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30th Annual network and Distributed System security Symposium, NDSS 2023

作者： Zhou, Chunyi Gao, Yansong Fu, Anmin Chen, Kai Dai, Zhiyang Zhang, Zhi Xue, Minhui Zhang, Yuqing School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Science China Data61 CSIRO Syndey Australia National Computer Network Intrusion Protection Center University of Chinese Academy of Science China

ISBN: (纸本)1891562835

—Federated learning (FL) trains a global model across a number of decentralized users, each with a local dataset. Compared to traditional centralized learning, FL does not require direct access to local datasets and thus aims to mitigate data privacy concerns. However, data privacy leakage in FL still exists due to inference attacks, including membership inference, property inference, and data inversion. In this work, we propose a new type of privacy inference attack, coined Preference Profiling A ttack (PPA), t hat accurately profiles t he p rivate p references o f a l ocal u ser, e .g., m ost liked (disliked) items from the client’s online shopping and most common expressions from the user’s selfies. I n g eneral, P PA can profile t op-k (i.e., k = 1, 2, 3 a nd k = 1 i n p articular) preferences contingent on the local client (user)’s characteristics. Our key insight is that the gradient variation of a local user’s model has a distinguishable sensitivity to the sample proportion of a given class, especially the majority (minority) class. By observing a user model’s gradient sensitivity to a class, PPA can profile the sample proportion of the class in the user’s local dataset, and thus the user’s preference of the class is exposed. The inherent statistical heterogeneity of FL further facilitates PPA. We have extensively evaluated the PPA’s effectiveness using four datasets (MNIST, CIFAR10, RAF-DB and Products-10K). Our results show that PPA achieves 90% and 98% top-1 attack accuracy to the MNIST and CIFAR10, respectively. More importantly, in real-world commercial scenarios of shopping (i.e., Products-10K) and social network (i.e., RAF-DB), PPA gains a top-1 attack accuracy of 78% in the former case to infer the most ordered items (i.e., as a commercial competitor), and 88% in the latter case to infer a victim user’s most often facial expressions, e.g., disgusted. The top-3 attack accuracy and top-2 accuracy is up to 88% and 100% for the Products-10K and RAF-DB, re

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MISP: An Efficient Quantum-resistant Misbehavior Preventing Scheme with Self-enforcement for Vehicle-to-Everything

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Chen, Ying He, Debiao Bao, Zijian Wang, Huaqun Luo, Min Wuhan University School of Cyber Science and Engineering Wuhan430072 China Key Laboratory of Computing Power Network & Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Nanjing University of Posts and Telecommunications College of Computer Nanjing210023 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network & Information System Security Jinan250014 China

In the Vehicle-to-Everything (V2X) communication system, the presence of ambiguous warnings significantly increases the risk of severe accidents, posing a substantial threat to the safety of autonomous driving. It is crucial to detect such confusing warnings to avoid danger. Existing solutions to address this issue heavily rely on trust entities or are constructed based on number theory assumptions, leading to low efficiency and vulnerability to quantum attacks. In this paper, we leverage the double authentication-preventing signature scheme (DAPS) to present a revocable identity-based double-authentication preventing signature scheme (RIDAPS) and provides an instantiation from lattice. Furthermore, we propose a post-quantum secure misbehavior preventing scheme (Misp) based on our RIDAPS scheme. We give a detailed proof in the random oracle model (ROM) to demonstrate that our contribution achieves security requirements. Additionally, the efficiency evaluation results demonstrate that our scheme is suitable to be applied in V2X. © 2004-2012 IEEE.

关键词： Authentication

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Length matters:Scalable fast encrypted internet traffic service classification based on multiple protocol data unit length sequence with composite deep learning

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Digital Communications and networks 2022年第3期8卷 289-302页

作者： Zihan Chen Guang Cheng Ziheng Xu Shuyi Guo Yuyang Zhou Yuyu Zhao School of Cyber Science and Engineering Southeast UniversityNanjing211189China Key Laboratory of Computer Network and Information Integration Ministry of EducationNanjing211189China Purple Mountain Laboratories Nanjing211111China

As an essential function of encrypted Internet traffic analysis,encrypted traffic service classification can support both coarse-grained network service traffic management and security ***,the traditional plaintext-based Deep Packet Inspection(DPI)method cannot be applied to such a ***,machine learning-based existing methods encounter two problems during feature selection:complex feature overcost processing and Transport Layer security(TLS)version *** this paper,we consider differences between encryption network protocol stacks and propose a composite deep learning-based method in multiprotocol environments using a sliding multiple Protocol Data Unit(multiPDU)length sequence as features by fully utilizing the Markov property in a multiPDU length sequence and maintaining suitability with a TLS-1.3 *** experiments show that both Length-Sensitive(LS)composite deep learning model using a capsule neural network and LS-long short time memory achieve satisfactory effectiveness in F1-score and *** to faster feature extraction,our method is suitable for actual network environments and superior to state-of-the-art methods.

