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arXiv 2024年

作者： Wu, Zhiyuan Sun, Sheng Wang, Yuwei Liu, Min Xu, Ke Pan, Quyang Gao, Bo Wen, Tian The State Key Lab of Processers Institute of Computing Technology Chinese Academy of Sciences Beijing China The University of Chinese Academy of Sciences Beijing China The Zhongguancun Laboratory Beijing China The Department of Computer Science and Technology Tsinghua University Beijing China The School of Computer Science and Technology The Engineering Research Center of Network Management Technology for High-Speed Railway of Ministry of Education Beijing Jiaotong University Beijing China

The rise of End-Edge-Cloud Collaboration (EECC) offers a promising paradigm for Artificial Intelligence (AI) model training across end devices, edge servers, and cloud data centers, providing enhanced reliability and reduced latency. Hierarchical Federated Learning (HFL) can benefit from this paradigm by enabling multi-tier model aggregation across distributed computing nodes. However, the potential of HFL is significantly constrained by the inherent heterogeneity and dynamic characteristics of EECC environments. Specifically, the uniform model structure bounded by the least powerful end device across all computing nodes imposes a performance bottleneck. Meanwhile, coupled heterogeneity in data distributions and resource capabilities across tiers disrupts hierarchical knowledge transfer, leading to biased updates and degraded performance. Furthermore, the mobility and fluctuating connectivity of computing nodes in EECC environments introduce complexities in dynamic node migration, further compromising the robustness of the training process. To address multiple challenges within a unified framework, we propose End-Edge-Cloud Federated Learning with Self-Rectified Knowledge Agglomeration (FedEEC), which is a novel EECC-empowered FL framework that allows the trained models from end, edge, to cloud to grow larger in size and stronger in generalization ability. FedEEC introduces two key innovations: (1) Bridge Sample Based Online Distillation Protocol (BSBODP), which enables knowledge transfer between neighboring nodes through generated bridge samples, and (2) Self-Knowledge Rectification (SKR), which refines the transferred knowledge to prevent suboptimal cloud model optimization. The proposed framework effectively handles both cross-tier resource heterogeneity and effective knowledge transfer between neighboring nodes, while satisfying the migration-resilient requirements of EECC. Extensive experiments on three datasets demonstrate that FedEEC achieves significantly hi

关键词： Federated learning

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Securing Vehicular Ad-Hoc networks: Detection and Identification Algorithm of MITM Attacks

Securing Vehicular Ad-Hoc Networks: Detection and Identifica...

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Electrical engineering (ICEENG), 12th International Conference on

作者： Nourhan El. Alashkar Bassem Mokhtar Tawfik Ismail Wireless Intelligent Networks Center (WINC) Nile University Giza Egypt Computer and Network Engineering Department College of Information Technology UAE University Electrical Engineering Department Faculty of Engineering Alexandria University College of Engineering Taibah University Madinah Saudi Arabia Department of EAL National Institute of Laser Enhanced Sciences Cairo University Giza Egypt

ISBN: (数字)9798350343427

ISBN: (纸本)9798350343434

The communication between vehicles (V2V) and vehicles to infrastructure (V2I) plays a crucial role in the Vehicular Ad-hoc network (VANET). Its aim is to enhance road safety, reduce traffic accidents, and improve traffic efficiency. This necessitates the exchange of critical information among vehicles, static roadside units (RSUs), and traffic control centers. This integration gives rise to Cooperative Intelligent Transport Systems (C-ITS). However, the latency in wireless communication can have adverse effects on safety. To address this, vehicle advancements have led to the formation of platoons, enabling better latency control and safer driving. Nonetheless, platooning also exposes vehicles to significant risks, such as Man-In-The-Middle (MITM) attacks. These attacks can result in message delays, juggling, and drop-offs, particularly affecting time-sensitive messages like accident notifications. This research paper focuses on analyzing the impact of MITM attacks that cause message delays and proposes measures to detect and sever connections with malicious vehicles. By establishing secure paths, this solution aims to enhance VANET security and optimize network performance.

