive radio network (HCRN). The licensed spectrum is divided in to n channels, U = {1, 2,..., n}. We denote the capability of user i as Ci⊆ U and the set of available channels (i.e. the channels not occupied by the paying users) as Vi⊆ Ci. We study the rendezvous problem in HCRN under two circumstances: fully available spectrum (Vi= Ci) and partially available spectrum (Vi≠ Ci). For any two users a, b, we propose the Traversing Pointer (TP) algorithm that guarantees rendezvous in O(max{|Ca|,|Cb|}log log n) time slots for the fully available spectrum scenario. This result is only O (log log n) larger than our constructive lower bound. Moreover, it removes an O(min{|Ca|, |Cb|}) factor as compared to the state-of-the-art result (O(|Ca||Cb|) in [26]). For the partially available spectrum scenario, we propose the Moving Traversing Pointers (MTP) algorithm to guarantee rendezvous in O((max{|Va|, |Vb|})2log log n) time slots, which works more efficiently than the previous best result (O(|Ca||Cb|) in [25]) in various circumstances. We also conduct extensive simulations and the results corroborate our analysis.

In this paper, we initiate the Communication and Block Game between two unlicensed users and an adversary in Cognitive Radio Networks (CRNs). In each time slot, the two unlicensed users can successfully communicate on the common available channel if it is not blocked by the adversary. In the communication and block game, the two un-licensed users aim to maximize their communication load, denoted as the number of time slots of their successful communications, while the adversary aims to minimize it. We propose efficient algorithms for both users and the adversary and we prove the proposed algorithms will lead a Nash Equilibrium, i.e. the users can achieve the maximum communication load against any adversary's blocking strategy, while the adversary can minimize the users' communication load against any users' channel accessing strategy. We also present efficient algorithms for both users and adversary for the multiple channels scenario where the users and the adversary are equipped with multiple radios. These algorithms also guarantee high communication load for the users, while the adversary can also block a considerable number of users'communications. Our simulations validate the theoretical analyses.

Rendezvous process is the cornerstone to construct Cognitive Radio Networks (CRNs), through which a secondary user can establish a link for communication with its neighbor on a common channel. Although many blind rendezvous algorithms have been proposed which do not rely on a central controller or a common control channel, all of these works still rely on the global parameters such as the number of licensed channels N and the number of users. This paper aims to design fully distributed blind rendezvous algorithms only based on each user's local information. We first give the Synchronous Check & Hop (SCH) algorithm for two synchronous users where they start the rendezvous process at the same time. The SCH algorithm guarantees rendezvous in O(min{ka,kb} N) time slots where ka,kb are the corresponding number of sensed channels of these two users. Our main contribution is a fully distributed algorithm called Conversion Based Hopping (CBH), where each user only uses its identifier (ID) and its number of sensed channels. CBH guarantees rendezvous between two asynchronous users in O((max{ka,kb})2) time slots. To our knowledge, this is the first result with rendezvous time independent of the global parameter N. We also derive a lower bound of rendezvous time between two users as Ω((ka-kg)(kb-kg)) where k_g is the number of their common channels. All of our results also apply to a more general blind rendezvous problem which we call Oblivious Blind Rendezvous where each user is free to assign their local labels to the sensed channels. Extensive simulation results compared with the state-of-the-art rendezvous algorithms corroborate our theoretical analyses.

Rendezvous is a fundamental process in constructing Cognitive Radio Networks (CRNs), through which the user can communicate with its neighbors by establishing a link on some licensed frequency band (channel). Most of the existing elegant rendezvous algorithms assume each user is equipped with a single radio. Nowadays the multi-radio cognitive radio architecture, where each user can access k ≥ 2 channels at the same time, has become a reality. In this paper, we study the rendezvous problem in multi-radio CRN to see whether and to what extent the multi-radio capability can improve the rendezvous performance. To begin with, we propose a family of deterministic distributed algorithms for two special situations when k=2 and k=O(√n), where n is the number of all channels. These algorithms show that the maximum time to rendezvous (MTTR) can be reduced (largely) in multi-radio CRN. Then we derive a lower bound of MTTR as Ω({|Vi||Vj|}/k2) for arbitrary k (Vi, Vj represents two users' available channel sets) and present a distributed algorithm to guarantee rendezvous in O({|Vi||Vj|}/k2) time slots, which meets the lower bound. Extensive simulations are conducted to corroborate our theoretical analyses.

