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6th International Conference on Machine Learning for Cyber Security, ML4CS 2024

作者： Wang, Qianshi Xu, Chongjun Yang, Huayu Zhai, Xilin Zhang, Hua State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Zhejiang Branch of National Computer Network Emergency Response Technical Team/Coordination Center of China Hangzhou China

ISBN: (纸本)9789819645657

Intelligent in-car operating system integrates various web platforms including browsers, therefore facing serious issues of malicious web content. Current detection models perform poorly in Internet of Vehicles (IoV) environment due to simple model structures and lack of data samples, which make models difficult to learn and extract multi-level and fine-grained sample features. In this paper, we propose a novel few-shot model, CarMaNet, to detect and classify malicious websites in IoV environment based on website images. We import inception module into Relation networks, which enhance the ability of CarMaNet to learn multi-level features of images. At the same time, we design a feature pipeline featuring adaptive recalibration between the embedding module and the relation module based on adaptive attention mechanism (FCAA), enhancing the model's ability to learn fine-grained features. In few-shot experiments on three dataset of pornography, gambling and counterfeit websites, CarMaNet demonstrates greater performance than current models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Websites

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Backdoor Attack on Android Malware Classifiers Based on Genetic Algorithms 6th

Backdoor Attack on Android Malware Classifiers Based on Gene...

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6th International Conference on Machine Learning for Cyber Security, ML4CS 2024

作者： Cai, Zhenghua Wang, Yongji Zhang, Hua Qiao, Lei Wang, Huawei Zhang, Chi State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Beijing Institute of Control Engineering Beijing China Information Center of China North Industries Group Corporation Beijing China

ISBN: (纸本)9789819645657

The rapid rise of malware challenges traditional detection methods due to code obfuscation and polymorphism. While machine learning classifiers offer quick detection and can identify complex malicious features, they are susceptible to backdoor attacks. We introduce GAT, a genetic algorithm-based approach for generating effective and stealthy Android backdoors. Using the SHAP interpretability tool, we first select efficient features as primary backdoors. A fitness function then enables iterative optimization through a genetic algorithm. Additionally, we propose a method to integrate backdoor features into the source code, maintaining functionality while facilitating attacks on Android classifiers in real data outsourcing scenarios. Our evaluation of the Drebin and Mamadroid malware detectors in data outsourcing scenarios indicates that an attack success rate exceeding 70% can be achieved with only 5% poisoned samples and a minimal number of trigger features, while keeping the false positive rate below 10% and the label flipping rate below 30%. Additionally, the performance degradation of the classifiers remains within 5%. This work provides new insights into backdoor attack methodologies in Android malware classifiers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Android malware

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Joint Deep Adversarial Semantic Decomposition Scheme for Model Division Multiple Access in IoT

Joint Deep Adversarial Semantic Decomposition Scheme for Mod...

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IEEE Conference on Wireless Communications and networking

作者： Di Wu Zhi Zhang Yue Meng Xiaoqi Qin Yiming Liu State Key Laboratory of Networking Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

To support the large-scale connectivity of massive intelligent devices in the Internet of Things (IoT) scenario, in this paper, an uplink multi-user semantic communication system based on model division multiple access (MDMA) is investigated. A model resource pool consisting of multiple mutually exclusive semantic models is built as one new kind of access resource, and a global optimization problem is formulated to make the mapping of different semantic models have stronger mutual exclusion. To solve this problem, a joint deep adversarial semantic decomposition (JDASD) algorithm is proposed to enhance the ability of the semantic models to eliminate interference from other devices. Simulation results demonstrate that the proposed JDASD algorithm achieves higher anti-interference capability in the MDMA system than the traditional independent training scheme applied in most works, showing the advantages of the proposed scheme for the IoT scenario with massive devices.

关键词： Training Performance evaluation Simulation Interference Semantic communication Generative adversarial networks Internet of Things Uplink Optimization

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PGPFL: A Parameter Guard-Based Efficient Personalized Federated Learning Framework with Local Differential Privacy

PGPFL: A Parameter Guard-Based Efficient Personalized Federa...

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IEEE Conference on Wireless Communications and networking

作者： Zhipeng Gao Leyu Han Qian Luo Ze Chai Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Data heterogeneity and privacy leakage pose inevitable challenges for federated learning in real-world scenarios. These obstacles have motivated the development of numerous personalized federated learning algorithms aimed at acquiring personalized models for individual clients. However, previous work in achieving personalization has typically proposed solutions from the perspective of the entire model or from the level of layer within the network structure. Diverging from them, we introduce a novel perspective, scrutinizing the personalization issue with finer granularity at the parameter level. In this paper, we propose PGPFL, a novel personalized federated learning framework incorporating differential privacy, which rapidly achieves the required local accuracy while providing privacy protection. PGPFL introduces configuring a guard for each parameter of the local network to constrain local updates for personalized learning, and calculate the importance value of each parameter of the current model during the local training process to determine the private parts. Additionally, PGPFL propose a valid client selection mechanism based on the response ratio and local accuracy to decrease single-round training time, enhancing training efficiency. Extensive experiments conducted under various simulated heterogeneous environments demonstrate that, in comparison with existing algorithms, our algorithm improves both average local accuracy and training efficiency.

