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Smart World Congress (SWC), IEEE

作者： Shuai Wang Shenghao Liu Xiaoxuan Fan Haijun Wang Yinxin Zhou Hanjun Gao Hongwei Lu Xianjun Deng Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Hunan Technical College of Railway High-speed China Nanjing University of Posts and Telecommunications China China National Nuclear Power Co Ltd China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

Intelligent Cyber-Physical systems typically utilize sensors to gather a significant amount of raw data which often possesses temporal and high-dimensional characteristics. Ensuring the data reliability is crucial for the normal operation of the system. While most existing methods are capable of identifying anomalies in high-dimensional time series data from sensors, they often lack adequate accuracy and fail to adequately consider the correlation between sensor nodes. To address these limitations, we propose an unsupervised framework which is a combination of variational autoencoder and graph neural network(VG-Net). By jointly optimizing the reconstruction module and the prediction module, VG-Net effectively models the correlation between sensor nodes while simultaneously detecting anomalies. The reconstruction module is responsible for modeling the latent representation space and reconstructing the time series data through variational autoencoder. The prediction module leverages bidirectional long short term memory networks and neural tensor network to learn the relationships between sensor nodes, and uses graph neural network to predict the future behavior of the data. Experiments on eight real and synthetic datasets demonstrate that, compared with existing methods, VG-Net can accurately model the relationship between sensor nodes and enhance the anomaly detection performance of high-dimensional time series data.

关键词： Time series analysis Autoencoders Sensor phenomena and characterization Predictive models Data models Graph neural networks Reliability Anomaly detection Intelligent sensors Synthetic data

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Repairing the N-vacancy in h-BN monolayer using NO molecules: A first-principles study

Repairing the N-vacancy in h-BN monolayer using NO molecules...

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Materials Science and Technology 2019, MS and T 2019

作者： Cui, Hao Liu, Tun Zhang, Xiaoxing State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing University Chongqing400044 China School of Electrical and Electronic Engineering Hubei University of Technology Hubei430068 China School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Traffic and Transportation Engineering Central South University Changsha410083 China

ISBN: (纸本)0873397703

In the practical synthesis of h-BN nanomaterial, there inevitably exist some defects, such as N-vacancy, which will largely impair its properties geometric and electronic behaviors. Therefore, it is necessary to repair these defects before further investigation and application. In this paper, we investigate the use of NO molecules through a NO disproportionation to heal the N-vacancy in h-BN monolayer based on first-principles theory. The calculations indicate that the repairing processes are energetically favorable and the repaired h-BN monolayer, from geometric and electronic aspects, has good similarity with the perfect counterpart. This theoretical work presents a promising strategy for repairing the N-vacancy in h-BN monolayer to perfect its physicochemical properties effectively, which may also be workable to repair N-vacancy in other nanomaterials. © 2019 MS&T19®

关键词： Monolayers

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Owl: Differential-Based Side-Channel Leakage Detection for CUDA Applications

Owl: Differential-Based Side-Channel Leakage Detection for C...

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International Conference on Dependable systems and Networks (DSN)

作者： Yu Zhao Wenjie Xue Weijie Chen Weizhong Qiang Deqing Zou Hai Jin National Engineering Research Center for Big Data Technology and System Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Jinyinhu Laboratory Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350341058

