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arXiv 2023年

作者： Yang, Jie Wu, Mei Zhang, Yichen Liu, Jinlu Fan, Fan Li, Yang Huang, Wei Wang, Heng Pan, Yan Su, Qi Bian, Yiming Jiang, Haoyuan Dou, Jiayi Yu, Song Xu, Bingjie Luo, Bin Guo, Hong State Key Laboratory of Information Photonics and Optical Communications School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing100876 China Science and Technology on Security Communication Laboratory Institute of Southwestern Communication Chengdu610041 China State Key Laboratory of Cryptology Beijing100878 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Center for Quantum Information Technology Peking University Beijing100871 China

Based on the intrinsic random property of quantum mechanics, quantum random number generators allow for access of truly unpredictable random sequence and are now heading towards high performance and small miniaturization, among which a popular scheme is based on the laser phase noise. However, this scheme is generally limited in speed and implementation complexity, especially for chip integration. In this work, a general physical model based on wiener process for such schemes is introduced, which provides an approach to clearly explain the limitation on the generation rate and comprehensively optimize the system performance. We present an insight to exploit the potential bandwidth of the quantum entropy source that contains plentiful quantum randomness with a simple spectral filtering method and experimentally boost the bandwidth of the corresponding quantum entropy source to 20 GHz, based on which an ultra-fast generation rate of 218 Gbps is demonstrated, setting a new record for laser phase noise based schemes by one order of magnitude. Our proposal significantly enhances the ceiling speed of such schemes without requiring extra complex hardware, thus effectively benefits the corresponding chip integration with high performance and low implementation cost, which paves the way for its large-scale applications. © 2023, CC BY.

关键词： Random number generation

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DarkSAM: fooling segment anything model to segment nothing 24

DarkSAM: fooling segment anything model to segment nothing

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Proceedings of the 38th International Conference on Neural information Processing Systems

作者： Ziqi Zhou Yufei Song Minghui Li Shengshan Hu Xianlong Wang Leo Yu Zhang Dezhong Yao Hai Jin National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Cluster and Grid Computing Lab and School of Computer Science and Technology Huazhong University of Science and Technology School of Cyber Science and Engineering Huazhong University of Science and Technology School of Software Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Hubei Engineering Research Center on Big Data Security and Hubei Key Laboratory of Distributed System Security and School of Cyber Science and Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University

ISBN: (纸本)9798331314385

Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://***/CGCL-codes/DarkSAM.

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An Anti-jamming Intelligent Decision-Making Method for Multi-user communication Based on Deep Reinforcement Learning

An Anti-jamming Intelligent Decision-Making Method for Multi...

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International Conference on communication technology (ICCT)

作者： Lan Zhang Liang Ma Feng Tian Tianyi Liang Science and Technology on Communication Information Security Control Laboratory Jiaxing China The 36th Research Institution of China Electronics Technology Group Corporation Jiaxing China Nanjing University of Posts and Telecommunications Nanjing China

In order to effectively cope with the external malicious jamming from jammers and avoid mutual interference caused by competitive channels among users, an intelligent antijamming decision-making method based on deep reinforcement learning for multi-user communication is proposed in this paper. In multiple jamming modes, the base station perceives the current spectrum information of the multi-user and the jammer and makes it as the input of the deep reinforcement learning strategy neural network, then selects the joint action according to the dynamic greedy algorithm, which will help the users intelligently select the communication frequency band via the feedback from the base station. The simulation results show that the proposed method effectively overcome the communication jamming induced by external jammers and internal users. Moreover, the normalized throughput of each user in the proposed algorithm is much higher than the random frequency hopping and fixed ε deep reinforcement learning algorithm with faster convergence speed.

关键词： Deep learning Base stations Simulation Heuristic algorithms Decision making Reinforcement learning Spread spectrum communication

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Fast Coding Mode Decision for Intra Prediction in VVC SCC

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IEEE Transactions on Broadcasting 2025年第2期71卷 506-516页

作者： Dayong Wang Weihong Liu Zeyu Zhou Xin Lu Jinhua Liu Hui Guo Ce Zhu Chongqing Key Laboratory on Big Data for Bio Intelligence Chongqing University of Posts and Telecommunications Chongqing China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China School of Cybersecurity Chengdu University of Information Technology Chengdu China School of Computer Science and Informatics De Montfort University Leicester U.K. School of Digital Technology Industry Shangrao Normal University Shangrao China Glasgow College and the School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