关键词： Training Differential privacy Privacy Accuracy Laplace equations Federated learning Noise Protection Edge computing Convergence

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Linear Array Motion-Based Time-Varying Channel Estimation for Three-Dimensional Millimeter-Wave Systems

Linear Array Motion-Based Time-Varying Channel Estimation fo...

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IEEE Conference on Wireless Communications and networking

作者： Ruizhe Zhao Zhi Zhang Jianhong Chu Tianshu Su State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Using linear arrays for compressive sensing-based (CS) three-dimensional (3D) millimeter-wave (mmWave) channel estimation presents significant challenges due to the dimensional limitations of the array aperture, which prevents direct sparse 3D channel representation. Fortunately, the emergence of array motion provides a chance for that. In this paper, by exploring the motion of linear arrays in time-varying channels and identifying their similarities to planar arrays, we propose a method for sparse representation of 3D channels based on linear arrays. Utilizing the sparsity, a two-step sparse channel recovery algorithm is presented. Initially, the on-grid and off-grid approaches are employed to efficiently and accurately obtain the angles of arrival (AoAs). Subsequently, path gains are estimated based on these AoAs. Simulation results show that the proposed method can effectively estimate 3D time-varying mmWave channels using a linear array with only a small number of pilots.

关键词： Three-dimensional displays Sparse approximation Simulation Millimeter wave technology Channel estimation Estimation Time-varying channels Sensors Millimeter wave communication Time-varying systems

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A Novel 3D Non-Stationary 6G Wideband Geometry-Based Channel Model for AIRS-Assisted Maritime Communication

A Novel 3D Non-Stationary 6G Wideband Geometry-Based Channel...

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IEEE Conference on Wireless Communications and networking

作者： Jiatong Wu Nan Ma Kaiheng Zhang Ke Peng State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In maritime scenarios, the quality of ground-to-sea communication can be affected by the blockage of terrains and the long communication distances. By introducing aerial intelligent reflecting surface (AIRS), it is possible to improve the communication range as well as the communication stability flexibly with relatively small costs. In this paper, we propose a new three-dimensional (3D) non-stationary wideband geometry-based stochastic model (GBSM) for AIRS-assisted ground-to-sea communication. In this model, the irregular sea surface fluctuation and the waveguide effects due to complex maritime climatic conditions are analyzed, the impulse response of the channel is computed and the relevant statistical properties such as the correlation function and the Doppler frequency shift are derived. Additionally, the 3D motion of the ship is simulated, the limitations of scatterer distribution in maritime environments are considered. Based on simulations, the enhancement of the channel by the AIRS phase design, the impact of relative motion between the AIRS and the ship are analyzed. Simulation results demonstrate that the proposed model accurately characterizes the AIRS-assisted ground-to-sea communication channel. With proper phase design and quantization, AIRS can effectively enhance channel stability and mitigate the Doppler effect.

关键词： Sea surface Analytical models Three-dimensional displays Surface waves Atmospheric modeling Computational modeling Doppler effect Marine vehicles Wideband Maritime communications

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HR-SKGs: Hyper-Relational Semantic Knowledge Graphs for Multi-hop Reading Comprehension

HR-SKGs: Hyper-Relational Semantic Knowledge Graphs for Mult...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yingying Zhang Bo Cheng Yuli Chen State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multi-hop Reading Comprehension (RC) is a challenging task that requires models to integrate dispersed information and perform multi-step reasoning. In recent years, graph-based methods have shown promising performance on multi-hop RC tasks. However, they often overemphasize nodes and connection strengths, neglecting the rich semantic information carried by the node relations. To address these limitations, we propose a novel Hyper-Relational Semantic Knowledge Graphs (HR-SKGs) model, which constructs hyper-relational graphs by integrating node, relation, and topic information, capturing semantic features more precisely. Additionally, we introduce a topic-aware mechanism during paragraph retrieval to effectively filter paragraphs relevant to the question. In the graph reasoning phase, we propose a hyper-relational graph attention mechanism that integrates relation-aware and topic-aware approaches to optimize information flow and reasoning between nodes. Experiments demonstrate that our approach achieves significant performance improvements on the HotpotQA dataset, and outperforms other graph-based methods.

