The rapid popularization of the Internet of Vehicles demands efficient, privacy-preserving distributed learning. However, deploying Federated Learning in dynamic IoV environments faces a ”trilemma” of model adaptability, communication efficiency, and privacy, exacerbated by severe spatiotemporal data heterogeneity. To address this issue, we propose a personalized hierarchical framework that integrates split meta-learning within a ”vehicle-edge-cloud” architecture, referred to as pHFSML. At the vehicle-edge layer, semi-asynchronous split meta-learning protocols significantly reduce vehicular computational burdens, enabling rapid local adaptation. At the edge-cloud layer, gradient-sensitive momentum aggregation and loss-adaptive personalization ensure global stability while retaining local precision. Extensive experiments on non-IID benchmarks verify pHFSML’s superiority. Specifically, on the domain-relevant GTSRB dataset, pHFSML achieves 93.03% accuracy, outperforming state-of-the-art baselines by 0.80%, with a convergence speed 38.8% faster than FedAvg. Ablation studies further validate the necessity and synergistic effects of the proposed components.

The hierarchical federated learning framework significantly reduces communication burdens on central servers; however, the integration of edge servers introduces potential risks such as Single Point of Failure (SPOF) and ongoing challenges like imbalanced data distribution. To tackle these challenges, we propose TrustHFL, an innovative aggregation method designed for secure hierarchical federated learning. TrustHFL enhances training efficiency by employing group training that clusters clients with similar data distribution characteristics. Inside each cluster, synchronous aggregation is implemented, while asynchronous aggregation is utilised between clusters to alleviate delays from bottleneck clients. We also introduce a robust access control mechanism for secure interactions between clients and edge servers, ensuring data privacy and system integrity. Moreover, our design favours off-chain computation and training, limiting on-chain storage to essential information and thereby minimising both storage and computational demands on the blockchain, ultimately enhancing training efficiency. Extensive experimental results demonstrate that the proposed method accelerates convergence speed and enhances model accuracy. Compared to existing classical federated learning methods, the model accuracy is improved by an average of 1.98% under various data distribution scenarios, while the time required to achieve the same accuracy is reduced by an average of 65.54%.

Software-defined Networking (SDN) is an emerging network architecture. The decoupled data and control plane provide flexible manageability and programmability to the network. One of the core components in SDN switches to direct traffic forwarding is the flow table, which is usually stored in the ternary content addressable memory (TCAM) with limited space and high power consumption, making the flow table a potential target for attacks. This paper examines the Low-Rate Flow Table Overflow (LRFTO) attacks, which fill the flow table and render it unavailable by continuously sending attack rules to occupy the space. We propose a quantitative model of LRFTO attacks to describe the attack behavior, analyze its difference from legitimate traffic, and summarize some key aspects and features to distinguish attack rules from legitimate rules. We also propose LRFTO-ADMS, a system that utilizes machine learning-based classification as its core to evict suspicious rules, thereby ensuring flow table availability. Experimental results show that the threat model can adapt correctly to the network environment. The proposed LRFTO-ADMS can evict attack rules with an accuracy of more than 95% and low overhead while protecting the flow table and the legitimate rules from being unavailable.

Set-valued data, an important data form expressing diversity and uncertainty, is widely used in fields such as recommendation systems and social network analysis. However, such data often contains fine-grained records, which may lead to the leakage of users’ sensitive information. To this end, some privacy-preserving set-valued data analysis schemes have been proposed. This paper first proposes a joint shift inference attack against the cyclic shift-based local differential privacy (LDP) protocol introduced by Huang et al. (10.1109/TIFS.2024.3423657), it exploits deterministic cyclic-shift patterns and significant frequency differences to infer the user’s original data. Experimental results demonstrate that the user’s original data can be inferred with a probability exceeding 96%. Theoretically, the cyclic-shift mechanism violates the core requirement of local differential privacy due to its non-surjective output space. To overcome the limitations of existing schemes, we propose a privacy-preserving joint distribution analysis scheme for set-valued data via LDP (SVJDA). It employs the Sparse Vector Mean Estimation (SVME) mechanism and utilizes a sign-based hashing function to compress data, allowing privacy-preserving joint distribution analysis while introducing only minimal additional noise. Theoretical analysis shows that SVJDA satisfies ϵ-LDP with minimal estimation error. The experimental results confirm that SVJDA achieves higher accuracy in joint distribution estimation while ensuring the accuracy of frequent itemset identification. For ϵ ∈ [0.4, 1], the L∞ error of SVJDA is only 7.208%–21.725% of SVSM and 2.821%–7.279% of LDP-RM, while its MSE is 0.00364%–2.338% of SVSM and 0.017%–0.068% of LDP-RM, demonstrating its superior performance.

Radiation therapyis one of the primary treatment modalities for head and neck (H&N) cancer in clinical practice, aiming to deliver sufficient dose to Planning Target Volume (PTV) while protecting surrounding Organs at Risk (OAR) from or minimizing exposure to radiation. Quantitative dose prediction of various tissues and organs is a prerequisite for implementing intelligent precision radiotherapy. In order to improve dose prediction accuracy, we propose a generative adversarial network CPFTransGAN based on Cross Perception Fusion Transformer (CPF Transformer). Specifically, we design a CPF Transformer module through deeply integrating CNN and Transformer. Using the CPF Transformer as basic unit, we constructed a generator with four-stage encoding-decoding structure called CPFTransGenerator. An adaptive weight loss is used to train the discriminator to alleviate the issues of imbalance training in adversarial learning. To further improve the prediction accuracy, a multiscale cross-window encoding network is designed, which can constrain the differences between predicted dose and the reference one at different granularity levels by calculating feature losses between them at different scales. The proposed method is evaluated on two public head and neck cancer datasets and a local clinical dataset. Extensive experiments demonstrate the superior performance of our method compared with the state-of-the-art ones.

As deep learning (DL) continues to advance, effective feature extraction from large-scale data remains crucial for enhancing model performance. To leverage the advantages of the frequency domain, such as concentrated signal energy, prominent data features, and rich detailed characteristics, this paper proposes a novel frequency-domain feature extraction method. However, existing frequency component selection algorithms often struggle to adapt to diverse tasks, tend to yield only locally optimal solutions, and require prolonged processing times. To overcome these limitations, we introduce the Adaptive Fast Frequency Selection (AFFS) algorithm, which seamlessly integrates a frequency component selection factor layer into DL models to identify globally optimal frequency combinations suited to various downstream tasks. We further analyze the relationship between selected frequency components and model performance, providing theoretical guarantees regarding optimality, robustness, and generalization error bounds. Moreover, a fast selection procedure is developed to exploit the empirically observed rapid convergence of the selection-factor ranking, significantly accelerating the selection process. Extensive experiments on five datasets, ten DL models, and two subsequent tasks demonstrate that AFFS achieves superior performance: even when the input data size is reduced to only 10% of the original frequency features, model classification accuracy improves by approximately 1%, while the early stopping mechanism shortens the selection process by about 80%.

The decoupling of SDN control plane and data plane allows control to be carried out independently, enhancing the programmability and manageability of the whole network. However, though this new architecture brings convenience for control, it also sets a pool for attacks. Due to the expensive and resource-consumption characteristics, SDN switches usually have a limited ternary content addressable memory to cache the flow rules in the data plane. Attackers can launch a space-consuming attack to maliciously preempt the flow table, forcing switches to reduce the quality of service. Among them, DDoS attacks should be taken seriously, especially the low-rate ones that have a lower attack rate with stronger concealment. In this paper, an architecture named MWD-CFM is proposed to protect against low-rate DDoS attacks in the switch flow tables. Multi-windows work collaboratively to improve detection performance. The feature of flow rules is corrected to do better classification, improving the effectiveness of attack traffic removal. Experiments based on real network topology and datasets are conducted to verify the deployment of MWD-CFM. Results prove that our architecture can greatly reduce the survival time of attack flows and ensure the availability of flow tables for legitimate services.

Sparse network monitoring, a breakthrough technology for cost-effective network-wide monitoring, has garnered significant attention from researchers and network equipment providers. By measuring only a subset of paths and nodes, it leverages the network’s low-rank property to obtain comprehensive monitoring data. However, a previously unnoticed vulnerability called the"global diffusion vulnerability"poses a significant threat to sparse network monitoring. This vulnerability suggests that if a few measurement samples are tainted, the entire network monitoring data can become inaccurate, leading to potential network failures and adverse effects on routing and bandwidth allocation. This paper presents the first exploration of the"global diffusion vulnerability"to launch effective attacks on sparse network monitoring. Sparse monitoring often employs various imputation models to estimate unmeasured data and collects multiple perspectives of network-wide data over extended periods. The challenges in attacking sparse monitoring lie in designing perturbations that can impact all views of network-wide data over time, regardless of the specific imputation models, while remaining unobtrusive. To tackle these challenges, we propose ModelFreeUP, the first perturbation generation algorithm designed for sparse network monitoring. ModelFreeUP creates imputation model-free, universal, and unobtrusive perturbations that exert a significant influence on multiple perspectives of network-wide data over time. Our experiments demonstrate that ModelFreeUP effectively disrupts the sparse monitoring process, causing substantial deviations in the network-wide monitoring data at a relatively low attack cost. Furthermore, when the manipulated monitoring data is used for downstream routing tasks, it triggers 100% Maximum Link Utilization in the Abilene network, indicating network congestion or failure. By shedding light on these critical mismeasurement issues, our work emphasizes the need for robust countermeasures against adversarial attacks in the network monitoring domain.

Bioactive peptides have emerged as promising therapeutic agents in drug development. Recently, several computational approaches have been proposed for the identification of specific peptides. However, existing methods often fail to fully capture the short-range interactions among local amino acid residues and the long-range dependencies introduced by critical residues across the peptide sequence. Moreover, most current predictors are designed for a single type of peptide, limiting their broader applicability. To address these limitations, we propose a deep learning-based computational framework, termed BioSparse, which incorporates a dual-path sparse attention module to accurately identify multiple classes of bioactive peptides. Experiments conducted on six benchmark antimicrobial peptide (AMP) datasets demonstrate that BioSparse outperforms existing methods. Furthermore, the strong generalizability of our model is confirmed through independent evaluations on five additional bioactive peptide classes, namely antiviral peptides (AVPs), anticancer peptides (ACPs), antifungal peptides (AFPs), antibacterial peptides (ABPs), and neuropeptides (NPs), conducted using ten-fold cross-validation. Therefore, BioSparse serves as an effective computational tool for prioritizing high-confidence peptide candidates and accelerating peptide-based drug discovery.

As deep learning-based website fingerprinting (WF) attacks become increasingly accurate, user privacy faces mounting risks. Existing defenses struggle with the discrete nature of packet direction sequences, rendering gradient-based optimization infeasible and leading to inefficient, heuristic-based perturbation solutions. We propose a novel defense framework that bridges this gap by introducing gradient---aligned offset vectors and a cosine similarity---based reward to evaluate and select perturbation candidates aligned with the gradient direction. We further design a parallel reward computation algorithm to improve efficiency and integrate it into GAPDiS, a universal perturbation generation method that combines gradient guidance with improved tabu search for global optimization. For practical deployment, GAPDiS supports both PT bridge and P4 switch implementations. Experiments on the AWF dataset show that GAPDiS reduces the classification accuracy of WF models from over 98% to below 7% with only 2.56% bandwidth overhead---achieving a 68.1% improvement over state-of-the-art methods.

Time-Sensitive Networking (TSN) is a fundamental technology for enabling deterministic communication in industrial Internet applications. Existing TSN traffic scheduling research primarily focuses on optimizing the scheduling of timetriggered (TT) flows to ensure low-delay transmission for these periodic and time-sensitive flows. However, another critical class of traffic exists in TSN that has received little attention-eventtriggered (ET) flows, such as sensor exception alarms. Unlike TT flows, ET flows exhibit higher time sensitivity and demand rapid response to ensure system security. Despite their importance, ET flows have been largely overlooked in prior studies. A few works only have explored dynamically expanding scheduling windows or sharing time slots to accommodate ET flows. However, these approaches treat ET flows as isolated events, neglecting the causal relationships between them. This oversight results in inefficient and unreliable ET flow transmission. To address this limitation, we propose GP-TSN, an event-triggered traffic scheduling framework based on causal inference. Firstly, GP-TSN introduces a Spatio-Temporal Graph Attention-based Hawkes Process (SGATPP) to model and mine event relationships, enabling the inference of future ET flows. Secondly, GP-TSN employs a geometric abstraction-based scheduling strategy to allocate slots for the inferred ET flows, ensuring compliance with TT/ET flow constraints. Additionally, a slot recycling mechanism is incorporated to minimize resource waste. By leveraging the causal dependencies among ET flows, GP-TSN enhances scheduling efficiency and improves the reliability of ET flow transmission. Experiments across four datasets demonstrate that SGATPP achieves superior event inference accuracy (F1-score up to 62.25 %). Moreover, under the condition of ensuring the transmission performance of TT flows, GP-TSN increases the number of ET flows transmitted with the minimum end-to-end delay by 1.7 to 9.7 x.

Mobile crowd sensing (MCS) has become a new paradigm for environment sensing. However, the sensing data often face the challenge of missing values, which can impact the performance of subsequent tasks. Although some deep learning-based imputation methods perform well, they still struggle with insufficient training data due to high missing rate and long-term missing data. To address these challenges, we propose a Historical Data-based Diffusion Imputation (HDDI) method. Unlike existing deep learning-based imputation methods, we design a historical data supplement module to match and fuse historical data to supplement the training data. Additionally, we propose a diffusion imputation module that utilizes the supplement training data to achieve high-accuracy imputation even under high missing rate and long-term missing scenarios. We conduct extensive experiments on four public datasets, the results show that our HDDI outperforms baseline methods across four datasets. Particularly, when the data missing rate is 90%, HDDI improves accuracy by 25.15% compared to the best baseline method in the random missing scenario, and by 13.64% in the long-term missing scenario.

Large-scale tensor completion plays a crucial role in data analysis and anomaly detection, with Alternating Least Squares (ALS)-based CANDECOMP/PARAFAC(CP) decomposition being widely adopted due to its convergence properties and computational stability. However, when accelerating ALS computation on GPUs, load imbalance caused by data partitioning significantly affects efficiency. Due to the sparsity and heterogeneity of data, different thread blocks handle varying amounts of non-zero values, leading to suboptimal utilization of computational resources. Moreover, updating factor matrices in ALS involves frequent global memory accesses, where the latency is 100 times higher than that of shared memory. Efficient utilization of shared memory is therefore critical for improving computational performance. To address these challenges, we propose a GPU-optimized ALS framework that incorporates a heat conduction-based load balancing strategy and a shared memory acceleration mechanism. The load balancing strategy dynamically adjusts subtensor partitioning based on the distribution of non-zero values, ensuring balanced workload allocation across GPU resources. Meanwhile, the shared memory acceleration mechanism caches frequently accessed factor matrices and employs element-wise implicit computation, eliminating explicit intermediate matrix storage and thereby reducing memory overhead and global memory access latency. Based on this, a comparison was made with the other three methods on four data sets. While ensuring accuracy, the time and memory usage were greatly reduced, providing a practical solution for efficient tensor completion.