ISBN: (纸本)9798350341065

Over the past decade, various methods for detecting side-channel leakage have been proposed and proven to be effective against CPU side-channel attacks. These methods are valuable in assisting developers to identify and patch side-channel vulnerabilities. Nevertheless, recent research has revealed the feasibility of exploiting side-channel vulnerabilities to steal sensitive information from GPU applications, which are beyond the reach of previous side-channel detection methods. Therefore, in this paper, we conduct an in-depth examination of various GPU features and present Owl, a novel side-channel detection tool targeting CUDA applications on NVIDIA GPUs. Owl is designed to detect and locate side-channel leakage in various types of CUDA applications. When tracking the execution of CUDA applications, we design a hierarchical tracing scheme and extend the A-DCFG (Attributed Dynamic Control Flow Graph) to address the massively parallel execution in CUDA, ensuring Owl's detection scalability. After completing the initial assessment and filtering, we conduct statistical tests on the differences in program traces to determine whether they are indeed caused by input variations, subsequently facilitating the positioning of side-channel leaks. We evaluate Owl's capability to detect side-channel leaks by testing it on Libgpucrypto, PyTorch, and nvJPEG. Meanwhile, we verify that our solution effectively handles a large number of threads. Owl has successfully identified hundreds of leaks within these applications. To the best of our knowledge, we are the first to implement side-channel leakage detection for general CUDA applications.

关键词： Target tracking Statistical analysis Filtering Scalability Graphics processing units Side-channel attacks Feature extraction

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Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning

arXiv

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arXiv 2023年

作者： Zhang, Hangtao Yao, Zeming Zhang, Leo Yu Hu, Shengshan Chen, Chao Liew, Alan Li, Zhetao School of Cyber Science and Engineering Huazhong University of Science and Technology China Swinburne University of Technology Australia Griffith University Australia National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab. Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China RMIT University Australia Xiangtan University China

Federated learning (FL) is vulnerable to poisoning attacks, where adversaries corrupt the global aggregation results and cause denial-of-service (DoS). Unlike recent model poisoning attacks that optimize the amplitude of malicious perturbations along certain prescribed directions to cause DoS, we propose a Flexible Model Poisoning Attack (FMPA) that can achieve versatile attack goals. We consider a practical threat scenario where no extra knowledge about the FL system (e.g., aggregation rules or updates on benign devices) is available to adversaries. FMPA exploits the global historical information to construct an estimator that predicts the next round of the global model as a benign reference. It then fine-tunes the reference model to obtain the desired poisoned model with low accuracy and small perturbations. Besides the goal of causing DoS, FMPA can be naturally extended to launch a fine-grained controllable attack, making it possible to precisely reduce the global accuracy. Armed with precise control, malicious FL service providers can gain advantages over their competitors without getting noticed, hence opening a new attack surface in FL other than DoS. Even for the purpose of DoS, experiments show that FMPA significantly decreases the global accuracy, outperforming six state-of-the-art attacks. © 2023, CC BY.

关键词： Federated learning

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Intersecting-boundary-sensitive fingerprinting for tampering detection of DNN models 24

Intersecting-boundary-sensitive fingerprinting for tampering...

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Proceedings of the 41st International Conference on Machine Learning

作者： Xiaofan Bai Chaoxiang He Xiaojing Ma Bin Benjamin Zhu Hai Jin School of Cyber Science and 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 Microsoft School of Computer Science and Technology Huazhong University of Science and Technology and National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Cluster and Grid Computing Lab.

Cloud-based AI services offer numerous benefits but also introduce vulnerabilities, allowing for tampering with deployed DNN models, ranging from injecting malicious behaviors to reducing computing resources. Fingerprint samples are generated to query models to detect such tampering. In this paper, we present Intersecting-Boundary-Sensitive Fingerprinting (IBSF), a novel method for black-box integrity verification of DNN models using only top-1 labels. Recognizing that tampering with a model alters its decision boundary, IBSF crafts fingerprint samples from normal samples by maximizing the partial Shannon entropy of a selected subset of categories to position the fingerprint samples near decision boundaries where the categories in the subset intersect. These fingerprint samples are almost indistinguishable from their source samples. We theoretically establish and confirm experimentally that these fingerprint samples' expected sensitivity to tampering increases with the cardinality of the subset. Extensive evaluation demonstrates that IBSF surpasses existing state-of-the-art fingerprinting methods, particularly with larger subset cardinality, establishing its state-of-the-art performance in black-box tampering detection using only top-1 labels. The IBSF code is available at: https://***/CGCL-codes/IBSF.