关键词： Wireless communication Vehicular ad hoc networks Traffic control Road safety Delays Safety Security

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Light-Field Image Multiple Reversible Robust Watermarking Against Geometric Attacks

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Wang, Chunpeng Zhang, Qinghua Wang, Xiaoyu Zhou, Linna Li, Qi Xia, Zhiqiu Ma, Bin Shi, Yun-Qing Qilu University of Technology (Shandong Academy of Sciences) Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan 250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan 250353 China Beijing University of Posts and Telecommunications School of Cyberspace Security Beijing 100876 China New Jersey Institute of Technology Department of Electrical and Computer Engineering Newark 07102 NJ United States

Light-field (LF) images contain rich visual information and have broader application scenarios than traditional images. However, their complex structure also makes their copyright protection more challenging. Currently, there are few watermarking schemes suitable for LF images, and most of them fail to restore the original image after embedding the watermark. In addition, geometric attacks remain a difficult problem in the field of LF image watermarking. In this study, we propose a multiple reversible robust LF image watermarking scheme based on code division multiplexing (CDM) and quaternion polar harmonic Fourier moments (QPHFMs). This scheme embeds multiple identical watermarks into the LF macro-pixel image, and the compensation information for information loss caused by watermark embedding is reversibly embedded into the LF sub-aperture images. The watermark can be extracted and the original LF image can be fully recovered if the image has not been attacked. The watermark can be extracted to verify the copyright ownership of the LF image even when the image has been attacked. Experimental results demonstrate that the proposed watermarking scheme is resistant to various attacks and exhibits strong robustness. © 2004-2012 IEEE.

关键词： Code division multiplexing Light-field images Multiple watermarking Quaternion polar harmonic Fourier moments Reversible data hiding Robust watermarking

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Reducing network Distance Measurement Overhead: A Tensor Completion Solution With a New Minimum Sampling Bound

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IEEE/ACM Transactions on networking 2024年

作者： Tian, Jiazheng Xie, Kun Wang, Xin Wen, Jigang Xie, Gaogang Cao, Jiannong Liang, Wei Li, Kenli Hunan University College of Computer Science and Electronics Engineering Changsha410082 China Ministry of Education Key Laboratory of 'Fusion Computing of Supercomputing and Artificial Intelligence' Changsha410082 China State of New York University at Stony Brook Department of Electrical and Computer Engineering Stony BrookNY11794 United States Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411199 China Chinese Academy of Sciences Computer Network Information Center Beijing100080 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing101408 China The Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong

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. © 1993-2012 IEEE.

关键词： Distance measurement

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Active Power Control for Off-Shore Wind Turbines Against FDI Attack

Active Power Control for Off-Shore Wind Turbines Against FDI...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Xuguo Jiao Hao Luo Liuyang Jiang Lin Wang Xin Wang Zuqiang Huang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China School of Information and Control Engineering Qingdao University of Technology Qingdao China Zhejiang Windey Co. Ltd. Hangzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Academy of Sciences Qilu University of Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Civil Engineering Zhengzhou University Zhengzhou China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

Recently, offshore wind power has rapidly developed due to its low environmental interference and abundant wind resources. However, due to its remote geographical location and high demand for communication infrastructure, off-shore wind turbines (OWTs) are vulnerable to potential threats from false data injection (FDI) attack. FDI attack can interfere with active power control strategies by tampering with the rotor speed data measured by sensors and pose a threat to the stability of the power grid. To ensure that OWTs can accurately track the active power instructions set by wind farm's control center under FDI attack, this paper proposes a rotor speed control (RSC) strategy based on Kalman filtering technology (RSC- KF). This strategy can estimate the rotor speed of the wind turbine in real-time, and then a residual detection and compensation mechanism is designed to effectively detect and correct attacked rotor speed information caused by FDI attack. Further, based on the corrected rotor speed, the RSC method is developed, which can flexibly switch control strategies according to different rotor speed values, ensuring the efficient operation of OWTs under different wind speed conditions. The proposed approach's effectiveness is proved via simulations conducted on the OpenFAST platform.