Currently, screen content video applications are widely used in our daily lives. As the latest Screen Content Coding (SCC) standard, Versatile Video Coding (VVC) SCC employs a quad-tree plus nested multi-type tree (QTMT) coding structure and various screen content coding modes (CMs). This design enhances the coding efficiency of VVC SCC but also results in a highly complex coding process, which significantly hinders the broader adoption of screen content video technology. Consequently, improving the coding speed of VVC SCC is highly desirable. In this paper, we propose a fast CM and transform decision algorithm for Intra prediction in VVC SCC. Specifically, we initially use Convolutional Neural Networks (CNNs) to predict content types for all Coding Units (CUs). Subsequently, we predict candidate CMs for CUs based on the CM distributions of different content types. We then select the Sum of Absolute Transformed Difference (SATD) as a feature and use a naive Bayes classifier to skip unlikely Intra mode early. Finally, we terminate Block-based Differential Pulse-Code Modulation (BDPCM) early and then select the best transform type in Intra mode prediction to improve coding speed. Experimental results demonstrate that the proposed algorithm improves coding speed by an average of 39.28%, with the BDBR increasing by 0.80%.

关键词： Encoding Copper Prediction algorithms Classification algorithms Transforms Electronic mail Standards Complexity theory Training Partitioning algorithms

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Research on Secure communication Methods for High-Speed Cross-Network Data Exchange

Research on Secure Communication Methods for High-Speed Cros...

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Electronic information Engineering and Computer communication (EIECC), International Conference on

作者： Yao Zhang Yitao Chen Jiujiang Fei Guojun Peng Rong Su Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China Wuhan Maritime Communication Research Institute Wuhan China Wuhan Railway Vocational College of Technology Wuhan China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

This paper introduces the application requirements for high-speed secure communication across networks, the fundamental principles of cross-network data exchange, and its implementation. Based on high-speed switch technology and proprietary protocols, a hardware-software co-design solution is proposed to achieve secure communication for cross-network data exchange. The solution eliminates potential security risks associated with unauthorized access, intrusion attacks, and information leakage in heterogeneous network interconnections. It establishes robust defense capabilities against illegal network intrusions, attacks, and failures, meeting the security communication requirements for controlled interconnection and cross-network data exchange between specialized networks.

关键词： Protocols Computer hacking Switches information leakage Heterogeneous networks Network intrusion

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Detector Collapse: Physical-World Backdooring Object Detection to Catastrophic Overload or Blindness in Autonomous Driving

arXiv

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arXiv 2024年

作者： Zhang, Hangtao Hu, Shengshan Wang, Yichen Zhang, Leo Yu Zhou, Ziqi Wang, Xianlong Zhang, Yanjun Chen, Chao School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China Cluster and Grid Computing Lab China School of Information and Communication Technology Griffith University Australia University of Technology Sydney Australia RMIT University Australia

Object detection tasks, crucial in safety-critical systems like autonomous driving, focus on pinpointing object locations. These detectors are known to be susceptible to backdoor attacks. However, existing backdoor techniques have primarily been adapted from classification tasks, overlooking deeper vulnerabilities specific to object detection. This paper is dedicated to bridging this gap by introducing Detector Collapse (DC), a brand-new backdoor attack paradigm tailored for object detection. DC is designed to instantly incapacitate detectors (i.e., severely impairing detector's performance and culminating in a denial-of-service). To this end, we develop two innovative attack schemes: SPONGE for triggering widespread misidentifications and BLINDING for rendering objects invisible. Remarkably, we introduce a novel poisoning strategy exploiting natural objects, enabling DC to act as a practical backdoor in real-world environments. Our experiments on different detectors across several benchmarks show a significant improvement (∼10%-60% absolute and ∼2-7× relative) in attack efficacy over state-of-the-art attacks. Copyright © 2024, The Authors. All rights reserved.

关键词： Denial-of-service attack

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Joint Radar-communication Beamforming for CRB-Based Target Localization

Joint Radar-Communication Beamforming for CRB-Based Target L...

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IEEE Conference on Vehicular technology (VTC)

作者： Tianhao Mao Jie Yang Le Liang Shi Jin National Mobile Communications Research Laboratory Southeast University Nanjing China Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China Purple Mountain Laboratories Nanjing China

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

This paper studies the beamformer design for an integrated sensing and communication system where simultaneous communication for multiple downlink users and bistatic sensing for a point target are realized. Firstly, to avoid self-interference in monostatic settings, we establish the bistatic sensing model where the sensing performance is measured by the Cramér-Rao bound (CRB) for the target's coordinates and the communication performance by the signal-to-interference-plus-noise ratio (SINR). We aim to minimize the CRB while ensuring a predefined SINR for each user. Then, we derive a closed-form beamformer for the single-user case that proves near-optimal. For the multi-user case, we introduce a beamformer design algorithm based on semidefinite relaxation and a suboptimal design algorithm based on successive convex approximation with reduced complexity. Finally, numerical results are provided to validate the solutions.