With the swift advancement of the Internet of Things (IoT) and artificial intelligence (AI), various technologies have been integrated into wearable medical health devices, improving users’ awareness of their physical states and enabling the analysis of a greater amount of human data. However, these sensitive pieces of information are prone to tampering or theft during storage and transmission, posing security risks. In this article, we propose a multiattribute sketch secure data sharing scheme for IoT wearable medical devices based on blockchain (MASS). We introduce a multiattribute sketch storage method that stores the encrypted hash of health data transmitted by medical wearable devices on the blockchain. This work also designs a ciphertext-policy attribute-based encryption (CP-ABE) access control mechanism that effectively addresses the secure sharing of data from wearable medical devices among healthcare professionals. Experimental findings indicate that with the rise in the number of medical health data documents, the costs associated with index generation and search time decrease by 55.3% and 10.83%, respectively. Additionally, as the frequency of data access increases, there is a 13.5% reduction in encryption time, and the implementation of multiattribute sketches results in a 24.8% and 11.3% reduction in index generation and search times, respectively.

Unmanned Aerial Vehicles (UAVs) play a significant role in wireless communication because of their high maneuverability and the advantage of forming Line-of-Sight (LoS) links with ground users. In this research, we investigate the trajectory design and resource scheduling problem of UAV relay communication optimization scenarios, considering heterogeneous tasks and QoS (Quality of Service) awareness. First, we model the optimization scenario and transform the problem-solving into a Markov Decision Process (MDP). Next, we propose R-DQN, a robust DQN (Deep Q-Network) algorithm tailored for heterogeneous tasks and QoS-aware UAV relay communication optimization scenarios. R-DQN introduces corresponding mechanisms in the training process, network structure, and sampling method to improve the effective exploration capability of DQN, making it more robust and suitable for the dynamic constrained optimization scenario tackled. Simulations and experimental results show that the proposed R-DQN algorithm has better convergence and global optimization abilities than other algorithms, such as Dueling DQN, Noisy DQN, and DDQN.

In Unmanned Aerial Vehicle Ad Hoc Networks (UANETs), rapid movement of nodes leads to frequent changes in network topology, increasing the risk of packet loss and affecting data transmission. Furthermore, current research on drone clustering lacks security considerations, which reduces the reliability of data transmission. Due to this, improving the stability and reliability of network data transmission in dynamically changing UANETs remains a challenge. In this work, we propose the reputation mechanism DSCR (Dynamically Secure Cluster Routing) Scheme for UANETs, a design for cluster head election in UANETs using the evaluated value and reputation value of drones and forms clusters through this reputation mechanism to avoid malicious nodes from interfering with the clusters to improve the security of UANETs. We apply a residual link survival time-based intra-and inter-cluster algorithm based on residual link survival time for data forwarding of nodes, optimizing the dynamic routing strategy and reducing the packet loss rate of nodes. In addition, reinforcement learning is used in UANETs to achieve cluster decisions based on the current network state and cluster dynamics, effectively improving the stability of clusters. Experiments were conducted on the proposed method to verify its efficiency and stability. Compared to the ICRA, RICR, and DCA algorithms, the stability of the cluster is improved by 11.92%, 28.12%, and 75.2%, respectively, and the packet loss rate reduced by 41.48%, 44.17%, and 55.74%, demonstrating that DSCR is a compelling dynamic routing solution applicable to UANETs.

The Pre-trained Foundation Models (PFMs) based on edge inference reduce communication latency from the cloud server to the user mobile devices, and support different edge nodes to provide adaptive Artificial Intelligence (AI) services to the users. The complex network environment has attracted widespread attention to the communication security of cross-domain AI services. Authentication protocol is an important way to ensure the communication security among entities. However, existing cross-domain authentication protocols have not taken into account the communication delay caused by complex cryptographic operations on the mobile devices. To address these issues, a Cross-Domain Inference Authentication (CDIA) protocol for PFMs is proposed in this paper. The complex cryptographic operations are executed collaboratively by the servers from different domains to realizes decentralized authentication. Besides, the correctness and security of CDIA protocol is proved by formal analysis. The security of CDIA protocol is verified by using the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. The security of negotiated group key is proved by Real-or-Random (RoR) model. Finally, the performance of CDIA protocol is evaluated to show its efficiency. Compared with related protocols, the computing cost and communication overhead of CDIA protocol are at least 52.38% and 11.9%, respectively.

With the development of the Internet of Things (IoT) towards supporting next-generation security and distributed applications, the integration of quantum communication technology provides a promising way to enhance data security, computing power, and perception accuracy. The large-scale interconnection of quantum nodes enables the formation of quantum enhanced IoT networks, with high fidelity entanglement serving as the cornerstone of secure and efficient communication. However, the inherent limitations of quantum resources and channel noise pose significant challenges to routing and resource management. This article constructs the quantum path selection problem as a multi criteria path selection problem, designs a weight scoring function to obtain the optimal path in the current network for each request, and proposes a two-stage routing and allocation strategy TPROA, namely the idea of "guarantee before utilization", aiming to maximize network throughput and improve resource utilization in resource constrained network environments. The simulation results show that compared with traditional methods, TPROA significantly improves quantum resource utilization and network throughput. The proposed method lays the foundation for scalable quantum IoT integration.

Mobile Edge Computing (MEC) pushes computing resources from the network center to the network edge to provide efficient and reliable computing services. However, due to the mobility and diversity of mobile users (MUs), edge servers (ESs) are likely to be overloaded, which leads to a rapid decline in the quality of service provided by the MEC system. This paper introduces unmanned aerial vehicles (UAVs) to solve the problem of ES overload. This paper presents an effective Multi-UAV Collaborative Decision-Making System (MCDS) tailored to optimize task offloading and resource allocation in MEC networks. The proposed system seeks to reduce task computing delays and energy consumption while guaranteeing timely task completion and compliance with resource constraints. We design a distributed two-stage optimization algorithm to jointly optimize the task offloading decision and resource allocation of the collaborative computing system (UAVs, ESs, and MUs). To address the flight cooperation problem of multi-UAVs, we also proposed a task-aware scheduling algorithm for multi-UAVs. In addition, we conducted a large number of simulation experiments. Experimental results show that our two-stage optimization scheme is lower than other benchmark algorithms in terms of task completion delay, energy consumption and total cost of objective function. Specifically, compared with seven baseline algorithms under three different system setting scenarios, our algorithm reduces the total cost by an average of 38.79%, the energy consumption by an average of 40.70%, and the delay by an average of 15.67%.

Mobile Crowd Sensing (MCS) is a new sensing paradigm that uses portable intelligent mobile devices (smartphones, wearable devices, etc.) carried by users to capture dynamic changes of social and urban information. Thanks to the ubiquity of mobile devices, MCS is able to collect data efficiently and achieve some valuable practical applications. Research reviews on MCS mostly focus on a single direction such as task assignment or incentive mechanism. This paper comprehensively reviews the research work on task allocation, incentive mechanisms, quality control and privacy protection in recent years. First, the system architecture and workflow of MCS are described and representative applications of MCS are illustrated. Furthermore, taking MCS as the target object, this paper expounds and discusses key issues such as task allocation, incentive mechanisms, quality control, and privacy protection, and introduces the latest research results in these fields. Subsequently, we provide a systematic review of the datasets reported in the literature. Ultimately, we point out the limitations of existing studies and look forward to future research directions to provide valuable references for related researchers.

Data recovery is a fundamental task for sparse network monitoring with a significant impact on many downstream tasks, such as congestion control, network capacity planning, and traffic engineering. To better capture the network dynamically and quickly respond to network failure, network monitoring systems take finer temporal granularity to collect data to form a real-time view of the network. Unfortunately, the current data recovery for network monitoring relies on matrix and tensor completion algorithms, which fail to satisfy the requirements of real-time recovery. To combat this problem, in this paper, we propose Real-TimeEvent-based Streaming NetworkMonitoring Data Recovery (EventMon) that achieves ultra-low latency data recovery in network measurement data streams. Specifically, we leverage a mixture of offline and online architecture, with the offline component learns to capture the historical spatial-temporal correlation while the online component is a novel streaming encoder that updates factor matrices incrementally. To enable training our event-level stream processing module, we devise a novel Stream2Batch algorithm to enable mini-batch style training and ensure the encoder generates the same results with one-by-one stream processing. We conduct extensive experiments on three network monitoring datasets and our evaluation and analysis of the experimental results demonstrate shows that the proposed method outperforms existing schemes in terms of accuracy, inference latency, and high processing throughput.

The Low-Rate Denial of Service (LDoS) attack poses a significant threat to Internet services. Exploiting vulnerabilities in adaptive mechanisms embedded within network protocols, LDoS attacks are covert and exhibit legal behavior, making defense challenging. Existing LDoS attack solutions cannot perform real-time LDoS attack defense at line speed. With the emergence of P4, users can program the per-packet processing logic of the P4 switch, which offers us the chance to propose PLUTO, the first data plane-aware LDoS attack defense system built upon the P4 switch, possessing line-speed execution capacity. To meet the resource constraints of the P4 switch, we propose the time window-based pre-inference strategy to detect LDoS attacks and the time-limited per-flow state management to filter the LDoS attack flows. For the practical deployment, we develop the P4 Function Tool to extend the P4 primitives for more function operations. We also adopt an encoding-based mapping method to deploy the pre-inference model. Furthermore, we develop the async-updated hash table for quickly filtering LDoS attack flows. Compared with the baseline, PLUTO reduces the equal error rate (EER) by 27.96% and the average mitigation response time by 12.749 s, increasing the AUC by 1.83%, the F1 Score by 7.27%, and the Recall by 9.58%.

Detailed system operations are recorded in logs. To ensure system reliability, developers can detect system anomalies through log anomaly detection. Log parsing, which converts semi-structured log messages into structured data, is a crucial step in log anomaly detection and advanced program analysis and verification. Despite the availability of various log parsing tools, they generally suffer from low parsing accuracy and slow efficiency due to the ignorance of variable characteristics and the use of costly pairwise comparison methods. In this paper, we propose a TCMS framework to parse logs, consisting of two main technologies. First, by studying 16 public log datasets, we find that most log variable tokens are structured variable tokens. Based on this discovery, we propose a token conversion algorithm to improve parsing accuracy. This algorithm converts the changed parts in structured variable tokens into wildcards (‘$< *> $’), preventing these tokens from being directly identified as constant tokens. Second, to improve efficiency, we propose the LogMLCS algorithm, which intelligently constructs a graph to facilitate the extraction of common parts from multiple log messages at once, instead of using pairwise comparisons. Comprehensive experiments conducted on 16 log datasets reveal that our TCMS outperforms seven other parsing methods, achieving the highest parsing accuracy at the fastest speed. Furthermore, experimental results from running a log anomaly detection algorithm in conjunction with different log parsing methods demonstrate that TCMS significantly boosts detection accuracy. For instance, on the OpenStack dataset, our TCMS-facilitated log anomaly detection algorithm achieves a perfect F1-score, precision, and recall of 100% each, surpassing the best peer method by 32.2, 0.8, and 19.5 percentage points, respectively.

Subsequently, by executing various attacks on benchmark datasets such as MNIST, we construct Federated Learning Malicious Parameter Identification (FLMPID) dataset to enable malicious client detection. Building on this dataset, we propose FORTRESS (Federated POisoning-Resistance Defense via Dual-Domain Distance and TRust AssESSment), a framework designed to detect and mitigate malicious updates from clients. FORTRESS employs a unique encoder-decoder architecture. The encoder utilizes dual-domain distance metrics on weights and gradients to extract hidden representations, while the decoder leverages Actor-Critic (AC) reinforcement learning for trust assessment. We evaluated FORTRESS under multiple attack scenarios and demonstrated its defense effectiveness, making it a promising solution for enhancing the security of FL systems.

The Payment Channel Network (PCN) provides an off-chain payment model to alleviate the problem of limited blockchain throughput. However, PCNs typically rely on third-party regulators to monitor the blockchain states continuously to prevent honest users’ funds from being lost, which increases user overhead and compromises transaction privacy. To overcome these challenges, we propose a highly compatible bidirectional Self-Regulatory Virtual Channel (SRVC) that eliminates third-party reliance while enhancing privacy and reducing overhead. By leveraging absolute time locks, we establish a self-regulatory mechanism where users only need to monitor the blockchain online for a short time window to ensure the security of their funds, effectively removing the need for third-party monitoring. Additionally, we introduce a novel payment paradigm and a punishment mechanism based on adaptor signatures across underlying channels to ensure transaction privacy and security, while also reducing transaction overhead. We formalize the security properties of SRVC as an ideal functionality and prove that SRVC is secure in the Universal Composability framework. Performance analysis demonstrates that, compared to other virtual channel protocols based on Lightning channels, SRVC reduces communication overhead by approximately 46.8% in the open operation and 62.9% in the update operation. In high-concurrency payment scenarios, SRVC further decreases communication overhead by about 57.8% and reduces the number of transactions by around 52.8% compared to Sleepy Channel’s Virtual Channel (SCVC) implementation.

CNNs have demonstrated superior performance in medical image segmentation. To overcome the limitation of only using local receptive field, previous work has attempted to integrate Transformers into convolutional network components such as encoders, decoders, or skip connections. However, these methods can only establish long-distance dependencies for some specific patterns and usually neglect the loss of fine-grained details during downsampling in multi-scale feature extraction. To address the issues, we present a novel hybrid Transformer network called FocalTransNet. Specifically, we construct a focal-enhanced (FE) Transformer module by introducing dense cross-connections into a CNN-Transformer dual-path structure and deploy the FE Transformer throughout the entire encoder. Different from existing hybrid networks that employ embedding or stacking strategies, the proposed model allows for a comprehensive extraction and deep fusion of both local and global features at different scales. Besides, we propose a symmetric patch merging (SPM) module for downsampling, which can retain the fine-grained details by establishing a specific information compensation mechanism. We evaluated the proposed method on four different medical image segmentation benchmarks. The proposed method outperforms previous state-of-the-art convolutional networks, Transformers, and hybrid networks. The code for FocalTransNet is publicly available at https://github.com/nemanjajoe/FocalTransNet.