Rendezvous process plays an important role in constructing Cognitive Radio Networks (CRNs), through which a user establishes a link on a common licensed channel for communication with its neighbors. Generally, the licensed spectrum is divided into N channels and most blind rendezvous algorithms are realized by the “channel hopping” method where each user repeats a Global Sequence constructed on top of all the N channels. This global sequence based method may contain lots of redundant channels resulting in large rendezvous time especially when the number of available channels each user has only accounts for a small fraction of all the N channels. In this paper, we introduce the Local Sequence based rendezvous algorithms where the local sequence is only constructed on top of each user's available channels and different user's local sequence could be different. Our first local sequence based algorithm called LS can guarantee rendezvous in O(N) time slots for symmetric users (both users have the same set of available channels) and in O(N2) time slots for asymmetric users, which matches the best known results [11]. Our major contribution is the Modified Local Sequence (MLS) based algorithm which can guarantee an exponentially shorter rendezvous time than the best known results when the number of available channels each user has is relatively small. Extensive simulation results comparing with the state-of-the-art rendezvous algorithms corroborate our theoretical analyses.

Gathering information in a sensing field of interest is a fundamental task in wireless sensor networks. Current methods either use multihop forwarding to the sink via stationary nodes or use mobile sinks to traverse the sensing field. The multihop forwarding method intrinsically has the energy hole problem and the mobile sinks method has a large gathering latency due to its low mobility velocity. In addition, all the mobile sinks methods assume unlimited power supply and memory which is unrealistic in practice. In this paper, we propose a new approach for information gathering through a Mobile Aerial Sensor Network (MASN). We adopt the Hive-Drone model [5] where a centralized station (Hive) responsible for serving and recharging Micro-Aerial Vehicle (MAV) sensor nodes (Drones) is strategically placed in the sensing field. We then face the challenges of how to control the mobility of each MAV and devising interference-free scheduling for wireless transmissions that can substantially reduce the latency. We present a family of algorithms with constant memory to reduce both gathering latency, which is the duration from dispatching the MAVs to the moment when all the sensed information are gathered at the central station, and information latency, which is the duration from when some information is sensed to when it is received by the station. We also consider how to extend the single Hive to multiple Hives for monitoring an arbitrarily large area. Extensive simulation results corroborate our theoretical analysis.

Rendezvous is a fundamental process in Cognitive Radio Networks, through which a user establishes a link to communicate with a neighbor on a common channel. Most previous solutions use either a central controller or a Common Control Channel (CCC) to simplify the problem, which are inflexible and vulnerable to faults and attacks. Some blind rendezvous algorithms have been proposed that rely on no centralization. Channel Hopping (CH) is a representative technique used in blind rendezvous, with which each user hops among the available channels according to a pre-defined sequence. However, no existing algorithms can work efficiently for both symmetric (both parties have the same set of channels) and asymmetric users. In this paper, we introduce a new notion called Disjoint Relaxed Difference Set (DRDS) and present a linear time constant approximation algorithm for its construction. Then based on the DRDS, we propose a distributed asynchronous algorithm that can achieve and guarantee fast rendezvous for both symmetric and asymmetric users. We also derive a lower bound for any algorithm using the CH technique. This lower bound shows that our proposed DRDS based distributed rendezvous algorithm is nearly optimal. Extensive simulation results corroborate our theoretical analysis.

The SINR model has attracted much attention in the field of wireless networks. The path loss exponent α in the model is generally treated as a constant between two and six. However, in real scenarios, the path loss is influenced by many factors such as environment (vegetation and barriers), propagation medium (dry or moist air), the distance between the transmitter and the receiver, etc. Therefore, the exact value of α is hard to detect in real scenarios and the attenuation of signal powers transmitted through different areas varies, which causes the value of α to ebb and flow among all wireless requests. In this paper, we initiate the study about the impact of α that fluctuates on the SINR model. We prove that for any given α and the fluctuation δ, a specific topology can always be constructed which is extremely vulnerable to the small change in α and all the existing algorithms dealing with wireless network problems perform dramatically poorly with the inaccurate α value. We call algorithms that can still perform well despite the fluctuating α “α-Robust” algorithms and we propose the first α-Robust algorithm for the Connectivity Problem which generates a link schedule with size of O(log n log Δ) even in the worst case, where Δ is the ratio between the longest and the shortest links in a nearest neighbor tree.