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IEEE Transactions on Sustainable Computing 2024年第3期10卷 534-546页

作者： Tian, Jiwei Shen, Chao Wang, Buhong Ren, Chao Xia, Xiaofang Dong, Runze Cheng, Tianhao Ministry of Education Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi'an Jiaotong University Xi'an China Air Traffic Control and Navigation College Air Force Engineering University Xi'an China Information and Navigation College Air Force Engineering University Xi'an China School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Computer Science and Technology Xidian University Xi'an China

Although conventional false data injection attacks can circumvent the detection of bad data detection (BDD) in sustainable power grid cyber physical systems, they are easily detected by well-trained deep learning-based detectors. Still, state estimation models with deep leaning-based detectors are not secure due to the vulnerabilities and fragility of deep learning models. Using the related laws of conventional false data injection attacks and adversarial sample attacks, this paper proposes the targEted adVersarial fAlse Data injEction (EVADE) strategy to explore targeted adversarial false data injection attacks for state estimation in Smart Grid. The proposed EVADE attack strategy selects key state variables based on adversarial saliency maps to improve the attack efficiency and perturbs as few state variables as possible to reduce the attack cost. In this way, the EVADE attack strategy can bypass the detection of BDD and neural attack detection (NAD) methods (that is, maintaining deep stealthy) with a high success rate and achieve the attack target simultaneously. Experimental results demonstrate the effectiveness of the proposed strategy, posing serious and pressing concerns for sustainable cyber physical power system security. © 2016 IEEE.

关键词： Cyber attacks

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Enhancing Security and Privacy in Federated Learning using Low-Dimensional Update Representation and Proximity-Based Defense

arXiv

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arXiv 2024年

作者： Li, Wenjie Fan, Kai Zhang, Jingyuan Li, Hui Lim, Wei Yang Bryan Yang, Qiang The State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore The Department of Computer Science and Engineering Hong Kong University of Science and Technology 999077 Hong Kong

Federated Learning (FL) is a promising privacy-preserving machine learning paradigm that allows data owners to collaboratively train models while keeping their data localized. Despite its potential, FL faces challenges related to the trustworthiness of both clients and servers, particularly against curious or malicious adversaries. In this paper, we introduce a novel framework named Federated Learning with Low-Dimensional Update Representation and Proximity-Based defense (FLURP), designed to address privacy preservation and resistance to Byzantine attacks in distributed learning environments. FLURP employs LinfSample method, enabling clients to compute the l∞ norm across sliding windows of updates, resulting in a Low-Dimensional Update Representation (LUR). Calculating the shared distance matrix among LURs, rather than updates, significantly reduces the overhead of Secure MultiParty Computation (SMPC) by three orders of magnitude while effectively distinguishing between benign and poisoned updates. Additionally, FLURP integrates a privacy-preserving proximity-based defense mechanism utilizing optimized SMPC protocols to minimize communication rounds. Our experiments demonstrate FLURP’s effectiveness in countering Byzantine adversaries with low communication and runtime overhead. FLURP offers a scalable framework for secure and reliable FL in distributed environments, facilitating its application in scenarios requiring robust data management and security. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Impact of Hardware Impairments on Covert Cooperative Backscatter Communications

Impact of Hardware Impairments on Covert Cooperative Backsca...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Huan Li Yinghui Ye Lu Lv Guangyue Lu Naofal Al-Dhahir Shaanxi information communication network and security Laboratory Xi'an University of Posts and Telecommunications Xi'an China State Key Laboratory of Integrated Services Networks Xidian University Xi'an China Department of Electrical and Computer Engineering The University of Texas at Dallas Richardson TX USA

In this paper, we investigate a covert cooperative backscatter communication (BackCom) network under residual hardware impairments (RHIs) that are unavoidable in practical radio frequency transceivers and may have an impact on the communication covertness and reliability. By exploiting the availability of statistical channel state information, we derive analytic expressions for the eavesdropper's detection error probability (DEP) and system outage probability (SOP) to evaluate the system covertness and communication reliability, respectively. Using the derived results, we prove that RHIs can improve the communication covertness but significantly degrade the outage performance. We also show that under RHIs and extremely high transmit signal-to-noise levels, the system outage performance has a non-zero asymptote, while the DEP still approaches one, similar to the scenario with perfect hardware. computer simulations corroborate our analytical results.