Exploring simple and efficient computational methods for drug repositioning has emerged as a popular and compelling topic in the realm of comprehensive drug development. The crux of this technology lies in identifying potential drug-disease associations, which can effectively mitigate the burdens caused by the exorbitant costs and lengthy periods of conventional drugs development. However, existing computational drug repositioning methods continue to encounter challenges in accurately predicting associations between drugs and diseases. In this paper, we propose a Multi-view Representation Learning method (MRLHGNN) with Heterogeneous Graph Neural Network for drug repositioning. This method is based on a collection of data from multiple biological entities associated with drugs or diseases. It consists of a view-specific feature aggregation module with meta-paths and auto multi-view fusion encoder. To better utilize local structural and semantic information from specific views in heterogeneous graph, MRLHGNN employs a feature aggregation model with variable-length meta-paths to expand the local receptive field. Additionally, it utilizes a transformer based semantic aggregation module to aggregate semantic features across different view-specific graphs. Finally, potential drug-disease associations are obtained through a multi-view fusion decoder with an attention mechanism. Cross-validation experiments demonstrate the effectiveness and interpretability of the MRLHGNN in comparison to nine state-of-the-art approaches. Case studies further reveal that MRLHGNN can serve as a powerful tool for drug repositioning.

Sparse data gathering has become a promising solution for reducing measurement costs by leveraging the inherent sparsity of data. However, most existing approaches rely on low-dimensional models such as compressive sensing or matrix completion, which are limited in capturing complex high-dimensional structures. To overcome these limitations, we proposeTensorMon, a novel tensor-based sparse data gathering framework that introduces a cuboid sampling strategy to more effectively exploit multidimensional correlations. Unlike traditional entry-based or tube-based sampling, TensorMon introduces the innovative concept ofcuboid sampling. We further develop a lightweight sampling scheduling algorithm and a non-iterative inference algorithm to ensure efficient measurement planning and accurate reconstruction of unmeasured data. Theoretical analysis establishes a new performance bound for our sampling strategy, which is significantly lower than those in existing literature. To validate our theoretical findings, we conduct extensive experiments on four real-world datasets: two network monitoring datasets, a city-scale crowd flow dataset, and a road traffic speed dataset. Experimental results demonstrate that TensorMon achieves substantial reductions in measurement cost, delivers high inference accuracy, and ensures rapid data recovery, highlighting its effectiveness and practicality across diverse application scenarios.

As a form of distributed machine learning, Federated learning allows users to complete training without sharing local data, thereby protecting user privacy to a certain extent. However, the gradients uploaded by users during the training process can still leak user privacy. Additionally, malicious or lazy cloud servers may tamper with or forge the aggregated results before returning them to users, causing significant losses to the entire training process. Existing solutions focus on security issues, but most privacy protection schemes based on complex cryptographic primitives require high computational power and communication bandwidth. Moreover, to verify the aggregated results, each user must compute proofs, which imposes an additional computational burden on users. Therefore, designing more efficient and lightweight solutions that ensure security while adapting to resource-constrained scenarios is necessary. An efficient group-based scheme for privacy preservation and verification outsourcing in federated learning, referred to as GPVO-FL, is introduced in this work. Specifically, we design a lightweight privacy protection mechanism based on group structure and masking techniques to protect user gradients. In addition, we design an outsourced verification mechanism that offloads the verification process to edge servers, thus reducing the computational burden on users. A detailed security and experimental analysis demonstrates the security and efficiency of our scheme.

Internet of Things (IoT) networks have penetrated our daily life and industries. However, IoT devices are typically small-sized with constrained storage. Distributed storage systems are emerging as promising solutions to tackle such challenges. InterPlanetary File System (IPFS) is a desired framework enabling IoT devices to upload its data to a distributed cloud while returning a hash-ID for downloading and file-sharing purposes. Nevertheless, IPFS lacks of robust security design and is vulnerable to security threats such as data tampering, and data leakage. In particular, whenever device A’s file hash-ID is shared to an arbitrary device B, device A will fully lose the control over file. In other words, device B could further share it to anyone without device A’s agreements. To conquer the challenge, we propose a comprehensive design for securing the distributed IoT storage systems, named StorSec. Specifically, we design a new heterogeneous framework using an improved attribute encryption algorithm to eliminate the single-point performance bottleneck problem, which not only realizes fine-grained access control and ensures the security of data during transmission, but also improves the performance of key generation. Secondly, we design an anomaly detection algorithm, which is based on hashchain technology and combines the user privacy metadata stored on the blockchain to complete the verification process, effectively protecting the file hash identifier, ensuring access control to the file, and thus providing protection for the security and integrity of data storage. Furthermore, we design an auditing algorithm that helps the system in tracking malicious entities. Ultimately, the security and efficiency of the proposed scheme are evaluated by both security analysis and experimental results.

Network distance measurement is crucial for evaluating network performance, attracting significant research attention. However, conducting measurements for the entire network is exceedingly expensive and time-consuming, making the reduction of network distance measurement costs a top priority. The tensor completion method efficiently reduces measurement costs by utilizing a small amount of measured data to estimate the entire network’s distance data. Unfortunately, current tensor completion methods still suffer from issues such as complex sample selection, high measurement overhead, slow recovery, and low inference accuracy. To address the aforementioned challenges, we present an online network-wide distance measurement scheme. In this approach, continuous distance data are structured into sliding-window-based tensors. Our method incorporates a lightweight sample selection algorithm with a lowest sampling bound and a rapid, accurate unmeasured data inference algorithm. We have conducted extensive experiments using four real network distance datasets and two citywide crowd flow datasets. The empirical evaluations demonstrate the effectiveness of our approach, particularly in reducing measurement costs and enhancing data recovery accuracy.

The surge in demand for latency-sensitive services has propelled network distance measurement to the forefront of networking research. Utilizing the low-rank structure of full network data, the tensor completion method can efficiently estimate network distance from partially sampled distance data measured from a small set of node pairs. However, its performance is affected by sampling algorithm limitations, including unreliability and high overhead in dynamic networks. To tackle these challenges, we propose tube-based sampling as an alternative to point-based sampling, utilizing a partition-based algorithm to incorporate randomness for improved reliability. Additionally, we introduce a Tube Length Identification Algorithm to dynamically adjust tube length based on network status, balancing scheduling overhead reduction with estimation accuracy. Experimental results on three real network distance datasets, compared against 13 baseline algorithms, demonstrate the high accuracy and low scheduling overhead of our approach.

Network traffic matrices often suffer from incompleteness and sparsity due to various factors, including network device policies and system limitations. The incompleteness can undermine the reliability and accuracy of network traffic monitoring services, negatively impacting downstream tasks such as network planning and fault diagnosis. Our focus is on network traffic data recovery, intending to infer missing traffic data from partial measurements accurately. Although tensor completion algorithms are quite effective in recovering traffic data, existing models often overlook cross-domain traffic relationships and fail to account for the order and distribution of traffic, leading to reduced recovery accuracy. To overcome these limitations, we propose a new contrastive tensor completion model that utilizes pairwise joint embedding. This model employs innovative techniques, including a cross-domain embedding module to avoid information homogeneity and enhance model expressiveness, a contrastive module to preserve the order and distribution of traffic volumes, and an injective interaction module to map entry embeddings into the numerical space, ensuring convergence and retaining the original numerical distribution. Experiments on three real-world network traffic datasets show that our model significantly reduces the error in missing traffic data recovery compared to other existing models while maintaining traffic order and distribution.

Remote sensing imagery plays a crucial role in areas such as environmental monitoring and urban planning. However, due to fragile communication links, limited bandwidth and harsh wireless environments, transmitting data from remote locations to ground applications faces the dilemma of high bit-error rates, which have a poor impact on downstream missions. Semantic communication is a feasible solution that transmits only the semantic features of the raw data extracted using neural networks. Although effective, existing semantic communication methods cannot cope with high compression rate requirements and complex communication environments. Therefore, in this paper, an effective image compression and transmission framework ASE-JSCC is proposed. To minimize the transmitted data, we design a semantic extraction module and an important feature selection module to efficiently extract, select, and compress critical semantic features required for downstream tasks. To improve the communication robustness of the model in complex environments affected by variable channels, we optimize the source-channel joint coding technique by randomly adding noise with different types and sizes. Finally, we deploy ASE-JSCC to the scene classification task of remote sensing images and conduct extensive experiments on four real datasets, achieving classification accuracy of 84.29%--88.62% under 384 times compression ratio, verifying the excellent performance of the proposed framework.

In recent years, the refinements in industrial processes and the increasing complexity of managing privacy-sensitive data from Industrial Internet of Things (IIoT) devices, have highlighted the critical need for secure, robust, and adaptive data management solutions. In this work, we propose a smart contract-assisted access control scheme for IIoT, which employs the Attribute-Based Access Control (ABAC) model to set access permissions for different industrial components. We defined a storage model and data format for private data through the design and deployment of smart contracts to manage system operations and access policies. In addition, the bloom filter component is deployed to optimize the efficiency of contract management and system performance. Experimental results show that in the real-world simulations, Hyper-IIoT shows well-controlled contract execution time, stable system throughput and fast consensus process, and is capable of handling high throughput and effective consensus in distributed systems even in large-scale request scenarios.

Accurately detecting traffic anomalies becomes increasingly crucial in network management. Algorithms that model the traffic data as a matrix suffers from low detection accuracy, while the work using the tensor model often assumes the tensor is regular without considering that network nodes may dynamically join in or leave, which will fail in a practical network with the change of node set as a result of mobility and churn behaviors. We propose a novel Tensor Recovery scheme in a Dynamic Network (TRDN) with traffic data modeled as a practical irregular tensor for accurate anomaly detection. To take advantage of correlations among small tensors, each formed with a short time duration to capture more hidden information in the data for higher detection accuracy, we propose several novel techniques: 1) a new joint tensor factorization model to capture the characteristic shared by the common nodes of small tensors, 2) a tensor partition algorithm to identify the data that can be applied to train the shared parameters efficiently, and 3) a bar-based algorithm that partitions nodes into the minimum number of no-overlapping subsets to form the shared tensor model. Extensive experiments on two Internet traffic data sets, Abilene and GÈANT, demonstrate the effectiveness of the proposed TRDN.

In the era of big data and growing privacy concerns, Federated Learning (FL) has emerged as a promising solution for collaborative model training while preserving user data privacy. However, FL faces challenges such as inconsistent data quality and frequent client dropouts, which hinder its performance. To address these challenges, we propose FedHPM, a novel framework that leverages the predictive capabilities of Long Short-Term Memory (LSTM) networks. FedHPM incorporates an intelligent client selection mechanism that utilizes LSTM-based analysis of historical data to forecast clients’ potential data quality and dropout likelihood. This mechanism enables efficient selection of the top best-performing clients for participation in each round of global model training. By mitigating the impact of dropouts and optimizing the composition of participating clients, FedHPM significantly improves both the convergence speed and final performance of the global model. Through extensive empirical evaluations, we demonstrate the superior performance of FedHPM in enhancing FL stability, accelerating the convergence rate, and maintaining model accuracy. The results highlight the effectiveness of our framework in addressing the challenges associated with data quality and dropouts, thereby advancing the field of federated learning.

Abstract Log blockchain can be used to ensure the integrity of log data. However, current methods are facing the problems of throughput mismatch and rough audit granularity. Packaging multiple logs to generate integrity proofs improves throughput but reduces audit granularity, and the auditor can only locate the tampering log packet instead of specific records. This paper proposes CFAuditChain, an audit blockchain based on a cuckoo filter, where the proof of existence (PoE) of the single log is calculated while calculating the integrity proof of packet logs. The PoE is saved in the cuckoo filter and stored using blockchain for immutability. Therefore, the auditor can verify the credibility of each log based on the filter when the integrity proof of a log packet does not match. The theoretical analysis and experimental results show that CFAuditChain provides the granularity of audit logs down to the records level at an acceptable cost.

Machine learning has demonstrated tremendous potential in solving real-world problems. However, with the exponential growth of data amount and the increase of model complexity, the processing efficiency of machine learning declines rapidly. Meanwhile, the emergence of quantum computing has given rise to quantum machine learning, which relies on superposition and entanglement, exhibiting exponential optimisation compared to traditional machine learning. Therefore, in the paper, we survey the basic concepts, algorithms, applications and challenges of quantum machine learning. Concretely, we first review the basic concepts of quantum computing including qubit, quantum gates, quantum entanglement, etc.. Secondly, we in-depth discuss 5 quantum machine learning algorithms of quantum support vector machine, quantum neural network, quantum k-nearest neighbour, quantum principal component analysis and quantum k-Means algorithm. Thirdly, we conduct discussions on the applications of quantum machine learning in image recognition, drug efficacy prediction and cybersecurity. Finally, we summarise the challenges of quantum machine learning consisting of algorithm design, hardware limitations, data encoding, quantum landscapes, noise and decoherence.

Federated learning (FL), as an effective method to solve the problem of “data island”, has become one of the hot and widespread concern topics in recent years. However, with the using of FL technology in the practical applications, an increasing number of FL tasks make the training management be more complex and the trade-off of multi-task becomes difficult. To overcome this weakness, this work proposes a privacy-preserving FL framework with multi-tasks using partitioned blockchain, which can run several different FL tasks by multiple requesters. First, a temporary committee is formed for an FL task to facilitating visualization, organization and management of security aggregation. Second, the proposed framework combines Paillier homomorphic encryption with Pearson correlation coefficient to protect users' privacy and ensure the accuracy of global model. Finally, a new blockchain-based reward method is presented to inspire participants to share their valuable data. The experimental results show that the global model accuracy of our proposed framework is able to reach 98.43%. Obviously, the proposed framework is more suitable for practical application environment, especially in industrial application field.

Recommendations for ride-hailing zones are crucial for matching drivers with passengers efficiently, improving mobility, and managing traffic effectively. However, current recommendation methods based on centralized machine learning suffer from privacy concerns due to the need for data sharing and aggregation. Furthermore, the lack of personalized results tailored to individual regions or drivers within a single architecture often leads to spatial supply imbalances in ride-hailing services. To address these challenges, we propose in this work federated geo-aware matrix factorization (FedGeoLMF), a framework for vehicular-cloud collaborative federated learning that identifies vehicles with similar passenger patterns, aiming to enhance the accuracy and efficiency of recommendations by improving local nodes for federated learning. At local nodes, federated matrix factorization algorithms based on historical trajectory data of moving vehicles and fused geographical information to achieve local learning and recommendations are designed. Additionally, we introduce a communication mechanism tailored for federated learning models exchanged between servers and mobile vehicles, employing the named data networking (NDN) protocol that streamlines parameter uploading and downloading processes, unveiling akin patterns within local federated matrix decomposition outcomes by harnessing the NDN routing algorithm, delivering personalized collaborative recommendations while upholding the confidentiality of user-sensitive data. Finally, we design an online recommendation method based on driver location and reachability matrices to recommend optimal ride-hailing zones for drivers in different locations. Experimental results demonstrate that the proposed method outperforms current baseline models in terms of accuracy and training efficiency and can efficiently provide personalized ride recommendations while ensuring privacy in the ride-hailing service domain.