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A Real-Time Demonstration of Bidirectional Multi-Format/Rate-Adjustable Integrated Laser Communication/Ranging for Satellite Communication

A Real-Time Demonstration of Bidirectional Multi-Format/Rate...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Yizhou Wang Yuanxiang Wang Zhen Luo Linsheng Zhong Yang Zou Xueyuan Ao Jiale Liu Feng Fan Xiaoliang Li Xiaoxiao Dai Qi Yang Deming Liu Wuhan National Lab for Optoelectronics (WNLO) & National Engineering Laboratory for Next Generation Internet Access System School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Beijing Research Institute of Telemetry Beijing China Jinyinhu Laboratory Wuhan China

A multi-format/rate-adjustable integrated laser communication/ranging system is proposed, achieving adjustable rates between 0.625-10Gbit/s. Real-time experiment shows that at 1e-4 BER the ranging error is only 0.29% ... 详细信息

ISBN: (纸本)9798350369311

关键词： Laser theory Codes Sensitivity Lasers Measurement uncertainty Measurement by laser beam Electro-optic effects Real-time systems Distance measurement Satellite communications

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Partial Discharge Fiber Grating Ultrasonic Sensor Based on Symmetric Double Diaphragm

Partial Discharge Fiber Grating Ultrasonic Sensor Based on S...

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Advanced Electrical Equipment and Reliable Operation (AEERO), International Conference on

作者： Yuxuan Song Weigen Chen Zhixian Zhang Kejie Wu Fan Liu Jiali Lei Tianhe Yang State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University Chongqing China State Grid Hubei Electric Power Research Institute Wuhan Hubei China

ISBN: (纸本)9781665402651

A novel nonmetallic fibre optic acoustic sensor on the basis of a symmetric double-diaphragm structure and a fibre Bragg grating (FBG) is presented and demonstrated. The structural principle of the sensor and the optimization of its structural parameters are introduced. A detection system for the response characteristics of the FBG sensor in oil is built. Based on the edge filter demodulation method, the response characteristics of the FBG sensor to ultrasonic signals in oil are measured. The analysis results indicate that the FBG sensor can measure the ultrasonic signal of tens to hundreds of kilohertz in the oil sensitively, and the sensor has multiple resonant peak values, whose sensitivity is high. A partial discharge (PD) detection system is built. The designed FBG sensor is used to measure the ultrasonic signal generated by the actual PD. The PD detection performance of the sensor at different angles is tested. The results indicate that the designed FBG sensor can detect the PD ultrasonic signal in time domain sensitively.

关键词： Partial discharges Partial discharge measurement Optical fibers Temperature sensors Temperature measurement Ultrasonic variables measurement Fiber gratings

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The security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective

arXiv

引用

arXiv 2024年

作者： Wang, Cheng Yuan, Zenghui Zhou, Pan Xu, Zichuan Li, Ruixuan Wu, Dapeng Oliver Hubei Engineering Research Center on Big Data Security Key Laboratory of Distributed System Security of Hubei Province School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Software The Key Laboratory for Ubiquitous Network Service Software of Liaoning Province Dalian University of Technology Dalian116024 China Intelligent and Distributed Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science City University of Hong Kong Kowloon999077 Hong Kong

Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network’s edge. By shifting the load of cloud computing to individual local servers, MEC helps meet the requirements of ultralow latency, localized data processing, and extends the potential of Internet of Things (IoT) for end-users. However, the crosscutting nature of MEC and the multidisciplinary components necessary for its deployment have presented additional security and privacy concerns. Fortunately, Artificial Intelligence (AI) algorithms can cope with excessively unpredictable and complex data, which offers a distinct advantage in dealing with sophisticated and developing adversaries in the security industry. Hence, in this paper we comprehensively provide a survey of security and privacy in MEC from the perspective of AI. On the one hand, we use European Telecommunications Standards Institute (ETSI) MEC reference architecture as our based framework while merging the Software Defined Network (SDN) and Network Function Virtualization (NFV) to better illustrate a serviceable platform of MEC. On the other hand, we focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI. Finally, we comprehensively discuss the opportunities and challenges associated with applying AI to MEC security and privacy as possible future research directions. Copyright © 2024, The Authors. All rights reserved.