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Terabit Ultra-high-speed NFDM Transmission over G.654E Fiber with 103Gbaud Probabilistic Shaped 64QAM Signals

Terabit Ultra-high-speed NFDM Transmission over G.654E Fiber...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Xinyu Chen Xiansong Fang Xiang Cai Lingjun Zhou Chao Li Zhixue He Fan Zhang State Key Laboratory of Advanced Optical Communication System and Networks Frontiers Science Center for Nano-Optoelectronics School of Electronics Peking University Beijing China School of Electronic and Computer Engineering Peking University Shenzhen China Peng Cheng Laboratory Shenzhen China

We experimentally demonstrate a record ultra-high-speed 1.004 Tb/s dual-polarization NFDM transmission system over 300 km G.654E fiber with probabilistic shaped 64QAM signals, achieving a spectral efficiency at 7.65 b... 详细信息

ISBN: (纸本)9781957171258

关键词： Frequency division multiplexing Optical amplifiers Optical signals Quadrature amplitude modulation Signal processing Signal transmission

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An Upper Bound of the Set Size of Perfect Sequences with Optimal Cross-correlation

An Upper Bound of the Set Size of Perfect Sequences with Opt...

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IEEE International Symposium on Information Theory

作者： Zilong Wang Qian Chen Guang Gong State Key Laboratory of Integrated Service Networks Xidian University Xi&#x2019 an China Department of Electrical and Computer Engineering University of Waterloo Waterloo Ontario Canada

ISBN: (数字)9781665421591

ISBN: (纸本)9781665421607

The set of perfect sequences with optimal cross-correlation has applications in communication and radar systems. Many different constructions, which are called optimal sets of perfect sequences according to Sarwate bound, have been studied in the literature. However, Song et al. and Zhang et al. recently showed that the set size of these constructions can be improved, since the term related to size vanishes for perfect sequences in Sarwate bound. Until now, we don’t know whether the set size of these constructions is optimal, though they are all called optimal sets. We studied the problem of the set size of perfect sequences with optimal cross-correlation, and showed that the set size must be upper bounded by the length of the perfect sequences in this paper.

关键词： Upper bound Radar applications Information theory

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Covert Communication in Underlay Multi-Antenna Cognitive Radio networks

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IEEE Transactions on Cognitive Communications and Networking 2024年

作者： Fan, Hongbin Yang, Weiwei Shi, Hui Pan, Gaofeng Si, Jiangbo Wang, Yutao Rong, Chuanzhen Army Engineering University of PLA College of Communications Engineering Nanjing210007 China Xiangnan University College of Computer and Artificial Intelligence Chenzhou423000 China Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software Guilin541004 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an710100 China

In this paper, we investigated a multi-antenna covert communication in underlay cognitive radio networks (UCRNs), by considering both cases that Willie has the perfect and statistical channel state information (CSI) of the link from the secondary transmitter (ST) to the primary receiver (PR), respectively. Specifically, the optimal antenna of the secondary receiver (SR) is selected to receive the private information from ST, and then one of the remaining antennas is randomly selected to transmit jamming signals. The closed-form expressions of Willie's detection error probability, the average minimum detection error probability, the transmission outage probability and the maximum effective covert rate under both cases are derived. The maximum effective covert rate is obtained by optimising the maximum jamming power of SR. The numerical results show that the combined effect of the maximum interference tolerance of PR and the maximum interference power of SR provides the positive effective covert rate between ST and SR. It is further found that the maximum effective covert rate between ST and SR when Willie knows the perfect CSI of the ST-SR link is greater than that when Willie only knows the statistical CSI of the ST-SR link. © 2015 IEEE.

关键词： Jamming

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Adaptive Loose Optimization for Robust Question Answering

arXiv

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arXiv 2023年

作者： Ma, Jie Wang, Pinghui Wang, Zewei Kong, Dechen Hu, Min Han, Ting Liu, Jun The Ministry of Education of Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China The Ministry of Education of Key Laboratory for Intelligent Networks and Network Security School of Automation Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China The China Mobile Research Institute China The Shannxi Provincial Key Laboratory of Big Data Knowledge Engineering School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China

Question answering methods are well-known for leveraging data bias, such as the language prior in visual question answering and the position bias in machine reading comprehension (extractive question answering). Current debiasing methods often come at the cost of significant in-distribution performance to achieve favorable out-of-distribution generalization ability, while non-debiasing methods sacrifice a considerable amount of out-of-distribution performance in order to obtain high in-distribution performance. Therefore, it is challenging for them to deal with the complicated changing real-world situations. In this paper, we propose a simple yet effective novel loss function with adaptive loose optimization, which seeks to make the best of both worlds for question answering. Our main technical contribution is to reduce the loss adaptively according to the ratio between the previous and the current optimization state on mini-batch training data. This loose optimization can be used to prevent non-debiasing methods from overlearning data bias while enabling debiasing methods to maintain slight bias learning. Experiments on the visual question answering datasets, including VQA v2, VQA-CP v1, VQA-CP v2, GQA-OOD, and the extractive question answering dataset SQuAD demonstrate that our approach enables QA methods to obtain state-of-the-art in- and out-of-distribution performance in most cases. The source code has been released publicly in https://***/reml-group/ALO. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Improved Particle Swarm Optimization Algorithm for EIT Image Reconstruction