In recent years, open mobile social networks focused on socializing and dating purposes have gained widespread popularity, such as Soul, Tinder, Momo, and Tantan, among several others. These applications permit users to post, comment, and send private messages to other users without their consent, making communication accessible. However, this low-entry communication approach has also increased malicious user attacks. We delve into a comprehensive analysis of malicious accounts in open socializing and dating applications, revealing that the existing methods overlook hidden malicious signals within the user text-related information, thus resulting in poor detection performance. For such, we propose GraphTAM, a novel graph- and text-based multihead attention fusion network model for detecting such malicious accounts, consisting of modules that effectively combine nontext-related and text-related information, enhancing the accuracy and performance of detecting malicious accounts. We employ graph convolutional networks (GCNs) for nontext-related information to extract advanced representations of users, incorporating their attribute and social relationship features. Regarding text-related information, we employ a multihead attention model to identify suspicious patterns in users’ posted articles, comments, and relevant behavioral statistics, so finally, we merge the advanced representations of nontext-related and text-related information using a multilayer perceptron to determine the maliciousness of an account. Data sets collected from SLink are utilized for the experimental evaluation and to compare the performance of the proposed model with the several state of the art algorithms. Experimental results show significant advantages in malicious account detection, where the F1 score achieves over 0.9, outperforming the existing methods that range between 0.6 and 0.85. Furthermore, the comparative experiments substantiate the critical role of text-related information in detecting malicious accounts in open socializing and dating applications.

With the increasing integration of location-based services (LBSs) into various societal domains, location privacy preservation has emerged as a pivotal concern. In most continuous LBS privacy protection approaches, the user needs to send a query to an untrusted location service provider (LSP) to request the corresponding query results, and these results are discarded immediately after being used. This leads to similar queries in the future having to be sent to the LSP again, which increases the risk of privacy leakage when facing the LSP. To solve these issues, caching techniques are typically used to provide answers to users’ future queries. However, minimizing the interaction with the LSP is a challenge. Here, we propose a trajectory privacy-preserving scheme based on a transition matrix and caching (TMC) scheme for continuous LBS in the Industrial Internet of Things (IIoT). It employs multilevel caching to reduce the risk of exposing sensitive information to untrusted entities. We designed a transition matrix to predict the user’s next query location and simplify the computation complexity. We designed a cloaking set generation algorithm by considering transition entropy, location prediction, data freshness, and cache contribution degree to enhance user location privacy and improve the cache hit rate. The security analysis demonstrates how the TMC scheme resists attacks from both internal and external entities and ensures robustness in trajectory privacy. The experimental results show that the proposed TMC scheme can provide a higher level of privacy protection and lower system overhead compared to several previous schemes.

As a typical privacy-aware machine learning paradigm, federated learning (FL) provides facilities to individually train edge clients with their private data and aggregate the central global model. In this way, privacy leakage can be prevented. Massive communication overhead caused by exchanging updated weights between clients and the server is one of the main obstacles in this strategy. Prior work advocates compressing the weights by employing quantization, gradient sparsification, and knowledge distillation approaches. However, most of them cannot be readily applied to secure aggregation in privacy-aware FL. Some research has made great progress in directly utilizing benchmark secure aggregation protocols on top of the non-privacy-aware FL. Graph-based and gradient-based sparsification has been widely adopted in previous studies. However, the results of reducing communication costs are still unsatisfactory. In this paper, we present a novel communication-efficient privacy-ware FL algorithm from a distinct perspective. We design a new Two-Phase Sparsification with Secure Aggregation (TPSSA) algorithm. In the subnetwork phase, we identify sparse subnetworks by freezing the initial random weights in sufficiently overparametrized networks. All edge clients collaboratively train to discover their subnetwork inside a dense randomly weighted neural network. Then the server aggregates to compute the global model. In the gradient phase, for each pair of edge clients, we introduce pairwise multiplicative random masks to identify the sparsification pattern. Then updates from surviving clients can be correctly cancelled out during the aggregation process in the server. Theoretical analysis reveals convergence, privacy and performance guarantee. We show improvements in accuracy, communication, and computation over traditional and sparsified secure aggregation benchmarks on two real-world datasets.

Cloud computing and the Internet of Things (IoT) provide robust technological support for the development of image retrieval services. Specifically, images are highly sensitive and private data in e-healthcare and security surveillance. Existing retrieval schemes often do not strike a good balance between privacy protection and retrieval performance. It makes data vulnerable to illegal attacks and leads to high retrieval costs, thereby affecting the overall quality and usability of the system. To address these issues, this paper introduces a fine-grained secure approximate image retrieval (FSAIR) scheme for mobile cloud computing. Our approach implements a multi-verification architecture that provides precise identity control and an untraceable strategy. Furthermore, FSAIR constructs a flexible and secure hierarchical structure to support the efficient retrieval of large-scale high-dimensional data. By introducing Bloom filters to replace index nodes, FSAIR ensures system efficiency and employs bit-pattern encoding descriptors to search approximate data without violating privacy. We show that the proposed scheme protects data under different threat modes through security analysis. We also demonstrate the feasibility and superiority of the proposed solution through experimental evaluation, using the Caltech-256 dataset.

Biometric and password-based two-factor authentication has received attention from the community over the past decades because of its simplicity, portability, and robustness. In wireless body area networks (WBANs), dozens of authentication and key agreement schemes have been proposed. Despite well-studied security issues, preserving user privacy in these schemes is still challenging. In this work, we propose biometric-based authentication and key agreement (BAKA), a scheme based on a fuzzy extractor for WBAN, where a novel biometric and password-based authentication algorithm is proposed by using a fuzzy extractor to achieve anonymous identity authentication, a privacy-preserving key agreement algorithm for session key security, and finally, we deploy blockchain to record biometric information using its noncomparability and distributed storage to protect users’ privacy to a large extent. BAKA is secure as per formal security proof and informal security analysis, symmetric encryption is utilized to reduce computation overhead to improve the efficiency of BAKA, where security is not compromised. Extensive experiments to validate the performance of BAKA are performed, and the results demonstrate the security efficacy proposed.

With the vigorous development of the Internet of Things (IoT), mobile users need to access data from other domains in edge computing. To achieve secure data sharing, mobile users first need to be authenticated by servers from different domains and then negotiate session keys among them. However, traditional schemes cannot solve cross-domain identity authentication and key agreement problems well due to the limited computational resources of IoT devices. In this work, we propose a Blockchain-based Cross-domain Authentication scheme for Edge computing, namely BCAE. First, to achieve secure identity verification, we design a novel cross-domain mutual identity authentication algorithm based on digital certificates and digital signatures. Next, to improve efficiency, we utilize the blockchain to share information among different domains to reduce the computation overhead. To realize quick key agreement, we apply the elliptic curve cryptography technique to design a lightweight key agreement algorithm and obtain secure session keys. Extensive experiments conducted on an actual smart healthcare issue to validate the performance of BCAE and formal security analysis confirmed the potential of the proposed work.

The development of the Internet of Things (IoT), Big Data, and deep learning technologies has brought convenience to people’s lives. As personal privacy data protection laws and regulations tighten, the cost of acquiring high-quality annotated data from vast IoT datasets has significantly increased, resulting in prevalent issues such as data acquisition challenges and label noise in training data. In this work, we focus on the demand for privacy protection and trustworthy sharing of IoT data, and propose a method for addressing data bias in IoT through federated learning and blockchain by utilizing the theory of local intrinsic dimension (LID), incorporating committee consensus to achieve noise label identification and correction at the data level, reducing information loss in the training data. Additionally, it performs screening of low-quality local model updates at the model level, leveraging blockchain technology that addresses the single point of failure issues in traditional federated learning, ensuring the performance and security of the federated learning models. Analysis, proof of convergence, and experimentations on the proposed framework demonstrate good security and robustness in noisy environments, effectively addressing data bias in intelligent IoT settings. In scenarios with a noise level of 0.3, 0.6, and 0.9, the average model accuracy improved respectively by 7.75%, 7.30%, and 14.04% compared to FedAvg. Similarly, when compared to FedCorr, the average improvement in model accuracy is 5.19%, 3.63%, and 8.74% respectively. Moreover, the training time remains within an acceptable range for all cases.

Network telemetry, characterized by its efficient push model and high-performance communication protocol (gRPC), offers a new avenue for collecting fine-grained real-time data. Despite its advantages, existing network telemetry systems lack a theoretical basis for setting measurement frequency, struggle to capture informative samples, and face challenges in setting a uniform frequency for multi-metric monitoring. We introduce FineMon, an innovative adaptive network telemetry scheme for precise, fine-grained, multi-metric data monitoring. FineMon leverages a novel Two-sided Frequency Adjustment (TFA) to dynamically adjust the measurement frequency on the Network Management System (NMS) and infrastructure sides. On the NMS side, we provide a theoretical basis for frequency determination, drawing on changes in the rank of multi-metric data to minimize monitoring overhead. On the infrastructure side, we adjust the frequency in real-time to capture significant data fluctuations. We propose a robust Enhanced-Subspace-based Tensor Completion (ESTC) to ensure accurate recovery of fine-grained data, even with noise or outliers. Through extensive experimentation with three real datasets, we demonstrate FineMon's superiority over existing schemes in reduced measurement overhead, enhanced accuracy, and effective capture of intricate temporal features.

Deep-Learning-as-a-service (DLaaS) has received increasing attention due to its novelty as a diagram for deploying deep learning techniques. However, DLaaS faces performance and security issues that urgently need to be addressed. Given the limited computation resources and concern of benefits, Quality-of-Service (QoS) metrics should be revised to optimize the performance and reliability of distributed DLaaS systems. New users and services dynamically and continuously join and leave such a system, resulting in cold start issues, and additionally, the increasing demand for robust network connections requires the model to evaluate the uncertainty. To address such performance problems, we propose in this article a deep learning-based model called embedding enhanced probability neural network, in which information is extracted from inside the graph structure and then estimated the mean and variance values for the prediction distribution. The adversarial attack is a severe threat to model security under DLaaS. Due to such, the service recommender system's vulnerability is tackled, and adversarial training with uncertainty-aware loss to protect the model in noisy and adversarial environments is investigated and proposed. Extensive experiments on a large-scale real-world QoS dataset are conducted, and comprehensive analysis verifies the robustness and effectiveness of the proposed model.

Drug target interaction prediction is a crucial stage in drug discovery. However, brute-force search over a compound database is financially infeasible. We have witnessed the increasing measured drug-target interactions records in recent years, and the rich drug/protein-related information allows the usage of graph machine learning. Despite the advances in deep learning-enabled drug-target interaction, there are still open challenges: (1) rich and complex relationship between drugs and proteins can be explored; (2) the intermediate node is not calibrated in the heterogeneous graph. To tackle with above issues, this paper proposed a framework named DSG-DTI. Specifically, DSG-DTI has the heterogeneous graph autoencoder and heterogeneous attention network-based Matrix Completion. Our framework ensures that the known types of nodes (e.g., drug, target, side effects, diseases) are precisely embedded into high-dimensional space with our pretraining skills. Also, the attention-based heterogeneous graph-based matrix completion achieves highly competitive results via effective long-range dependencies extraction. We verify our model on two public benchmarks. The result of two publicly available benchmark application programs show that the proposed scheme effectively predicts drug-target interactions and can generalize to newly registered drugs and targets with slight performance degradation, outperforming the best accuracy compared with other baselines.

Outdoor images taken in haze usually exhibit contrast reduction, color distortion, and detail loss. Removing the haze from a given image is a tough issue owing to its highly ill-posed property. To restore the haze-free image effectively, we develop an unsupervised dehazing method using patch-line and fuzzy clustering-line priors in this paper. The method obtains the dehazed image by inversely solving the atmospheric scattering model, which involves in estimating two key parameters, including atmospheric light and scene transmission. First, the orientation of atmospheric light is achieved by using a patch-line prior. Then, a quadtree subspace hierarchical searching scheme is designed to get the magnitude by calculating the differences between the mean and variance of each component for local regions. Besides, a fuzzy clustering-line prior combined with a guided filtering is proposed to estimate the scene transmission for each pixel. The proposed method can obtain the dehazed image directly without any training process and achieve much better performance than many existing ones with less space and time cost.

Blockchain technology, with its unique decentralized and tamper-resistant features, is being utilized to address the issue of information silos in traditional electronic healthcare. However, as healthcare data sources become increasingly complex and numerous, the limited scalability and transaction throughput of traditional blockchains result in challenges such as slow processing efficiency and vulnerability to attacks in modern healthcare blockchain systems. To address these issues, we propose a Dynamic Security Resource Configuration scheme based on Medical Consortium Blockchain (MC-DSC). This scheme allows for dynamic blockchain configuration based on the varying urgency levels of data, enhancing data processing efficiency. It ensures the security of the data processing process through identity control and data encryption methods. Experimental results demonstrate that, compared to existing blockchain configuration algorithms (SsHealth and Medge-Chain), the proposed scheme achieves approximately a 15% performance improvement by dynamically configuring the blockchain for three data types (secure, urgent, and normal). Additionally, the security module accounts for only 7% of the total time overhead, efficiently safeguarding the security of healthcare data while effectively handling data with different urgency levels.

Inferring potential drug indications plays a vital role in the drug discovery process. It can be time-consuming and costly to discover novel drug indications through biological experiments. Recently, graph learning-based methods have gained popularity for this task. These methods typically treat the prediction task as a binary classification problem, focusing on modeling associations between drugs and diseases within a graph. However, labeled data for drug indication prediction is often limited and expensive to acquire. Contrastive learning addresses this challenge by aligning similar drug-disease pairs and separating dissimilar pairs in the embedding space. Thus, we developed a model called DrIGCL for drug indication prediction, which utilizes graph convolutional networks and contrastive learning. DrIGCL incorporates drug structure, disease comorbidities, and known drug indications to extract representations of drugs and diseases. By combining contrastive and classification losses, DrIGCL predicts drug indications effectively. In multiple runs of hold-out validation experiments, DrIGCL consistently outperformed existing computational methods for drug indication prediction, particularly in terms of top-k. Furthermore, our ablation study has demonstrated a significant improvement in the predictive capabilities of our model when utilizing contrastive learning. Finally, we validated the practical usefulness of DrIGCL by examining the predicted novel indications of Aspirin.