关键词： Knowledge engineering Attention mechanisms Semantics Knowledge graphs Spread spectrum communication Signal processing Information filters Cognition Acoustics Speech processing

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Self-Adaptive Dynamic In-Band Network Telemetry Orchestration for Balancing Accuracy and Stability

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IEEE Transactions on Network and Service Management 2025年第2期22卷 1514-1530页

作者： Ouyang, Tianhao Yao, Haipeng He, Wenji Mai, Tianle Wang, Fu Yu, F. Richard Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Beijing Institute of Technology School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing University of Posts and Telecommunications School of Electronic and Engineering Beijing100876 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

In-band network telemetry (INT) is an emerging network measurement technique that offers real-time and fine-grained visualization capabilities for networks. However, the utilization of INT for network measurement introduces additional overheads to the network. The process of data collection consumes extra bandwidth resources, and adjustments to the data collection scheme can impact network stability. Additionally, the INT orchestration scheme requires adaptation to dynamics in the network to improve measurement accuracy. Therefore, striking a balance between accuracy and stability becomes a critical problem. In this paper, our focus lies in the trade-off between measurement accuracy and network stability. We consider the long-term orchestration of multiple telemetry tasks, rationally deploying distinct telemetry tasks to different application flows. To address the challenge, we propose a self-adaptive Dynamic INT Orchestration scheme, D-INTO. Specifically, we formulate a stochastic optimization problem for dynamic INT orchestration. Then we employ Lyapunov optimization to decouple the stochastic optimization problem and use surrogate Lagrangian relaxation to construct a polynomial-time approximation algorithm. Theoretical analysis and experimental results demonstrate that our proposed D-INTO outperforms existing schemes in terms of adaptability to the network dynamics. © 2004-2012 IEEE.

关键词： Polynomial approximation

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SeqBalance: Congestion-Aware Load Balancing with no Reordering in Data Center Networks

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IEEE Internet of Things Journal 2025年

作者： Luo, Huimin Zhang, Jiao Yu, Mingxuan Pan, Yongchen Pan, Tian Huang, Tao BUPT State Key Laboratory of Networking and Switching Technology Beijing100876 China Purple Mountain Laboratories Nanjing211111 China

With the rapid development of the Internet of Things (IoT), an increasing amount of sensor data generated by IoT applications has been transferred to data center networks for storage and data analysis. Remote Direct Memory Access (RDMA) is widely used in data center networks because of its high performance. However, due to the characteristics of RDMA's retransmission strategy, current load balancing schemes for data center networks are unsuitable for RDMA. In this paper, we propose SeqBalance, a load balancing framework designed for RDMA. SeqBalance implements fine-grained load balancing for RDMA through a reasonable design and does not cause reordering problems. SeqBalance detects link congestion at the switch by sensing ECN signals and link utilization, and guides routing decisions accordingly. SeqBalance's designs are all based on existing commercial RNICs and commercial programmable switches, so they are compatible with existing data center networks. We have implemented SeqBalance Shaper for fine-grained sub-flow splitting in Mellanox CX-6 RNIC and implemented routing decisions in Intel Tofino P4 programmable switch. The results of hardware testbed experiments and large-scale simulations show that compared with existing load balancing schemes, SeqBalance improves 24.7% and 15.9% on average FCT and 99th-percentile FCT. © 2025 IEEE.

关键词： Steganography

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Joint DOA and TOA Estimation for Moving MIMO Radar Based on OFDM-ISAC Signals

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IEEE Transactions on Vehicular technology 2025年

作者： Su, Tianshu Zhang, Zhi Chu, Jianhong Zhao, Ruizhe Huang, Yuzhen State Key Laboratory of Networking and Switching Technology Beijing100876 China Academy of Military Sciences of PLA Beijing100091 China

Integrated sensing and communication (ISAC) has emerged as a critical enabling technology for future communication networks because of its higher spectrum efficiency and lower hardware cost. Considering arrays will possibly be mounted on moving platforms in mobile ISAC scenarios, in this paper, array motion is introduced into monostatic multiple input multiple output (MIMO) radar based on orthogonal frequency division multiplexing ISAC (OFDM-ISAC) waveforms to improve the array aperture. Specifically, a time-frequency dual expansion (TFDE) framework, which exploits the channel state information (CSI) containing the entirety of the motion samplings and subcarrier information, is designed to construct a synthetic virtual array with an expanded aperture. With this TFDE framework, two array configurations are proposed and then analyzed, which can generate more virtual elements than the stationary MlMO array. Subsequently, taking into account the issues of parameter coupling, a reduced-complexity multiple signal classification with two-step decoupling (TSD-RC MUSIC) algorithm is pertinently designed to estimate the direction of arrival (DOA) and time of arrival (TOA) of targets while ensuring high estimation accuracy and low complexity. Numerical simulation verifies the effectiveness and superiority of the proposed TFDE framework and TSD-RC MUSIC algorithm under the proposed array configurations. © 1967-2012 IEEE.

关键词： Orthogonal frequency division multiplexing

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