关键词： Filtering Velocity control Power control Rotors Power system stability Stability analysis Real-time systems Wind turbines Kalman filters Cyberattack

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A hybrid uplink-downlink secure transmission scheme for UAV-aided coordinated multi-point networks

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Digital Communications and networks 2025年第03期 925-936页

作者： Xin Fan Guangkai Li Jianqiang Li Yue Wang Chuanwen Luo Yi Hong Ting Chen Yan Huo School of Information Science and Technology Beijing Forestry University The Engineering Research Center for Forestry-oriented Intelligent Information Processing of National Forestry and Grassland Administration National Computer Network Emergency Response Technical Team/Coordination Center of China Department of Computer Science Georgia State University School of Computer Science and Engineering University of Electronic Science and Technology of China School of Electronics and Information Engineering Beijing Jiaotong University

Recognized as a pivotal facet in Beyond Fifth-Generation(B5G) and the upcoming Sixth-Generation(6G) wireless networks, Unmanned Aerial Vehicle(UAV) communications pose challenges due to limited capabilities when serving as mobile base stations, leading to suboptimal service for edge users. To address this, the collaborative formation of Coordinated Multi-Point(Co MP) networks proves instrumental in alleviating the issue of the poor Quality of Service(Qo S) at edge users in the network periphery. This paper introduces a groundbreaking solution, the Hybrid Uplink-Downlink Non-Orthogonal Multiple Access(HUD-NOMA) scheme for UAV-aided Co MP networks. Leveraging network coding and NOMA technology, our proposed HUD-NOMA effectively enhances transmission rates for edge users, notwithstanding a minor reduction in signal reception reliability for strong signals. Importantly, the system's overall sum rate is elevated. The proposed HUD-NOMA demonstrates resilience against eavesdroppers by effectively managing intended interferences without the need for additional artificial noise injection. The study employs a stochastic geometry approach to derive the Secrecy Outage Probability(SOP) for the transmissions in the Co MP network, revealing superior performance in transmission rates and lower SOP compared to existing methods through numerical verification. Furthermore, guided by the theoretical SOP derivation, this paper proposes a power allocation strategy to further reduce the system's SOP.

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RNN-Based Demand Awareness in Smart Library Using CRFID

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China Communications 2020年第5期17卷 284-294页

作者： Ruiqin Bai Jumin Zhao Dengao Li Xiaoyu Lv Qiang Wang Biaokai Zhu College of Information and Computer Taiyuan University of TechnologyJinzhong 030600China College of Data Science Taiyuan University of TechnologyJinzhong 030600China Technology Research Center of Spatial Information Network Engineering of Shanxi Jinzhong 030600China

To provide more intelligence service in the smart library, we need to better perceive the reader’s preferences. In addition to perceiving online records based on readers’ search history and borrowing records, advanced information technologies give us more chance to perceive the behavior of readers in the actual reading process and further discover the need for reading. In this paper, we use CRFID and RNN deep learning network to recognize book motions in the reading process, so as to judge readers’ need degree for the book, which can provide a basis for library book purchases and readers personalized service. In order to improve the recognition accuracy, we use the RSS as well as acceleration magnitude gathered from CRFID as the input data for RNN, and design a new encoding scheme. We trained and tested the deep learning network using real-world data, recorded during actual reading in our lab environment which mimics a typical reading room, from the experimental results, we conclude that our approach is feasible to recognize different reading phase to perceiving the needs of the readers.

关键词： demand awareness detection of phases CRFID RNN smart library

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MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks

arXiv

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arXiv 2023年

作者： Zhou, Yuyang Cheng, Guang Chen, Zongyao Yu, Shui School of Cyber Science and Engineering Southeast University Purple Mountain Laboratories Jiangsu Province Engineering Research Center of Security for Ubiquitous Network Nanjing211189 China School of Computer Science University of Technology Sydney UltimoNSW2007 Australia