Distributed Reflection Denial-of-Service (DRDoS) attacks have caused significant destructive effects by virtue of emerging protocol vulnerabilities and amplification advantages, and their intensity is increasing. The emergence of programmable data plane supporting line-rate forwarding provides a new opportunity for fine-grained and efficient attack detection. This paper proposed a light-weight DRDoS attack detection and mitigation system called FAPM, which is deployed at the victim end with the intention of detecting the amplification behavior caused by the attack. It places the work of collecting and calculating reflection features on the data plane operated by “latter window assisting former window” mechanism, and arranges complex identification and regulation logic on the control plane. This approach avoids the hardware constraints of the programmable switch while leveraging their per-packet processing capability. Also, it reduces communication traffic significantly through feature compression and state transitions. Experiments show that FAPM has (1) fast response capability within seconds (2) a memory footprint at the KB level and communication overhead of 1 Kbps, and (3) good robustness.

Impulsive noise is always present in real-world image Compressive Sensing (CS) acquisition systems, where existing CS reconstruction performance may seriously deteriorate. In this article, we propose a robust CS formulation for image reconstruction to suppress outliers in the presence of impulsive noise. To address this issue, we consider a novel truncated-Cauchy loss function as the metric of residual error to elevate the reconstruction robustness. Specifically, we design a complementary priors model to incorporate nonconvex nonlocal low-rank prior and deep denoiser prior for high-accuracy image reconstruction. By means of the half-quadratic optimization theory and generalized soft-thresholding technique, we also develop an alternative optimization algorithm for solving the induced nonconvex optimization problem. Numerical simulations demonstrate the robustness and accuracy of the proposed robust CS method compared to some recent CS methods for image reconstruction in impulsive noise.

Packet level measurement in the data plane provides a microscopic view of the network’s state. Although advances in programmable switches and routers make it possible to measure the Sequence of Packet Lengths and Arrival Times (SPLT) in the data plane, collecting this information remains challenging due to limited storage, processing resources, and bandwidth. To address this issue, we propose MES, which Measures and Encodes Simultaneously the packet length and timestamp when each packet passes through the Network Processor (NP) in the switch/router. We design the packet length compression and timestamp compression algorithms to be lightweight and implement the designed algorithms using simple operations supported by the network processor, while taking into account the computation constraints of the NP. Through extensive experiments on five packet traces, we demonstrate that our MES achieves high precision SPLT measurements (up to 99.82% cosine similarity) while reducing storage and bandwidth overhead by up to 87%. Simulations conducted on the BMV2 P4 software switch demonstrate that our designed SPLT measurement mechanism imposes little impact on network throughput and delay.

The visual information processing technology based on deep learning can play many important yet assistant roles for unmanned aerial vehicles (UAV) navigation in complex environments. Traditional centralized architectures usually rely on a cloud server to perform model inference tasks, which can lead to long communication latency. Using transfer learning to unload deep neural networks to the edge-fog collaborative networks has become a new paradigm for dealing with the conflicts between computing resources and communication latency. However, ensuring the security of edge-fog collaborative networks entity remains challenging. For such, we propose an anonymous authentication and group key agreement scheme for the UAV-enabled edge-fog collaborative networks, consisting of the UAV authentication protocol and the collaborative networks authentication protocol. Utilizing the AVISPA assessment tool and security analysis, the security requirements and functional features of the proposed scheme are demonstrated. From the performance results of the proposed scheme, we show that it is superior to existing authentication schemes and promising.

The payment channel network aims to solve the problems of long payment confirmation time and limited throughput in cryptocurrencies through off-chain payments. Hash Time-Lock Contract (HTLC) is an off-chain payment protocol that Lightning Network (LN) adopted. Unfortunately, when performing high-valued payments off-chain, due to the impact of payment channel capacity, it is often necessary to split a single payment, which increases the transaction fees and time. Therefore, we propose LightPay, an atomic off-chain multi-path payment protocol based on adapter signature and discrete logarithm problem. Among different conditions encoded in the multi-path contract, the multi-path transmission of a single high-valued payment can be realized under the premise of the unlinkability of partial payments. We construct an ideal functionality in the Universal Composability framework and demonstrate that LightPay UC-realizes it, thereby providing proof of its security and privacy. Experimental results indicate that the payment success rate of LightPay can be increased by 11.08% in 0.0025 BTC payments compared with the single-path payment protocol Multihop HTLC in LN. Additionally, compared with the multi-path payment protocol CryptoMaze, the communication overhead required by LightPay is reduced to about 55.6% on average in the simulated network. Overall, LightPay has advantages regarding payment success rate and overhead.

Accurately identifying IoT device types is crucial for IoT security and resource management. However, existing traffic-based device identification algorithms incur high measurement, storage, and computation costs, as they continuously need to capture, store, and parse device traffic. To overcome these challenges, we propose an innovative framework that employs a discontinuous traffic measurement strategy, reducing the number of packets captured, stored, and parsed. To ensure accurate identification, we introduce several novel techniques. First, we propose a graph neural network-based tensor completion model to estimate missing traffic features in unmeasured time slots. Our model can utilize historical information to flexibly and efficiently estimate missing features. Second, we propose a convolutional neural network-based classifier for device identification. The classifier utilizes traffic features and node embeddings learned from the tensor completion model to achieve precise device identification. Through extensive experiments on real IoT traffic traces, we demonstrate that our framework achieves high accuracy while significantly reducing costs. For instance, by capturing only 30% of the packets, our framework can identify devices with a high accuracy of 0.9558. Moreover, compared to current tensor completion methods, our method can estimate missing values with higher accuracy and achieve a 1.53-fold speedup over the next-fastest baseline.

Traffic prediction is crucial for intelligent transportation systems, assisting in making travel decisions, minimizing traffic congestion, and improving traffic operation efficiency. Although effective, existing centralized traffic prediction methods have privacy leakage risks. Federated learning-based traffic prediction methods keep raw data local and train the global model in a distributed way, thus preserving data privacy. Nevertheless, the spatial correlations between local clients will be broken as data exchange between local clients is not allowed in federated learning, leading to missing spatial information and inferior prediction accuracy. To this end, we propose a federated graph neural network with spatial information completion (FedGCN) for privacy-preserving traffic prediction by adopting a federated learning scheme to protect confidentiality and presenting a mending graph convolutional neural network to mend the missing spatial information during capturing spatial dependency to improve prediction accuracy. To complete the missing spatial information efficiently and capture the client-specific spatial pattern, we design a personalized training scheme for the mending graph neural network, reducing communication overhead. The experiments on four public traffic datasets demonstrate that the proposed model outperforms the best baseline with a ratio of 3.82%, 1.82%, 2.13%, and 1.49% in terms of absolute mean error while preserving privacy.

Network operation and maintenance rely heavily on network traffic monitoring. Due to the measurement overhead reduction, lack of measurement infrastructure, and unexpected transmission error, network traffic monitoring systems suffer from incomplete observed data and high data sparsity problems. Recent studies model missing data recovery as a tensor completion task and show good performance. Although promising, the current tensor completion models adopted in network traffic data recovery lack an effective and efficient retraining scheme to adapt to newly arrived data while retaining historical information. To solve the problem, we propose LightNestle, a novel sequential tensor completion scheme based on meta-learning, which designs (1) an expressive neural network to transfer spatial knowledge from previous embeddings to current embeddings; (2) an attention-based module to transfer temporal patterns into current embeddings in linear complexity; and (3) meta-learning-based algorithms to iteratively recover missing data and update transfer modules to catch up with learned knowledge. We conduct extensive experiments on two real-world network traffic datasets to assess our performance. Results show that our proposed methods achieve both fast retraining and high recovery accuracy.

Summary Array‐unit dual‐usage register is a kind of register resource that can be read or written as a whole or individually. It is mainly configured in processors with SIMD processing units and provides register‐level speed data transfer between the scalar and vector processing units. To improve the efficiency of algorithms by using an array‐unit dual‐usage register, we investigate in this article the problem of adapting register allocation to code containing array‐unit dual‐usage register names. We propose a corresponding global register allocation method by combining the allocation of regular registers with array‐unit dual‐usage register, ensuring that the names of array‐unit dual‐usage register can be used in the input code of register allocation. Moreover, we present the processing framework of this method and the specific algorithms of some related vital aspects and demonstrate the working principles of the algorithms by an example. Experimental studies were conducted on a platform based on the FT‐M7002 DSP core, and showing that our register allocation method can effectively handle codes containing array‐unit dual‐usage register names and support relevant application algorithms to improve their data transfer scheme. For some typical algorithms with input matrix, substantial performance improvements of twofold or higher are achieved.

Summary With the advancement of global positioning systems and communication technologies, location‐based services (LBS) have become widely used. However, user location privacy is vulnerable during service exchange, and location‐related queries may result in serious privacy disclosure issues. In this article, considering that attackers may make use of auxiliary information to track a mobile user, we propose a real‐time privacy‐preserving scheme based on the grouping of queries in continuous LBS. In this scheme, we first design the probability density functions to select virtual locations. Then, we group these virtual locations and the query location of the user into several queries. Simultaneously, we send these grouping queries to the LBS server. Following the steps outlined above, grouping queries can be used to conceal the true query. Moreover, the results of security analyses and performance evaluations show that the proposed scheme is effective and secure.

Crowdsourcing takes advantage of human intelligence to solve complex problems that computers cannot handle. People actively participate in computational tasks for rewards, especially those that are relatively simple for humans but challenging for computers. However, most traditional crowdsourcing systems must rely on a central organisation to handle the work associated with crowdsourcing. This may lead to privacy and security issues such as information leakage, single point of attack and unfair judgement. Meanwhile, the high service fees of existing central crowdsourcing platforms will reduce people's motivation to participate in crowdsourcing tasks. Consequently, PrivBCS is proposed in this paper, a blockchain-based decentralised crowdsourcing system that does not rely on third-party agencies and requires only low transaction fees. In this paper, PrivBCS protects the confidentiality of crowdsourcing data and the identity privacy of participants by cryptographic means, such as Paillier, El-Gamal and CPABE. It selects the most suitable worker to perform tasks by setting access control policies on task data and matching workers' attributes while ensuring the anonymity of workers' identity. Considering that the data volume of crowdsourcing tasks may be large and there is a certain upper limit to the block capacity of the blockchain, an on-chain and off-chain collaborative storage scheme is proposed, which can increase the throughput of the blockchain system and improve its scalability to a certain extent. In PrivBCS, we implement the transaction process of crowdsourcing tasks through smart contracts. The experimental results and security analysis in this paper demonstrate the effectiveness, efficiency, and feasibility of PrivBCS.

Through the digitalisation of Civil Trials and the implementation of the Telematic Civil Process framework, the Italian Ministry of Justice has amassed a wealth of data covering all facets of modern Trials. While data availability has surged, the focus now lies in actively analysing this data to optimise Trials and curtail their duration. This paper, with a strong emphasis on its outcomes, delves into the analysis of data from the Court of Livorno. It seeks to pinpoint specific events within the Trial workflow that significantly extend Trial durations. Notably, Domain Experts have identified a set of events, intending to validate their pivotal role in recognising critical Trials. Leveraging Machine Learning techniques, the paper evaluates multiple binary classifiers to proactively identify potentially critical Trials, empowering Judges to mitigate future issues. The study has yielded a remarkable 80% accuracy rate in predicting Trials exceeding acceptable duration thresholds.

Traffic matrices (TMs) are essential for managing networks. Getting the whole TMs is difficult because of the high measurement cost. Several recent studies propose sparse measurement schemes to reduce the cost, which involve taking measurements on only a subset of origin and destination pairs (OD pairs) and inferring data for unmeasured OD pairs through matrix completion. However, existing sparse network measurement schemes suffer from the problems of high computation costs and low recovery quality. This paper investigates the coherence feature of real traffic flow data traces (Abilene and GÈANT). Both data sets are high coherence, with column coherence greater than row coherence. According to the coherence feature of both data sets, we propose our Redundant Row Subspace-based Matrix Completion (RRS-MC). RRS-MC involves several techniques. Firstly, we design an algorithm to identify subspace rows (OD pairs) from historical data. Secondly, based on the identified subspace rows, we design our sampling scheduling algorithm, which takes full measurement samples in subspace rows while taking partial measurement samples in the remaining rows. Moreover, we propose a redundant sampling rule prevent the recovery accuracy decrease caused by the subspace rows varying. Finally, we design a completion algorithm to recover the partially measured rows. We conduct extensive experiments. Results indicate that the proposed scheme is superior to the state-of-the-art sampling and completion scheme in computation costs and recovery accuracy.

In this paper, we propose a dynamic object elimination algorithm that combines semantic and geometric constraints to address the problem of visual SLAM being easily affected by dynamic feature points in dynamic environments. This issue leads to the degradation of localisation accuracy and robustness. Firstly, we employ a lightweight YOLO-Tiny network to enhance both detection accuracy and system speed. Secondly, we integrate the YOLO-Tiny network into the ORB-SLAM3 system to extract semantic information from the images and initiate the elimination of dynamic feature points. Subsequently, we augment this approach by incorporating geometric constraints between neighbouring frames to further eliminate dynamic feature points. Then, the former is supplemented by combining the geometric constraints between neighbouring frames to further eliminate dynamic feature points. Experiments on the TUM dataset demonstrate that the algorithm in this paper can improve the Relative Pose Error (RPE) by up to 95.12% and the Absolute Trajectory Error (ATE) by up to 99.01% in high dynamic sequences compared to ORB-SLAM3. The effectiveness of dynamic feature point elimination is evident, leading to significantly improved localisation accuracy.

The deep integration of Internet of Medical Things (IoMT) and Artificial intelligence makes the further development of intelligent medical services possible, but privacy leakage and data security problems hinder its wide application. Although the combination of IoMT and federated learning (FL) can achieve no direct access to the original data of participants, FL still can't resist inference attacks against model parameters and the single point of failure of the central server. In addition, malicious clients can disguise as benign participants to launch poisoning attacks, which seriously compromises the accuracy of the global model. In this paper, we design a new privacy protection framework (BFG) for decentralized FL using blockchain, differential privacy and Generative Adversarial Network. The framework can effectively avoid a single point of failure and resist inference attacks. In particular, it can limit the success rate of poisoning attacks to less than 26%. Moreover, the framework alleviates the storage pressure of the blockchain, achieves a balance between privacy budget and global model accuracy, and can effectively resist the negative impact of node withdrawal. Simulation experiments on image datasets show that the BFG framework has a better combined performance in terms of accuracy, robustness and privacy preservation.

The Dual-motor multi-gear coupling powertrain (DMCP) has the potential to improve transmission system efficiency and driving comfort, but its complex structure and multiple working modes present challenges. The switching between different modes is easy to cause longitudinal biggish vehicle jerk. To address these issues,this paper introduces the Deep Deterministic Policy Gradient (DDPG) algorithm in the design of an Energy Management Strategy (EMS) that minimises total drive power consumption. And the number of working modes is divided and simplified. The process of switching dual motor and single motor to single motor is introduced in detail. The simulation results using AMESim and MATLAB show that the energy management strategy can effectively improve the economy, achieve no power interruption during mode switching, shift impact is less than 8m/s3, and output torque is remains stable.