关键词： Encrypted internet traffic Encrypted traffic service classification Multi PDU length sequence Length sensitive composite deep learning TLS-1.3

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Multi-loss joint cross-modal pedestrian re-identification method fused with grayscale and RGA 6

Multi-loss joint cross-modal pedestrian re-identification me...

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6th International Conference on Electronic information Technology and computer Engineering, EITCE 2022

作者： Yuan, Ruichao Hu, Xiaoguang Dong, Anyang Cai, Nengbin School of information and network security People's Public Security University of China Beijing China School of investigation School of information and network security People's Public Security University of China Beijing China Shanghai Key Laboratory of Crime Scene Evidence Shanghai China

ISBN: (纸本)9781450397148

Cross-modal pedestrian re-identification is to match the color image and infrared image of the pedestrian to determine whether there is the same pedestrian. The images have large inter-modal differences and large intra-modal variations, resulting in low recognition accuracy. This paper proposes a multi-loss joint cross-modal pedestrian re-identification method that fuses grayscale images and relation-aware global attention (RGA). First, grayscale the color images in the cross-modal dataset to reduce the network's dependence on color information;second, use the trained generative adversarial network to convert the color images and infrared images in the dataset to each other to reduce pedestrian poses and modality changes;then, the RGA is embedded into a shared-weight dual-stream ResNet50 to capture more robust features;finally, the hard sample triplet loss is improved and converted into a cross-modal hard sample triplet loss. The cross-entropy loss of the smooth label is combined with the hard sample triplet loss and the cross-modal hard sample triplet loss, respectively, to form joint functions L1 and L2, and then supervised training of the network. Experiments are carried out on the RegDB and SYSU-MM01 datasets, and the mAP reaches 75.85% and 69.56%, respectively, which are better than many current methods, indicating that the proposed method has better recognition accuracy. © 2022 Association for Computing Machinery.

关键词： Generative adversarial networks

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Enhancing Power System Stability Against DoS Attacks Through Hardware-in-the-Loop Simulation 16

Enhancing Power System Stability Against DoS Attacks Through...

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16th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2024

作者： Tao, Jiaye Liang, Zhihong Wei, Lipeng Hong, Chao Fu, Yun Yang, Yiwei Zhou, Zeyuan Chen, Lin Information Centre Guizhou Power Grid Co. Guizhou China Electric Power Research Institute China Southern Power Grid Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China

ISBN: (纸本)9798350386127

This study explores the stability and security of power systems in the face of Denial of Service (DoS) attacks through Hardware-in-the-Loop (HiL) simulation technology. As the application of Distributed Energy Resources (DER) and smart devices becomes more widespread, power systems have evolved from traditional physical networks to complex Cyber-Physical Systems (CPS). While this transformation has enhanced system efficiency and flexibility, it has also made power systems more susceptible to cyberattacks, especially concerning the security of critical infrastructure. This study establishes a CPS test platform incorporating real hardware, compares the response and recovery capabilities of centralized versus distributed control architectures under DoS attacks, and assesses in real time the direct impact of cyberattacks on the physical operation of power systems. Through these simulations, the study aims to reveal the strengths and limitations of different control architectures, providing a scientific basis for enhancing the security and stability of power systems. © 2024 IEEE.

关键词： Hardware-in-the-loop simulation

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SR-AFU: super-resolution network using adaptive frequency component upsampling and multi-resolution features

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Frontiers of computer Science 2023年第1期17卷 123-132页

作者： Ke-Jia CHEN Mingyu WU Yibo ZHANG Zhiwei CHEN School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023China Jiangsu Key Laboratory of Big Data Security&Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing 210023China College of Telecommunications and Information Engineering Nanjing University of Posts and TelecommunicationsNanjing 210003China

Image super-resolution (SR) is one of the classic computer vision tasks. This paper proposes a super-resolution network based on adaptive frequency component upsampling, named SR-AFU. The network is composed of multiple cascaded dilated convolution residual blocks (CDCRB) to extract multi-resolution features representing image semantics, and multiple multi-size convolutional upsampling blocks (MCUB) to adaptively upsample different frequency components using CDCRB features. The paper also defines a new loss function based on the discrete wavelet transform, making the reconstructed SR images closer to human perception. Experiments on the benchmark datasets show that SR-AFU has higher peak signal to noise ratio (PSNR), significantly faster training speed and more realistic visual effects compared with the existing methods.

关键词： super-resolution multi-resolution features adaptive frequency upsampling wavelet transformation

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Switched Event-Triggering Secondary Frequency Control of Power Systems Considering Wind and Solar Stochastics Under Denial of Service Attack 16

Switched Event-Triggering Secondary Frequency Control of Pow...