关键词： Network security

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Extrinsic Fabry-Perot Sensor for Partial Discharge Acoustic Detection in Liquid

Extrinsic Fabry-Perot Sensor for Partial Discharge Acoustic ...

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International Conference on High Voltage Engineering and Application, ICHVE

作者： Jiali Lei Weigen Chen Zhixian Zhang Shiyezi Xiang Zhengqi Li Fan Liu State Key Laboratory of Transmission and Distribution Equipment and System Security and New Technology Chongqing University Chongqing China State Grid Hubei Electric Power Research Institute Wuhan Hubei China

ISBN: (数字)9781728155111

ISBN: (纸本)9781728155128

Partial discharge online detection technology can reflect the insulation status of electrical equipment. The extrinsic fiber Fabry-Perot sensor uses a diaphragm cavity to sense the changes in interference intensity caused by the sound waves generated by partial discharge. It has stronger resistance to electromagnetic interference, longer transmission distance, and smaller structure, comparing with traditional ultrasonic sensors. With so many advantages, it undoubtedly becomes the focus of research on partial discharge ultrasonic detection. This paper first explores the sensitivity mechanism of optical fiber Fabry-Perot sensors to acoustic signals. Then, according to the change laws of the resonance frequency of the diaphragm with the size parameters, a fiber optic Fabry-Perot partial discharge sensor based on the structure of the nickel circular diaphragm and the quartz tube is developed. The fabricated Fabry-Perot sensor is placed in water, and the acoustic signals from the piezoelectric ceramic transducer driven by the signal generator are detected; the partial discharge ultrasonic signals generated by the pin-plate electrode model in transformer oil are also tested. And the tests have achieved the desired results.

关键词： Fabry-Perot Acoustics Partial discharges Nickel Resonant frequency Sensitivity Oils

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SF₆ adsorption behavior on ZnO surfaces with deficient configurations and H coverage

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Computational and Theoretical Chemistry 2024年 1239卷

作者： Lv, Langlang Zhu, Kexin Zhou, Zhengwei Chen, Guangzhi Jiang, Hua Wang, Xiangyu Zeng, Fuping School of Electrical Engineering and Automation Wuhan University Hubei Key Laboratory of Power Equipment & System Security for Integrated Energy Resources

The emission of SF6 is a worldwide concern. ZnO-based catalysts, featuring high specific surface area and abundant active sites, are potential catalysts for converting waste SF6. This study, based on density functiona... 详细信息

The emission of SF₆ is a worldwide concern. ZnO-based catalysts, featuring high specific surface area and abundant active sites, are potential catalysts for converting waste SF₆. This study, based on density functional theory (DFT), investigates the adsorption behavior of SF₆ on clean and modified ZnO surfaces with different polarities. The results show that polar ZnO (0001) exhibits better adsorption and activation of SF₆. The O vacancy on ZnO (101¯0) surfaces promotes SF₆ activation and bond breaking, while the Zn vacancy inhibits SF₆ adsorption on both ZnO facets. Among various modification treatments, hydroxylated ZnO surfaces exhibit the most significant SF₆ adsorption and activation properties. Due to the orbital hybridization between F-2p and Zn-3d, the hydroxylated ZnO (101¯0) surface demonstrates 46 times higher average adsorption energy and 30 times higher average charge transfer of SF₆ compared to clean surfaces. This paper elucidates the key role of ZnO hydroxylated surfaces in SF₆ activation and decomposition. © 2024 Elsevier B.V.

关键词： Adsorption Defect Density functional theory Hydroxylation SF6 ZnO

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