Food-oriented cross-modal retrieval aims to retrieve relevant recipes given food images or vice versa.The modality semantic gap between recipes and food images (text and image modalities) is the main challenge.Though several studies are introduced to bridge this gap, they still suffer from two major limitations: 1) The simple embedding concatenation only can capture the simple interactions rather than complex interactions between different recipe components.2) The image feature extraction based on convolutional neural networks only considers the local features and ignores the global features of an image, as well as the interactions between different extracted features.This paper proposes a novel method based on Latent Component Weight Factors and Global Information (LCWF-GI) to learn the robust recipe and image representations for food-oriented cross-modal retrieval.This proposed method integrates the textual embeddings of different recipe components into a compact embedding to represent the recipes with the latent component-specific weight factors.A transformer encoder is utilised to capture the intra-modality interactions and the importance of different extracted image features for enhanced image representations.Finally, the bi-directional triplet loss is further used to perform retrieval learning.Experimental results on the Recipe 1M dataset show that our LCWF-GI method achieves competent improvements.

Aiming at the problem of low detection accuracy of network traffic data types by traditional intrusion detection methods, we propose an improved Harris Hawk hybrid intrusion detection method to enhance the detection capability.The improved Harris Hawk optimization algorithm is used as a feature selection scheme to reduce the impact of redundant and noisy features on the performance of the classification model.The algorithm introduces the singer map to initialise the population, uses multi-information fusion to obtain the best prey position, and applies the sine function-based escape energy to execute a prey search strategy to obtain the optimal subset of features.In addition, the original data is preprocessed by the k-nearest neighbour and deep denoising autoencoder (KNN-DDAE) to relieve the imbalance problem of the network traffic data.Finally, a deep neural network (DNN) is used to complete the classification.Simulation experiments are conducted on the dataset NSL-KDD, KDD CUP99, and UNSW-NB15.The results show that our feature selection and data balancing scheme greatly improves the detection accuracy.In addition, the detection performance of this method is better than the current popular intrusion detection schemes.

Abstract With the development of the drying rack system, users with limited time tend to use the fully functional drying rack system to realize various intelligent control functions. However, the existing control methods for drying rack system has some defects such as, low intelligence and system delay, which are unsuitable for most users. In this paper, an efficient intelligent control algorithm based on the back‐propagation (BP) neural network is proposed. In this system, STM32F103 is first utilized as the central controller and multiple sensors are used to collect environmental information. Then, the remote control, voice keywords, and buttons can be used to achieve intelligent control. Subsequently, a motor drive intelligence control algorithm based on the BP neural network (MCBP) is proposed to improve the accuracy of the intelligent control. Next, an Application (APP) that can display environmental data such as wind speed, temperature, and humidity is developed. The APP can realize various intelligent control functions such as lifting, panning, rotating, and harvesting. Finally, MCBP is compared with normal control and programmable logic controller control. The accuracy of the MCBP is higher than other two control methods. The final extensive experiments confirm the accuracy and efficiency of the proposed intelligent control algorithm.

With the expansion of scale, the Internet of Things (IoT) suffers more and more security threats, and vulnerability and sensitivity to attacks are also increasing. As a distributed and secure network architecture, Blockchain is suitable for protecting the security and privacy of the IoT. In this article, we propose a secure smart blockchain IoT architecture based on Graph Neural Networks (GNN) named GTxChain, using a distributed intelligent prophecy machine to obtain off-chain data and construct the transaction data structure of the blockchain through the blockchain-directed acyclic graph (DAG). In the off-chain transaction and off-chain storage part, we use the lightning network, improved IPFS and GNN to obtain transaction information and continuously update the blockchain network and blockchain for transaction verification and other operations. GTxChain employs an IPFS storage architecture to enhance user privacy and reduce data processing time. Compared to other blockchain architectures, it improves by 10.51% and has better efficiency and stability in terms of Merkle-proof time overhead. Experimental results show that the GTxChain architecture can effectively ensure the IoT’s trustworthiness, security, and privacy (TSP).

Location-based services have become prevalent and the risk of location privacy leakage increases. Most existing schemes use third-party-based or third-party-free system architectures; the former suffers from a single point of failure (SPOF) and the latter experiences a heavy load on user terminal equipment and higher communication costs. Ensuring location privacy while lowering system overhead becomes a challenge. Many existing schemes fail to leverage the responses from an LBS server; such responses can be cached to answer subsequent queries. As a result, providing users with a relatively comprehensive level of location privacy protection is troublesome. In this article, we propose a caching-based dual${K}$-anonymous (CDKA) location privacy-preserving scheme in edge computing environments. Our scheme employs an edge server to intercedes between a user and LBS the server. We reduce the load on the user device by applying multilevel caching and to protect location privacy through dual anonymity. To ensure the location privacy in our construction, we set mobile clients and edge servers as anonymous. We use caching to lower the communication overhead and enforce the location privacy. The security analysis of our scheme supports its robustness against the edge server and the LBS server privacy offenses. We rely on the computation time, the communication cost, and the cache hit ratio to evaluate our work against existing constructions. The results are twofold: our work possesses a better response rate down to 15–32.6 ms and exhibits lower communication cost requirements down to 6.2–38.9 kB compared to existing works. Our scheme witnesses a higher cache hit ratio of up to 13.6% and 39.1% compared to the literature.

With the expansion of the number of users in online social networks, the diversity of users and community characteristics become more prominent. Hypernetwork theory provides a path for characterizing complex relationships in networks. This paper used hypergraph’s hyperedges to represent the community relationship between users, and created an online social hypernetwork information dissemination model (UHIR model) based on user and information attributes by combining the hypernetwork model with the SEIR model. Through this model, this article simulated and analyzed the dynamic process and laws of information dissemination under different network structures, and studied the influence of user influence, confidence, interest value, and information timeliness of the process. The simulation results show that this model can accurately describe the information dissemination trend and process in the real online social network. This work extends a new research direction of information dissemination in hypernetworks and contributes to the in-depth study of more complex information dissemination mechanisms.

Software-Defined Networking (SDN) switches typically have limited ternary content addressable memory (TCAM) that caches the flow entries on the data plane. The scarcity and strong resource competitiveness of TCAM space put the flow tables at the risk of malicious Distributed Denial-of-Service (DDoS) attacks. In this paper, we propose LtRFT, a Learning-To-Rank (LtR) based scheme for mitigating the low-rate DDoS attacks targeted at flow tables. LtRFT consists of three modules:monitor,ranker, andmitigator.Monitormanages the flow table status and sends alerts to other modules after detecting attacks.Rankermodels the attack mitigation problem as a flow entry ranking task, and ranks malicious flows with a high eviction priority using a pairwise-based LtR algorithm. Themitigatorfrees up the flow table space by deleting malicious flow entries according to the flow entry ranking sequence generated byranker. We introduce LtR to network attack detection innovatively and use both classification and information retrieval metrics to describe and evaluate LtRFT. Extensive experiments were conducted to validate the effectiveness and robustness of LtRFT in detecting and mitigating the low-rate data plane DDoS attacks. LtRFT can detect malicious attack flows with an accuracy of over 96%, and can reduce the attack flow duration by 97.7% with an average extra latency of 0.5 seconds, which proves that LtRFT is practicable in SDN deployments.

As part of Big Data trends, the ubiquitous use of the Internet of Things (IoT) in the industrial environment has generated a significant amount of network traffic. In this type of IoT industrial network where there is a large equipment heterogeneity, security is a fundamental issue; thus, it is very important to detect likely intrusion behaviors. Furthermore, since the proportion of labeled data records is small in the IoT environment, it is challenging to detect various attacks and intrusions accurately. This investigation builds a semisupervised ladder network model for intrusion detection in the Industrial IoT. This model considers the manifold distribution of high-dimensional data and incorporates a manifold regularization constraint in the decoder of the ladder network. Meanwhile, the feature propagation between layers is strengthened by adding more cross-layer connections in this model. On this basis, a random attention-based data fusion approach is proposed to generate global features for intrusion detection. The experiments on the CIC-IDS2018 dataset show that the proposed approach can recognize the intrusion with less false alarm rate, while model training is time efficient.

With the development of edge computing and 5G, the demand for resource-limited devices to execute computation-intensive tasks can be effectively alleviated. The research on computation offloading lays an essential foundation for realizing mobile edge computing, and deep reinforcement learning (DRL) has become an emerging technique to address the computation offloading problem. This article utilizes a DRL-based algorithm to design a decentralized computation offloading framework aimed at minimizing the computational cost. We employ a multiuser system model with a single-edge server suitable for industrial scenarios. Then, we propose a dual-critic deep deterministic policy gradient (DC-DDPG) algorithm based on the deep deterministic policy gradient (DDPG) algorithm to tackle computation offloading and resource allocation problems for all users. DC-DDPG adopts two critic nets in both the primary and target nets to fit the action value of two different optimization objectives, which expedites the convergence during the training process and reduces the computational cost of the edge computation system during operation. Compared with other DRL methods, such as deep Q-network and DDPG, numerical results demonstrate that the proposed DC-DDPG algorithm has a faster convergence speed and performs significantly better than other DRL-based algorithms in terms of system computational cost in computing-intensive tasks, which makes it more suitable for industrial intelligent manufacturing scenarios with large data volume.

Traffic flow forecasting is indispensable in today’s society and regarded as a key problem for Intelligent Transportation Systems (ITS), as emergency delays in vehicles can cause serious traffic security accidents. However, the complex dynamic spatial-temporal dependency and correlation between different locations on the road make it a challenging task for security in transportation. To date, most existing forecasting frames make use of graph convolution to model the dynamic spatial-temporal correlation of vehicle transportation data, ignoring semantic similarity between nodes and thus, resulting in accuracy degradation. In addition, traffic data does not strictly follow periodicity and hard to be captured. To solve the aforementioned challenging issues, we propose in this article CRFAST-GCN, a multi-branch spatial-temporal attention graph convolution network. First, we capture the multi-scale (e.g., hour, day, and week) long- short-term dependencies through three identical branches, then introduce conditional random field (CRF) enhanced graph convolution network to capture the semantic similarity globally, so then we exploit the attention mechanism to captures the periodicity. For model evaluation using two real-world datasets, performance analysis shows that the proposed CRFAST-GCN successfully handles the complex spatial-temporal dynamics effectively and achieves improvement over the baselines at 50% (maximum), outperforming other advanced existing methods.

With the prevalence of Intelligent Transportation Systems (ITS), massive sensors are deployed on roadside, vehicles, and infrastructures. One key challenge is imputing several different types of missing entries in spatial-temporal traffic data to meet the high-quality demand of data science applied in Cooperative-ITS (C-ITS) since accurate data recovery is critical to many downstream tasks in ITSs, such as traffic monitoring and decision making. For such, it is proposed in this article solutions to three kinds of data recovery tasks in a unified model via spatial-temporal aware Graph Neural Networks (GNNs), named Spatial-Temporal Aware Data Recovery Network (STAR), enabling a real-time and inductive inference. A residual gated temporal convolution network is designed to permit the proposed model to learn the temporal pattern from long sequences with masks and an adaptive memory-based attention model for utilizing implicit spatial correlation. To further exploit the generalization power of GNNs, a sampling-based method is adopted to train the proposed model to be robust and inductive for online servicing. Extensive numerical experiments on two real-world spatial-temporal traffic datasets are performed, and results show that the proposed STAR model consistently outperforms other baselines at 1.5-2.5 times on all kinds of imputation tasks. Moreover, STAR can support recovery data for 2 to 5 hours, with its performance barely unchanged, and has comparable performance in transfer learning and time-series forecast. Experimental results demonstrate that STAR provides adequate performance and rich features for multiple data recovery tasks under the C-ITS scenario.

Software-defined networking (SDN) faces challenges in efficiently forwarding packets across the network due to the limited capacity of flow tables in the switches. Ternary content addressable memory (TCAM) is typically used to store flow tables, but its limited capacity makes it vulnerable to attacks. Specifically, the Low-rate Flow Table Overflow (LFTO) attack is an attack against the flow table capacity limit, which can occupy massive space in the flow table to decrease the forwarding performance of normal flow rules by slowly sending packets that cannot match the flow table. To address this, we propose the FTMaster, a system to monitor, detect and mitigate LFTO attacks based on machine learning. FTMaster monitors and detects the flow table state by analyzing the features of flow tables. Once the LFTO attack is detected, FTMaster will activate the mitigation module to extract and analyze the features of each flow rule, evict attack flows, and ultimately block the attack source, thereby protecting flow tables and normal flows. Experimental results demonstrate that FTMaster enables real-time LFTO attack detection and mitigation, ensuring normal forwarding and availability of flow tables.

Network trouble shooting, failure location, and anomaly detection rely heavily on network traffic measurement data. Due to the lack of measurement infrastructure, the high measurement cost, and the unavoidable transmission loss, network monitoring systems suffer from the problem that the network traffic data are incomplete. This article models the traffic data as a tensor to exploit its strong ability of feature extraction to recover the missing data. Different from traditional tensor completion which relies on tensor factorization, we design a novel Deep Adversarial Tensor Completion (DATC) scheme based on Deep Learning (DL) techniques. DATC is the first scheme that exploits the data reconstruction ability of autoencoder and the power of adversarial training from Generative Adversarial Networks to infer the missing data. Despite that DL techniques achieve great success in the image field, designing an algorithm based on DL techniques to recover the traffic data with missing entries faces additional challenges due to the skewed distribution and the sparsity of traffic data. To conquer these challenges, we propose the use of two techniques, adversarial training and missing data aware convolution. These techniques help DATC to learn the complex features of the traffic data and infer the missing data following the data distribution of traffic data. Our extensive experimental results using two public real-world network traffic datasets and running both offline and online demonstrate that DATC can achieve significantly better recovery accuracy while capturing the data distribution of the traffic data even when the sampling ratio is very low.

With the advances and innovations in digital technologies, blockchain has empowered advancements in communications and networking, promising to build trust and establish secure decentralized communications networks. Unfortunately, current personal data privacy protection schemes still suffer from explicit storage, lack of data ownership and implementation of fine-grained access control by users, and lack of transparency and auditability of data. In this article, we propose a personal data privacy protection scheme based on consortium blockchain that stores original data encrypted with an improved Paillier homomorphic encryption mechanism, namely PDPChain, where users realize fine-grained access control based on ciphertext policy attribute-based encryption (CP-ABE) on blockchain. In this scheme, consortium blockchain combines distributed private clusters to store the encrypted data, improving data transmission efficiency, and guaranteeing user privacy and security through off-chain storage and on-chain transmission synergy. In addition, it is more lightweight encryption and demarcation, ultimately protecting personal data privacy and providing a secure and trusted way to obtain information for data mining. For the performance testing, data in the form of files are used as an example, and the scheme is designed and simulated on Hyperledger Fabric and InterPlanetary File System. Experimental results show that the improved Paillier encryption mechanism reduces the overall encryption and decryption elapsed time by 25% and encryption elapsed time by 48%. Furthermore, the proposed CP-ABE access control method is adaptive to storing and sharing a massive amount of data. With the increase in the number of access control policies, the overall time-consuming of the scheme does not increase, and the time-consuming of decryption can also be stabilized at about 2 s.