关键词： Location awareness Vehicular and wireless technologies Interference cancellation Coordinate measuring machines Downlink Approximation algorithms Integrated sensing and communication

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Towards improving detection performance for malware with a correntropy-based deep learning method

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Digital communications and Networks 2021年第4期7卷 570-579页

作者： Xiong Luo Jianyuan Li Weiping Wang Yang Gao Wenbing Zhao School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083China Beijing Intelligent Logistics System Collaborative Innovation Center Beijing101149China China Information Technology Security Evaluation Center Beijing100085China Department of Electrical Engineering and Computer Science Cleveland State UniversityClevelandOH44115USA

With the rapid development of Internet of Things(IoT)technologies,the detection and analysis of malware have become a matter of concern in the industrial application of Cyber-Physical System(CPS)that provides various services using the IoT ***,many advanced machine learning methods such as deep learning are popular in the research of malware detection and analysis,and some achievements have been made so ***,there are also some *** example,considering the noise and outliers in the existing datasets of malware,some methods are not robust ***,the accuracy of malware classification still needs to be *** at this issue,we propose a novel method that combines the correntropy and the deep learning *** our proposed method for malware detection and analysis,given the success of the mixture correntropy as an effective similarity measure in addressing complex datasets with noise,it is therefore incorporated into a popular deep learning model,i.e.,Convolutional Neural Network(CNN),to reconstruct its loss function,with the purpose of further detecting the features of *** present the detailed design process of our ***,the proposed method is tested both on a real-world malware dataset and a popular benchmark dataset to verify its learning performance.

关键词： Malware detection Mixture correntropy Deep learning Convolutional neural network(CNN)

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Combined Field Integral Equation Using Multibranch RWG Functions for Electromagnetic Scattering from Piecewise Homogeneous Dielectric Objects with Junctions

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IEEE Antennas and Wireless Propagation Letters 2025年

作者： Dong, Zhiwen Chen, Xinlei Gao, Fan Gu, Changqing Li, Zhuo Yang, Wu Lu, Weibing Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Key Laboratory of Radar Imaging and Microwave Photonics Nanjing211106 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Key Laboratory of Radar Imaging and Microwave Photonics Key Laboratory of Inlet and Exhaust System Technology Nanjing211106 China Southeast University State Key Laboratory of Millimeter Waves Nanjing210096 China Southeast University State Key Laboratory of Millimeter Waves School of Information Science and Engineering Nanjing210096 China Southeast University Center for Flexible RF Technology MOE Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China Purple Mountain Laboratories Nanjing210096 China

A new method that utilizes multibranch Rao-Wilton-Glisson (MB-RWG) functions and the electric and magnetic current combined field integral equations (JMCFIE) is proposed for analyzing piecewise homogeneous dielectric targets of arbitrary shapes with junctions. The proposed method allows for the independent discretization of the outer surface of the object and the interfaces of different materials within it, thus enabling the generation of quasi-nonconformal meshes. This provides superior flexibility during preprocessing, especially when handling interfaces connected to the junctions. At the junctions, where more than two material regions meet, there are quasi-nonconformal boundaries between the meshes, and these quasi-nonconformal meshes are then connected in the form of MB-RWG functions. Due to the natural normal continuity of MB-RWG, no continuity requirements or special treatments need to be imposed at these junctions. Numerical experiments are given to demonstrate the feasibility and versatility of the proposed method. © 2002-2011 IEEE.

关键词： Integral equations

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Analysis of Attack Effectiveness Evaluation of AD hoc Networks based on Rough Set Theory

Analysis of Attack Effectiveness Evaluation of AD hoc Networ...

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International Conference on Computational Intelligence and security

作者： Jiahao Dai Yongqun Chen Science and Technology on Communication Information Security Control Laboratory China Electronic Corporation NO.36 Institute Jiaxing China

ISBN: (纸本)9781665494908

This paper mainly studies an attack effectiveness evaluation method for AD hoc networks based on rough set theory. Firstly, we use OPNET to build AD hoc network simulation scenario, design and develop attack module, and obtain network performance parameters before and after the attack. Then the rough set theory is used to evaluate the attack effectiveness. The results show that this method can effectively evaluate the performance of AD hoc networks before and after attacks.

关键词： Rough sets Data processing Ad hoc networks security Indexes Computational intelligence

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