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16th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2024

作者： Hong, Chao Fu, Yun Chen, Lin Tao, Jiaye Liang, Zhihong Wei, Lipeng Yang, Yiwei Ban, Qiucheng Wu, Shutan Electric Power Research Institute China Southern Power Grid Guangdong Provincial Key Laboratory of Power System Network Security Guangdong China Information Centr Guizhou Power Grid Co. Guizhou China

ISBN: (纸本)9798350386127

This paper addresses the stochastic nature of wind and photovoltaic power generation, along with the increasing threat of denial of service (DoS) attacks on power systems. To counteract the growing incidence of DoS attacks, we propose a switch-like event triggering mechanism (SLETM) aimed at reducing their impact on data transfer processes. Additionally, we establish a stability criterion utilizing linear matrix inequality (LMI) to ensure the power system's stability and to ascertain the final parameters of a dynamic output feedback (DOF) controller. The proposed methods are then implemented in two interconnected power system regions. Simulation results indicate that incorporating SLETM and DOF controls enhances the stability of load frequency control (LFC) within the power system. © 2024 IEEE.

关键词： Stability criteria

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Secrecy Communication for Wireless-Powered Cooperative NOMA Systems With a Friendly Jammer

Secrecy Communication for Wireless-Powered Cooperative NOMA ...

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2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Ren, Yuan Zhang, Xuewei Long, Pinyi Wang, Junxuan Lu, Guangyue Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security School of Communications and Information Engineering Xi'an710121 China

ISBN: (纸本)9781665459754

In this paper, we investigate the physical layer security of wireless-powered cooperative non-orthogonal multiple access (NOMA) systems with an energy harvesting (EH) friendly jammer. In particular, the eavesdropper intends to wiretap the transmitted signals from relay, and a friendly jammer, with the EH capability, emits a jamming signal to confuse the eavesdropper. We design two time phases to achieve source-to-users transmission in the systems. In the first phase, the source transmits a superimposed signal to the near user and the EH relay, which harvests energy and decodes the information simultaneously, and the jammer can harvest energy from the radio frequency signals. In the second phase, the relay forwards the signals to the two users and the transmitted signals can be wiretapped by an eavesdropper. A wireless-powered friendly jammer is applied to emit artificial noise to confuse the eavesdropper. Under the proposed scheme, we derive the closed-form expression for the secrecy throughput to evaluate the secure performance. Simulation results verify the theoretical analysis of the proposed scheme, and demonstrate that our scheme obtains better secrecy performance, as compared to the conventional cooperative NOMA scheme. © 2022 IEEE.

关键词： Energy harvesting

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Adaptive Group Personalization for Federated Mutual Transfer Learning 41

Adaptive Group Personalization for Federated Mutual Transfer...

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41st International Conference on Machine Learning, ICML 2024

作者： Xu, Haoqing Shen, Dian Wang, Meng Wang, Beilun School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China College of Design and Innovation Tongji University Shanghai China

Mutual transfer learning aims to improve prediction with knowledge from related domains. Recently, federated learning is applied in this field to address the communication and privacy concerns. However, previous clustered federated learning (CFL) solutions lack theoretical guarantee of learnability recovery and require time-consuming hyper-parameter tuning, while centralized mutual transfer learning methods lack adaptability to concept drifts. In this paper, we propose the Adaptive Group Personalization method (AdaGrP) to overcome these challenges. We adaptively decide the recovery threshold with a nonparametric method, adaptive threshold correction, for tuning-free solution with relaxed condition. Theoretical results guarantee the perfect learnability recovery with the corrected threshold. Empirical results show AdaGrP achieves 16.9% average improvement in learnability structure recovery compared with state-of-the-art CFL baselines. Copyright 2024 by the author(s)

关键词： Federated learning

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Collision Detection and Load Estimation for Massive Random Access in Satellite-Based Internet of Things: A Deep Learning Approach

Collision Detection and Load Estimation for Massive Random A...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhen, Li Li, Mingyang He, Yuxuan Su, Jingrui Yu, Keping Rodrigues, Joel J. P. C. Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Xi'an China Hosei University Graduate School of Science and Engineering Tokyo Japan Amazonas State University AM Manaus Brazil

ISBN: (纸本)9798350351255

Satellite communications have been regarded as a promising solution to be incorporated in future Internet of Things (IoT) to support continuous and ubiquitous connectivity services. Constrained by limited contention resources, the conventional random access (RA) scheme will suffer from severe overload problem when applied to the emerging satellite-based IoT. Focusing on improving access efficiency in the massive and concurrent access scenarios, we propose a sample feature enhancement based collision detection and load estimation scheme with the aid of deep learning. Specifically, by leveraging the inherent characteristics of correlation results in case of preamble collision, a feature extraction method is first designed to precisely screen important sample features related to the current load information. Then, a multi-feature enhancement network with an adaptive neuron optimization strategy is further proposed to enable original 1D features mapped to a 2D domain, so as to improve the representation capability of the model while preventing overfitting. Simulation results validate the feasibility of our scheme in large-scale RA collision scenarios and demonstrate its remarkable performance superiority in terms of load estimation accuracy and collision detection probability over the state-of-the-art schemes. © 2024 IEEE.

关键词： Satellite communication systems

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