Improved Particle Swarm Optimization Algorithm for EIT Image...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Oumayma Kahouli Mariem Hafsa Hiba Hellara Imed Eddine Bennour Najoua Essoukri Ben Amara Olfa Kanoun Department of Computer Science National Engineering School of Sousse University of Sousse Sousse Tunisia Laboratory of Advanced Technology and Intelligent Systems National Engineering School of Sousse Laboratory of Measurement and Sensor Technology Technische Universität Chemnitz Chemnitz Germany Laboratory of Signals Systems Artificial Intelligence and Networks Digital Research Center of Sfax University of Sfax Tunisia

Electrical Impedance Tomography (EIT) is a clinical imaging technique that gained a lot of attention because it is a non-invasive and radiation-free method. To obtain an inner image of the domain under study, a specific number of electrodes is attached to the surface of the object, then through a pair of electrodes, a low alternative current is injected. Potentials produced by this injection are measured with the remaining electrodes. Based on these boundary voltages measurements, the inner conductivity can be inferred, thus the image is reconstructed. The focus of this paper is to overcome the major weakness of the EIT image reconstruction technique, which is the low spatial resolution of the reconstructed images caused by the non-linearity of EIT inverse problem. This paper considers this problem, an optimization problem that is solved using the Particle Swarm Optimization (PSO) meta-heuristic. A variant of the standard PSO is proposed and validated, first on a circular mesh composed of 686 triangles then on a lung mesh composed of 2707 triangles.

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High-Order-QAM Blind Equalization for Cognitive Communication Systems

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IEEE Transactions on Cognitive Communications and Networking 2024年

作者： Li Zheng, Wei Xing Liu, Mingqian Chen, Yunfei Zhao, Nan Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China Western Sydney University Computer Data and Mathematical Sciences SydneyNSW2751 Australia University of Durham Department of Engineering DurhamDH1 3LE United Kingdom Dalian University of Technology School of Information and Communication Engineering Dalian116024 China

Blind equalization can not only counteract channel multi-path fading but also guarantee the transmission efficiency of a communication system. However, determining an optimal blind equalizer is typically NP hard. The cost function of such blind equalizer involves an exponentially increasing number of local minima. To reduce the complexity, this study develops an efficient two-stage blind equalization algorithm for quadrature-amplitude-modulation (QAM) systems. To reduce the number of local minima, the multimodulus algorithm (MMA) is implemented in the first stage, and to reduce the steady state error, an improved soft decision-directed algorithm (ISDDA) is implemented in the second stage. A novel modified least squares method (MLSM) is proposed to quickly search for the desired equalizer. The MLSM can converge to the invariance set of the MMA and ISDDA cost functions and has a quadratic termination property. In particular, theoretical analysis shows that the MLSM has a considerably lower computational load than the Newton methods by computing the constant Hessian matrix and its inverse only once. Furthermore, to ensure that the proposed algorithm switches to the second stage as early and suitably as possible, an attainable switching threshold between the two stages is provided on the basis of a hypothesis that the equalizer output error obeys a normal distribution, and the rationale of this hypothesis is also provided. Simulation results illustrate that the proposed algorithm has better convergence stability, superior equalization quality, and considerably faster convergence speed than the traditional stochastic-gradient-type dual-mode blind equalization algorithms. © 2015 IEEE.

关键词： Cost functions

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Synesthesia of Machines (SoM)-Enhanced Wideband Multi-User CSI Learning With LiDAR Sensing

arXiv

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arXiv 2024年

作者： Zhang, Haotian Gao, Shijian Cheng, Xiang Yang, Liuqing State Key Laboratory of Photonics and Communications School of Electronics Peking University Beijing100871 China Guangzhou511400 China Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Shenzhen China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Light detection and ranging (LiDAR) has been utilized for optimizing wireless communications due to its ability to detect the environment. This paper explores the use of LiDAR in channel estimation for wideband multi-user multiple-input-multiple-output orthogonal frequency division multiplexing systems and introduces a LiDAR-Enhanced Channel State Information (CSI) Learning Network (LE-CLN). By utilizing user positioning information, LE-CLN first calculates user-localized over-complete angular measurements. It then investigates the correlation between LiDAR and CSI, transforming raw LiDAR data into a low-complexity format embedded with signal propagation characteristics. LE-CLN also adapts the use of LiDAR based on channel conditions through attention mechanisms. Thanks to the unique wireless features offered by LiDAR, LE-CLN achieves higher estimation accuracy and spectrum efficiency compared to benchmarks, particularly in latency-sensitive applications where pilot transmissions are expected to be reduced. Copyright © 2024, The Authors. All rights reserved.

关键词： Orthogonal frequency division multiplexing

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