Against the backdrop of the booming supply chain finance and logistics industry, Logistics 4.0 has emerged. However, the financing capacity of today's logistics companies is still unable to respond to the needs of the rapid development of the supply chain. On the one hand, the problem of logistics companies’ lack of existing high-value collateral in supply chain finance has led to a clutter of their credit data and difficulty in verifying their creditworthiness. On the other hand, the massive access to logistics companies’ business information can also lead to privacy leaks. At the same time, the transparency feature of blockchain is used to solve the financing dilemma of many industries in supply chain finance. On this basis, we propose a blockchain-based financing scheme (BFS) for logistics company. BFS utilises more efficient and interpretative smart contract technology, as well as improved privacy information query and invocation algorithms, to realise automatic control of entity node privacy information flow and significantly simplifying the steps and lowering the thresholds to financing for logistics companies, while protecting the privacy of their data. After extensive simulation testing, BFS can run supply chain blocks at a stable transaction throughput of around 280 RPS, with data transfers that meet the financing needs of logistics companies, providing a higher and more stable performance than the native Hyperledger Fabric.

Currently, the main challenges of the frameworks for healthcare monitoring are as follows: minimising latency, especially for delay-sensitive diseases such as sudden heart disease; identifying health situation in a timely and accurate manner when correlating physiological indicators and context information; reducing the risk of exposure because health data are highly private. In response to the above, this paper proposes a framework for 5G-secure-smart healthcare monitoring (5GSS) to achieve the following goals: fast and accurate identification of context-aware health situation, blockchain-based secure data sharing, and low-latency services for emergent patients. The framework consists of a data acquisition layer, a diagnosis and security layer (edge cloud), and a health service layer. The proposed framework adopts the following key technologies: a 5G-IPv6 communication network, context-aware health situation identification-based similarity measure, and blockchain-based secure data sharing mechanism. Finally, a prototype system has been implemented to monitor hypertensive heart disease, confirming its effectiveness with respect to a real scenario. Combined with the data of 45 patients, the prototype system can identify health situations with an accuracy of 96.34% at a sensitivity of 92.46% and a specificity of 93.62%, while significantly reducing the latency and improving the data sharing security.

Traffic forecasting is highly challenging due to its complex spatial and temporal dependencies in the traffic network. Graph Convolutional Neural Network (GCN) has been effectively used for traffic forecasting due to its excellent performance in modelling spatial dependencies. In most existing approaches, GCN models spatial dependencies in the traffic network with a fixed adjacency matrix. However, the spatial dependencies change over time in the actual situation. In this paper, we propose a graph learning-based spatial-temporal graph convolutional neural network (GLSTGCN) for traffic forecasting. To capture the dynamic spatial dependencies, we design a graph learning module to learn the dynamic spatial relationships in the traffic network. To save training time and computation resources, we adopt dilated causal convolution networks with a gating mechanism to capture long-term temporal correlations in traffic data. We conducted extensive experiments using two real-world traffic datasets. Experimental results demonstrate that the proposed GLSTGCN achieves superior performance than all state-of-art baselines.

When the ambient temperature is low, a large amount of water mist and dust will inevitably appear around the zinc flotation cell, forming haze, which seriously affects the extraction of flotation froth image features. General defogging methods of natural image are difficult to obtain satisfactory results for such industrial haze image. Therefore, we propose a real-time defogging method based on ASM-VoFD (Atmospheric Scattering Model and the Variable-order Fractional Differential). First, the dark pixel ratio is used to detect fog in froth image, which solves the redundant calculation caused by unnecessary defogging operations. Second, the linear transformation of the atmospheric scattering model is used to calculate the initial transmission map, and the gaussian filter is used to optimize the initial transmittance, and the haze-free image is restored with atmospheric light estimation. Finally, a variable order fractional differential operator is used to enhance the edges and texture details of the restored froth image, which solves the problems of blurred edges and low contrast. The experiments show that the algorithm has a good defogging effect on the industrial images, enhance the edges of the image, and can be effectively implemented in O(N) time to meet the application requirements of real-time flotation monitoring.

The Internet of Vehicles is deployed in an open environment, and protecting its security and data privacy is the challenge. Carrying out the Internet of Vehicles secure authentication before interaction of information is an important part of ensuring the security foundation. Therefore, this article designs a safe and reliable Internet of Vehicles authentication and key agreement schemeassisted by blockchain. This article uses a multi-TA network model to improve the efficiency of authentication. Because of the rapid movement of vehicles, it will continue to appear cross-RSU and TA certification. Considering the disadvantages of most centralised authentication protocols using a single TA, this paper uses the multi-TA model to improve the efficiency of authentication. By usingblockchain technology to store the authentication information of vehicles, the cross-domain authentication of vehicles and the protection of user privacy information can be well realised. At the same time, in order to reduce the time of vehicle authentication, this scheme uses a lightweight calculation operation to complete the whole process of authentication. Through security analysis and results of Proverif simulation, the security of the solution is well proved, our scheme can resist various common attacks. Compared with some existing Internet of Vehicles security authentication protocols, the proposed scheme has a lower cost of computation, communication, and storage.

Cyber–physical systems (CPSs) is a security real-time embedded system. CPS integrates the information sensed by the current physical sensors, through high-speed real-time transmission, and then carries out powerful information processing to effectively interact and integrate the physical and the information worlds. With the aim to improve the quality of service, optimize the existing physical space, and increase security, collaborative filtering algorithms have also been widely used in various recommendation models for Internet of Things (IoT) services. However, general collaborative filtering algorithms cannot capture complex interactive information in the sparse Mashup–Web service call matrix, which leads to lower recommendation performance. Based on the artificial intelligence technology, this study proposes a recommendation algorithm for a security collaborative filtering service that integrates content similarity. A security collaborative filtering module is used to capture the complex interaction information between Mashup and Web services. By applying the content similarity module to extract the semantic similarity information between the Mashup and Web services, the two modules are seamlessly integrated into a deep neural network to accurately and quickly predict the rating information of Mashup for the Web services. Real data set on the intelligent CPS is captured and then compared with mainstream service recommendation algorithms. Experimental results show that the proposed algorithm not only efficiently completes the Web service recommendation task under the premise of sparse data but also shows better accuracy, effectivity, and privacy. Thus, the proposed method is highly suitable for the application of intelligence CPS.

Blockchain technology is rapidly changing the transaction behavior and efficiency of businesses in recent years. Data privacy and system reliability are critical issues that is highly required to be addressed in Blockchain environments. However, anomaly intrusion poses a significant threat to a Blockchain, and therefore, it is proposed in this article a collaborative clustering-characteristic-based data fusion approach for intrusion detection in a Blockchain-based system, where a mathematical model of data fusion is designed and an AI model is used to train and analyze data clusters in Blockchain networks. The abnormal characteristics in a Blockchain data set are identified, a weighted combination is carried out, and the weighted coefficients among several nodes are obtained after multiple rounds of mutual competition among clustering nodes. When the weighted coefficient and a similarity matching relationship follow a standard pattern, an abnormal intrusion behavior is accurately and collaboratively detected. Experimental results show that the proposed algorithm has high recognition accuracy and promising performance in the real-time detection of attacks in a Blockchain.

Summary The risk of sharing data in cloud computing has gathered increasing attention. After the owner of some confidential data outsources the data to cloud storage services and shares it with others, the data owner lost the control to the data to a large extent. To achieve data sharing while keeping data confidentiality, attribute‐based encryption (ABE) can be employed by cloud storage services. However, ABE can only guarantee that outsourced data on the cloud is decrypted by attribute‐satisfying users but cannot restrict data from being accessed by dishonest users whose attributes also satisfy the access‐control policy. It is impossible for the data owner to control the shared data after it has been decrypted by dishonest users, especially when a set of attribute‐satisfying dishonest users may collude. To address this concern, we propose a traceable data sharing scheme called TraceChain. In TraceChain, data is encrypted over a new CP‐ABE scheme called E‐CP‐ABE. Furthermore, the system parameters for generating the private key in E‐CP‐ABE are uploaded to the private blockchain and transactions are performed on the chain. The data owner can obtain the identity of users by monitoring system parameters simultaneously and control data sharing on the blockchain. To prove the security of our scheme, the security analysis is given in this paper. Meanwhile, experimental results also show that our system is viable and efficient.

Internet of Things (IoT) devices are widely considered in smart cities, intelligent medicine, and intelligent transportation, among other fields that facilitate people's lives, producing a large amount of private data. However, due to the mobility, limited performance, and distributed deployment of IoT, traditional access control methods cannot support the security of private data's access control process in current IoT environments. To address such problems, this article proposes an auditable access control model, based on an attribute-based access control model, and manages the access control policy for private data through the request record, the response record, and the access record stored in the blockchain network. Additionally, a Blockchain-based auditable access control system is also proposed based on the auditable access control model, ensuring private data security in IoT environments and realizing effective management and auditable access to these data. Experimental results show that the proposed system can maintain high throughput while ensuring private data security for real application scenarios in IoT environments.

In Industry 5.0, the massive number of Internet of Things devices have increasing demands for group communication with a high communication efficiency and low energy consumption. However, group communication meets continuously increasing security risk challenges. Existing authentication and group key agreement schemes have encountered many problems, such as lack of anonymity and untraceability. In this article, we propose an anonymous authentication and dynamic group key agreement scheme based on the Blockchain and token mechanism, where each group member can apply for a time-sensitive token during the first authentication and only needs to check the validity of the token in the subsequent authentication, reducing the computational and transmission costs considerably. The verification on the security of the proposed scheme is tackled through mathematical analysis and validated using ProVerif, and comparisons with existing schemes demonstrate that the proposed scheme reduces the security risks and each group member’s energy consumption.

The Industrial Internet of Things ( IIoT ) is designed to refine and optimize the process controls, thereby leveraging improvements in economic benefits, such as efficiency and productivity. However, the Radio Frequency Identification ( RFID ) technology in an IIoT environment has problems such as low security and high cost. To overcome such issues, a mutual authentication scheme that is suitable for RFID systems, wherein techniques in Deep Learning ( DL ) are incorporated onto the Arbiter Physical Unclonable Function ( APUF ) for the secured access authentication of the IC circuits on the IoT, is proposed. The design applies the APUF-MPUF mutual authentication structure obtained by DL to generate essential real-time authentication information, thereby taking advantage of the feature that the tag in the PUF circuit structure does not need to store any essential information and resolving the problem of key storage. The proposed scheme also uses a bitwise comparison method, which hides the PUF response information and effectively reduces the resource overhead of the system during the verification process, to verify the correctness of the two strings. Security analysis demonstrates that the proposed scheme has high robustness and security against different conventional attack methods, and the storage and communication costs are 95.7% and 42.0% lower than the existing schemes, respectively.

As a novel Internet architecture focused on data contents, Named Data Networking (NDN) has been proven to be of great value in supporting the Internet of Things, Edge computing, Blockchain, and other popular topics. They can benefit mainly from NDN's intrinsic properties, such as flexible multicasting, in-network caching, among several others. However, once NDN's forwarding plane suffers Denial of Service (DoS) attacks, the overall system performance would be affected significantly. In NDN data transmission, interest forwarding is the most time-consuming operation and thus opens a possible vector of DoS attacks. It is proposed in this paper a fast name lookup algorithm for NDN interest forwarding, which selects feature prefixes instead of lengths to filter out interest packets in NDN interest forwarding. Due to the excellent filtration with feature prefixes, the algorithm accelerates NDN forwarding processes. Compared with other existing solutions, the proposed algorithm shows more than 70% of time improvement in forwarding malicious interests, while remaining at the same performance level in standard cases.

Representation learning techniques have been frequently applied in multimedia content analysis and retrieval. In this study, an efficient multimedia data clustering method is presented, which consists of two independent algorithms. First, we propose a new representation framework by incorporating sparse coding and manifold regularisation in an optimisation objective function, the cluster indicator matrix is estimated by introducing ℓ1 sparsity norm coarsely. Second, we refine the estimated cluster indicator matrix by performing spectral rotation such that an optimal assignment for clustering can be learned. Compared with existing methods, we have the following merits: our method takes into account the global matrix reconstruction information and locality manifold information simultaneously. Therefore, global and locality information both are respected. Additionally, theoretical justification about the novel representation method is presented in this study. Comprehensive experiments demonstrate the effectiveness and efficiency of our method in comparison with the state-of-the-art clustering methods on six real-world image datasets.

Network coding remarkably improves network performance and transmission efficiency for multi-cast. Nevertheless, as its inherent defect, it is vulnerable to pollution attacks, bringing in a severe decrease of the network performance. In the proposed work, a novel approach is put forwarded, which can rapidly identify and isolate the malicious nodes from the networks as early as possible. On the one hand, we raise a secure infrastructure, on the other hand, we advocate a secure transmission protocol to verify every received packet. Theoretical analysis and experimental results demonstrate that the proposed scheme has the optimal performance in terms of security, network delay and network throughput.

The rapid development of information technology promotes the development and application of Telecare Information System (TMIS). However, TMIS also has security problems such as information leakage, false authentication, and key loss. In order to solve the safety problems of TMIS, this paper combines Physical Unclonable Function (PUF) and Elliptic Curve Cryptography (ECC) technology to propose an access control and authentication scheme suitable for TMIS. The proposed scheme uses PUF and compact PUF identity authentication models to implement secure mutual authentication between tag and server. The key information in the scheme is generated by PUF, which not only reduces the cost of algorithm design but also avoids the risk of information leakage and key loss. In addition, this article uses ECC technology to encrypt the PUF response information and random numbers, which can ensure that this data information will not be leaked to the attacker. Then through the ProVerif verification tool and security attribute analysis, it is proved that the scheme is safe in the face of major attacks. The comparative analysis results show that the proposed scheme has higher security and is more suitable for TMIS.