Machine learning (ML) has gained significant adoption in Android malware detection to address the escalating threats posed by the rapid proliferation of malware attacks. However, recent studies have revealed the inherent vulnerabilities of ML-based detection systems to evasion attacks. While efforts have been made to address this critical issue, many of the existing defensive methods encounter challenges such as lower effectiveness or reduced generalization capabilities. In this paper, we introduce a novel Android malware detection method, MalPurifier, which exploits adversarial purification to eliminate perturbations independently, resulting in attack mitigation in a light and flexible way. Specifically, MalPurifier employs a Denoising AutoEncoder (DAE)-based purification model to preprocess input samples, removing potential perturbations from them and then leading to correct classification. To enhance defense effectiveness, we propose a diversified adversarial perturbation mechanism that strengthens the purification model against different manipulations from various evasion attacks. We also incorporate randomized "protective noises" onto benign samples to prevent excessive purification. Furthermore, we customize a loss function for improving the DAE model, combining reconstruction loss and prediction loss, to enhance feature representation learning, resulting in accurate reconstruction and classification. Experimental results on two Android malware datasets demonstrate that MalPurifier outperforms the state-of-the-art defenses, and it significantly strengthens the vulnerable malware detector against 37 evasion attacks, achieving accuracies over 90.91%. Notably, MalPurifier demonstrates easy scalability to other detectors, offering flexibility and robustness in its *** Codes 62 © 2023, CC BY.

关键词： Android malware

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Robust and Low-Complexity Secure Beamforming for Distributed RIS-Aided mmWave Systems with Limited Feedback

Robust and Low-Complexity Secure Beamforming for Distributed...

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International Conference on computer and Communications (ICCC)

作者： Qingqing Tu Zheng Dong Xianbing Zou Ning Wei Yan Li National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu China National Computer Network Emergency Response Technical Team/Coordination Center of China Chengdu China School of Information Science and Engineering Shandong University Qingdao China Shandong Massive Information Technology Research Institute Jinan China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

This paper presents a robust and low-complexity beamforming design to enhance physical layer security in mmWave systems with distributed RIS under imperfect CSI. This design jointly optimizes feedback bit allocation with active and passive beamforming to adapt to varying channel conditions. To address this complex and non-convex optimization, we propose an alternating optimization algorithm that decouples the problem into subproblems, applying appropriate methods iteratively at each level to obtain the optimal solution. Simulations demonstrate the robustness of the proposed algorithm in mitigating the effects of imperfect CSI, providing improved security performance and resource efficiency compared to benchmarks, particularly with limited feedback bit resources or in challenging spatial conditions.

关键词： Adaptation models Uncertainty Array signal processing Bit rate Reconfigurable intelligent surfaces Physical layer security Benchmark testing Robustness Millimeter wave communication Optimization

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HANE-SHC: Heterogeneous attributed network embedding with structural homophily contents

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Applied Intelligence 2025年第10期55卷 1-40页

作者： Fu, Yue Zhao, Shuliang Wu, Yongliang College of Information Engineering Hebei GEO University Hebei China College of Computer and Cyber Security Hebei Normal University Hebei China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Hebei China Hebei Provincial Key Laboratory of Network and Information Security Hebei China School of Information Science and Technology Shijiazhuang Tiedao University Hebei China

Heterogeneous attributed networks (HANs) are ubiquitous in real-world, spanning domains from the academic network to the movie network. Heterogeneous attributed network embedding (HANE), a promising analytical technique in recent years, aims to generate low-dimensional dense vectors containing network structure and attribute content. Compared to the embedding approaches that only consider topology structure, researchers embed the network by leveraging both network topology and content to improve the quality of embedding vectors. However, when two nodes with a connected edge may have a small content similarity value, while two nodes without a connected edge may have a large content similarity value, so, in this situation, it is not appropriate to utilize the network topology and nodes’ content to generate embedding vectors. To overcome this challenge, we propose a novel approach of Heterogeneous Attributed networks Embedding based on Structural Homophily Contents, abbreviated as HANE-SHC, which is based on convolutional neural networks to leverage both the network structure and the content of nodes connected with the embedded node to generate the representation vectors of the embedded node. To our knowledge, this is the first study that proposes the Structural Homophily Contents to enhance the heterogeneous attributed network embedding. Through comprehensive evaluations on downstream tasks including clustering, classification, link prediction, and visualization, the experimental results demonstrate that the HANE-SHC outperforms the baselines. On the artificial synthetic networks with various network structure-content dissimilarity scores, the HANE-SHC model consistently produces high-quality embedding vectors regardless of the network dissimilarity scores between structure and content, significantly outperforming existing approaches.

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