Selecting suitable web services based on the quality-of-service (QoS) is essential for developing high-quality service-oriented applications. A critical step in this direction is acquiring accurate, personalised QoS values of web services. As the number of web services is enormous and the QoS data are highly sparse, improving the accuracy of QoS prediction has become a challenging issue recently. In this study, we propose a novel QoS prediction model, called neural fusion matrix factorisation, wherein we combine neural networks and matrix factorisation to perform non-linear collaborative filtering for latent feature vectors of users and services. Moreover, we consider context bias and employ multi-task learning to reduce prediction error and improve the predicted performance. Furthermore, we conducted extensive experiments in a large-scale real-world QoS dataset, and the experimental results verify the effectiveness of our proposed method.

Online social networks (OSNs) are nowadays an important field of applications thanks to the recent surge in online interaction. However, the illegal disclosure of user's private data can cause damaging consequences and even threaten the safety of users' life. The privacy issues of OSNs have become a matter of great concern for many people. In recent years, there are some research works to address this privacy issue, yet they do not always focus on providing the normal social network services for users, such as data sharing, data retrieval and data access services. Therefore, it is a challenge to ensure the security of sensitive data while providing efficient and privacy-preserving social network services for users. In this paper, we propose a novel blockchain-based privacy-preserving framework for online social networks, called BPP. Combined blockchain and public-key cryptography technique, the BPP framework can achieve secure data sharing, data retrieving, and data accessing with fairness and without worrying about potential damage to users' interest. Specifically, based on blockchain and public key encryption with keyword search technique, a secure, fair and efficient keyword search algorithm is proposed, with which the BBP framework realises privacy preservation of user's query and then obtain accurate query results with assurance and without needing for any further verification operation in online social network. Finally, we implement a prototype of our framework and deploy it to a locally simulated network. The extensive experiments and security analysis demonstrate the security, efficacy and efficiency of our proposed framework.

Data credibility plays a key role in facilitating evidence-based decision making in organizations and governments (e.g., policy making). One of the key data sources is the Internet of Things (IoT) devices and systems, say within a fog environment. However, the increasing complexity and interconnectivity of such IoT and fog environments can result in security vulnerabilities (e.g., due to implementation errors or flaws in the underpinning devices or systems), which can be exploited to compromise the credibility of the data. Therefore, in this article, we propose a secure Blockchain-based scheme to guarantee the credibility of nodes and data and ensure data transmission security in the fog environment. We then demonstrate the feasibility of the proposed scheme using experiments.

Crowdsensing is regarded as a critical component of the Internet of Things (IoT) and has been widely applied in smart city services. Incentive mechanism design, data reliability evaluation, and privacy preservation are the research focuses of crowdsensing. However, most existing incentive mechanisms fail to protect data privacy and evaluate data credibility, simultaneously. Moreover, traditional privacy and reliability-aware incentive schemes are usually challenging to realize real-time reward distribution. To this end, we first point out a single-time slice of failure problem in real-time incentive mechanisms and propose a two-layer truth discovery model (TLTD) to resolve this problem. Then, a reliability-aware real-time incentive mechanism (RRIM) is designed based on the proposed TLTD. In order to evaluate data reliability in a privacy-preserving manner, we build a privacy-preserving truth discovery solution (PriTD) based on secure computation protocols. Finally, our proposed system [privacy and reliability-aware real-time incentive system for crowdsensing (PRICE)] integrating the aforementioned protocols realizes real-time reward distribution, data reliability evaluation, and privacy protection, simultaneously. Theoretical analysis and experimental evaluations on a synthetic and real-world data set demonstrate the feasibility and efficiency of the proposed PRICE.

With the rapid advancement of video and image processing technologies in the Internet of Things, it is urgent to address the issues in real-time performance, clarity, and reliability of image recognition technology for a monitoring system in foggy weather conditions. In this work, a fast defogging image recognition algorithm is proposed based on bilateral hybrid filtering. First, the mathematical model based on bilateral hybrid filtering is established. The dark channel is used for filtering and denoising the defogging image. Next, a bilateral hybrid filtering method is proposed by using a combination of guided filtering and median filtering, as it can effectively improve the robustness and transmittance of defogging images. On this basis, the proposed algorithm dramatically decreases the computation complexity of defogging image recognition and reduces the image execution time. Experimental results show that the defogging effect and speed are promising, with the image recognition rate reaching to 98.8% after defogging.

With the fast advancements of electronic chip technologies in the Internet of Things (IoT), it is urgent to address the copyright protection issue of intellectual property (IP) circuit resources of the electronic devices in IoT environments. In this article, a fast deep-reinforcement-learning (DRL)-based detection algorithm for virtual IP watermarks is proposed by combining the technologies of mapping function and DRL to preprocess the ownership information of the IP circuit resource. The deep$Q$-learning (DQN) algorithm is used to generate the watermarked positions adaptively, making the watermarked positions secure yet close to the original design, turning the watermarked positions secure. An artificial neural network (ANN) algorithm is utilized for training the position distance characteristic vectors of the IP circuit, in which the characteristic function of the virtual position for IP watermark is generated after training. In IP ownership verification, the DRL model can quickly locate the range of virtual watermark positions. With the characteristic values of the virtual positions in each lookup table (LUT) area and surrounding areas, the mapping position relationship can be calculated in a supervised manner in the neural network, as the algorithm realizes the fast location of the real ownership information in an IP circuit. The experimental results show that the proposed algorithm can effectively improve the speed of watermark detection as also reducing the resource overhead. Besides, it also achieves excellent performance in security.

The massive redundant data storage and communication in network 4.0 environments have issues of low integrity, high cost, and easy tampering. To address these issues, in this article, a secure data storage and recovery scheme in the blockchain-based network is proposed by improving the decentration, tampering-proof, real-time monitoring, and management of storage systems, as such design supports the dynamic storage, fast repair, and update of distributed data in the data storage system of industrial nodes. A local regenerative code technology is used to repair and store data between failed nodes while ensuring the privacy of user data. That is, as the data stored are found to be damaged, multiple local repair groups constructed by vector code can simultaneously yet efficiently repair multiple distributed data storage nodes. Based on the unique chain storage structure, such as data consensus mechanism and smart contract, the storage structure of blockchain distributed coding not only quickly repair the nearby local regenerative codes in the blockchain but also reduce the resource overhead in the data storage process of industrial nodes. Experimental results show that the proposed scheme improves the repair rate of multinode data by 9% and data storage rate increased by 8.6%, indicating to be promising with good security and real-time performance.

Industrial networks are complex and diverse. Among existing intrusion prevention systems available, several of them have problems such as low detection accuracy rate, high false positive (FP) rate, and low real-time performance for impersonation attacks. To address such issues, it is proposed in this article an industrial network intrusion detection algorithm based on multifeature data clustering optimization model, where the weighted distances and security coefficients of data are classified based on the priority threshold of data attribute feature for each node in the network, given that the data modules in the industrial network environment are diverse and easy to diagnose, restore, and rebuild. The proposed algorithm can effectively improve the detection rate and real-time performance of detecting abnormal behavior for the multifeature data in industrial networks. The novel features are twofold, to rapidly select a node with high-security coefficient as the cluster center, and match the multifeature data around the center into a cluster. Experimental results show that the proposed algorithm has good superiority in terms of detection rate and time compared to other algorithms. In the industrial network, the detection accuracy of abnormal data reaches 97.8%, and the FP of detection is decreased by 8.8%.

With the advancement and emergence of diverse network services in Internet of Vehicles (IoV), large volume of data are collected and stored, making data important properties. Data will be one of the most important commodities in the future blockchain-based IoV systems. However, efficiency challenges have been commonly found in blockchain-based data markets, which is mainly caused by transaction confirmation delays and the cold-start problems for new users. To address the efficiency challenges, we propose a secure, decentralized IoV data-trading system by exploiting the blockchain technology, and design an efficient debt-credit mechamism to support efficient data-trading in IoV. In the debt-credit mechanism, a vehicle with loan demand could loan from multivehicles by promising to pay interest and reward. In particular, we encourage loaning among vehicles by a motivation-based investing and pricing mechanism. We formulate a two-stage Stackelberg game to maximize the profits of borrower vehicle and lender vehicles jointly. In the first stage, the borrower vehicle set the interest rate and reward for the loan as its pricing strategies. In the second stage, the lender vehicles decide on their investing strategies. We apply backward induction to analyze the subgame perfect equilibrium at each stage for both independent and uniform pricing schemes. We also validate the existence and uniqueness of Stackelberg equilibrium. The numerical results illustrate the efficiency of the proposed pricing schemes.

The previous blockchain data transmission techniques in industrial Internet of Things (IoT) have low security, high management cost of the trading center, and big difficulty in supervision. To address these issues, this paper proposes a secure FaBric blockchain-based data transmission technique for industrial IoT. This technique uses the blockchain-based dynamic secret sharing mechanism. A reliable trading center is realized using the power blockchain sharing model, which can also share power trading books. The power data consensus mechanism and dynamic linked storage are designed to realize the secure matching of the power data transmission. Experiments show that the optimized FaBric power data storage and transmission has high security and reliability. The proposed technique can improve the transmission rate and packet receiving rate by 12% and 13%, respectively. Moreover, the proposed technique has good superiority in sharing management and decentralization.

The increasingly severe network security situation brings unanticipated challenges to mobile networking. Traditional HMM (Hidden Markov Model) based algorithms for predicting the network security are not accurate, and to address this issue, a weighted HMM based algorithm is proposed to predict the security situation of the mobile network. The multiscale entropy is used to address the low speed of data training in mobile network, whereas the parameters of HMM situation transition matrix are also optimized. Moreover, the autocorrelation coefficient can reasonably use the association between the characteristics of the historical data to predict future security situation. Experimental analysis on DARPA2000 shows that the proposed algorithm is highly competitive, with good performance in prediction speed and accuracy when compared to existing design.

ABSTRACT With the development and maturation of the wireless communication technologies, the wireless sensor networks have been widely applied in different environments to acquire specific information. The wireless medical sensor networks (WMSNs), as a professional application of the wireless sensor networks in medicine, have attracted more and more attention because of its potential in improving the quality of healthcare services. Through the WMSNs, the parameters of patients' vital signs can be gathered from the sensor nodes equipped on the body of the patients and then can be accessed by the healthcare professionals by using a mobile device. By reason of the open feature of wireless communication, how to guarantee secure communication becomes an important issue. On the other hand, because the vital signs parameters are sensitive to the patients' health status and no one wants to reveal it to the others except the healthcare professionals, the protection of patients' privacy becomes another key issue for WMSNs applications. User authentication protocol with anonymity is the most basic and commonly used method to resolve the security and privacy issues of WMSNs. Recently, He et al. proposed an enhanced authentication protocol for healthcare applications using WMSNs to protect the security and privacy problems. However, we find that their scheme is incorrect in authentication and session key agreement phase. Besides, their scheme has no wrong password detection mechanism, which will not only waste the unnecessary computation and communication costs, but also may deduce the denial of service problem. In this paper, the biometric is introduced as the third authentication factor, and a new user anonymous authentication protocol based on WMSNs is designed so as to remove the drawbacks of the protocol of He et al. Compared with previous protocols, the new presented protocol enhances the security and also keeps the computation efficiency. Copyright © 2015 John Wiley & Sons, Ltd.

Many watermarking techniques for intellectual property (IP) protection are not resilient to tampering or removal attacks, especially for field programmable gate array (FPGA)-based IP cores. If attacked, the damaged watermarks cannot provide sufficient evidence in front of a court. To address this issue, the authors present a signature restoration scheme. The thought of secret sharing is introduced to share the signature into small watermarks. These watermarks are encoded with Reed-Solomon (RS) codes and embedded into unused lookup tables (LUTs) of used slices. Unlike most of existing techniques, the proposed scheme can restore the signature only by extracting parts of watermarks. So, it is tolerant to some damaged watermarks caused by removal attacks. The experiments show that the proposed scheme incurs no extra hardware resource and timing overhead. The robustness against attacks is much better by comparing to other schemes.

Efficient mining of high-throughput data has become one of the popular themes in the big data era. Existing biology-related feature ranking methods mainly focus on statistical and annotation information. In this study, two efficient feature ranking methods are presented. Multi-target regression and graph embedding are incorporated in an optimization framework, and feature ranking is achieved by introducing structured sparsity norm. Unlike existing methods, the presented methods have two advantages: (1) the feature subset simultaneously account for global margin information as well as locality manifold information. Consequently, both global and locality information are considered. (2) Features are selected by batch rather than individually in the algorithm framework. Thus, the interactions between features are considered and the optimal feature subset can be guaranteed. In addition, this study presents a theoretical justification. Empirical experiments demonstrate the effectiveness and efficiency of the two algorithms in comparison with some state-of-the-art feature ranking methods through a set of real-world gene expression data sets.

Summary form only given. Named Data Networking (NDN) is an emerging future Internet architecture with an alternative communication paradigm. For NDN, name lookup, just like IP address lookup for TCP/IP, plays an important role in forwarding. However, performing Longest Prefix Matching (LPM) to NDN names is more challenging. Recently, Graphic Processing Units (GPUs) have been shown to be of value in supporting wire speed name lookup, but the latency resulted by batching and transferring names is not so encouraging. On the other hand, in the area of IP address lookup, FPGA is widely used to implement Static Radom Accessing Memory (SRAM)-based pipeline for fast lookup and controllable latency. Thus, in this paper, we study how to accelerate NDN name lookup using FPGA-based pipeline.

Recently, Graphic Processing Units (GPUs) have been shown to be of value in supporting wire-speed name lookup in Named Data Networking (NDN). However, due to the computing model on GPU, the lookup latency is not so encouraging. In this paper, we shift the focus from GPU to Field-Programmable Gate Arrays (FPGA). We highlight three key challenges in accelerating name lookup using FPGA, and then present a scalable approach to address them. In our approach, a hierarchical and compact data structure is proposed to represent the name trie, which achieves not only effective pipeline mapping but also high memory efficiency. Further, it is finally implemented as a linear pipeline on the FPGA platform, enabling both fast lookup speed and low lookup latency. The experimental results show that our approach gains a reduction of memory cost over 90% compared with the referred GPU-based solution. Besides, the lookup throughput of our approach is almost 2.4 times higher, and the latency is up to 3 orders of magnitude lower.

Gene selection based on microarray data, is highly important for classifying tumors accurately. Existing gene selection schemes are mainly based on ranking statistics. From manifold learning standpoint, local geometrical structure is more essential to characterize features compared with global information. In this study, we propose a supervised gene selection method called locality sensitive Laplacian score (LSLS), which incorporates discriminative information into local geometrical structure, by minimizing local within-class information and maximizing local between-class information simultaneously. In addition, variance information is considered in our algorithm framework. Eventually, to find more superior gene subsets, which is significant for biomarker discovery, a two-stage feature selection method that combines the LSLS and wrapper method (sequential forward selection or sequential backward selection) is presented. Experimental results of six publicly available gene expression profile data sets demonstrate the effectiveness of the proposed approach compared with a number of state-of-the-art gene selection methods.