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2022 IEEE International Conference on High Voltage Engineering and Applications, ICHVE 2022

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

ISBN: (数字)9781665407502

ISBN: (纸本)9781665407502

Ultrasonic detection of partial discharges in Gas-Insulated Switchgear (GIS) is mostly done with sensors mounted outside the equipment. Due to the complex internal structure of GIS and the strong sound absorption rate of SF6, it is difficult for traditional piezoelectric sensors to detect the weak partial discharge ultrasonic signal inside GIS. Therefore, based on the fully insulated and anti-electromagnetic interference characteristics of the fiber optic sensor, this paper proposes a built-in Michelson ultrasonic sensing technology, and designs a hollow mandrel structure to increase the sensitivity of the sensor. A GIS partial discharge detection test platform is built to carry out partial discharge detection tests. The results show that the built-in Michelson ultrasonic sensing fiber loop can detect the tip discharge of 8.8pC and the free particle discharge of 15.3pC after being arranged near the GIS partial discharge model. © 2022 IEEE.

关键词： Electric switchgear

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Confident Information Coverage Reliability Evaluation for Sensor Networks of Openly Deployed ICP systems

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical systems 2024年 2卷 565-574页

作者： Chen, Suning Yi, Yuanyuan Yi, Lingzhi Liu, Shenghao Deng, Xianjun Xia, Yunzhi Fan, Xiaoxuan Yang, Laurence T. Park, Jong Hyuk Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security The School of Cyber Science and Engineering Wuhan430074 China Huazhong University of Science and Technology School of Journalism and Information Communication Wuhan430074 China Zhongnan University of Economics and Law School of Information and Safety Engineering Wuhan430073 China Seoul National University of Technology Department of Computer Science and Engineering Seoul01811 Korea Republic of

Industrial Cyber-Physical (ICP) systems are integration of computation and physical processes to help achieve operational excellence. As sensors and actuators compose the openly deployed ICP systems and are often susceptible to interference, it puts forward higher requirements on reliability. Reliable coverage ensures sensors acquiring information and therefore coverage reliability of sensor network is studied from various perspectives. However, most existing reliability evaluation methods ignore the belief degree of coverage which influences the reliability to an extent. To evaluate the coverage reliability from the perspective of belief degree, we firstly define Confident Information Coverage Belief Reliability (CICBR) based on the Confident Information Coverage (CIC) model and D-S evidence theory, which comprehensively considers the coverage rate, interference from the environment, and belief degree of the result. Next, to evaluate the belief degree more reasonably, an Evidence-discount-based basic probability assignment generating method is proposed based on D-S theory to describe the interference in the belief degree. Moreover, a belief evaluation algorithm is proposed to calculate the belief degree of the coverage. Finally, an evidence-Discount-based Confident Information Coverage Belief Reliability Evaluation (D-CICBRE) algorithm is proposed to evaluate CICBR. The simulation result indicates that the proposed method can obtain higher and more reasonable reliability. Therefore, the proposed D-CICBRE algorithm can be used to evaluate the reliability of ICP systems more accurately and reasonably. © 2023 IEEE.

关键词： Sensor networks

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Backdoor Attacks on Bimodal Salient Object Detection with RGB-Thermal Data 24

Backdoor Attacks on Bimodal Salient Object Detection with RG...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Yin, Wen Zhu, Bin Benjamin Xie, Yulai Zhou, Pan Feng, Dan School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Microsoft Corporation Beijing China Wuhan National Laboratory for Optoelectronics 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 Wuhan430074 China

ISBN: (纸本)9798400706868

RGB-Thermal Salient Object Detection (RGBT-SOD) plays a critical role in complex scene recognition applications, such as autonomous driving. However, security research in this domain is still in its infancy. This paper presents the first backdoor attack on RGBT-SOD systems, generating saliency maps on triggered inputs that depict non-existent salient objects chosen by the attacker or falsely mark an entire image as fully salient or entirely non-salient. We uncover that triggers have an influence range for generating non-existent salient objects, supported by a theoretical analysis. Extensive experiments show the effectiveness of our attack in both digital and physical-world scenarios. Notably, our dual-modality backdoor attack achieves an Attack Success Rate (ASR) of 86.72% with only five pairs of poisoned images in model training. After investigating potential countermeasures, we find them inadequate in mitigating our attacks, highlighting the urgent need for robust defenses against sophisticated backdoor attacks in RGBT-SOD systems. © 2024 ACM.

关键词： Object detection

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Intelligent Contract Timestamp Vulnerability Detection Based on key Control Flow Graph 36

Intelligent Contract Timestamp Vulnerability Detection Based...

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36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Quan, Qing Yu Zeng, Cheng Wang, Yi Feng Wang, Jiao Ran School of Computer Science and Information Engineering Hubei University Wuhan China School of Artificial Intelligence Hubei University Wuhan China Key Laboratory of Intelligent Sensing System and Security Hubei University Ministry of Education Wuhan China

ISBN: (纸本)1891706594

The extensive application of smart contract technology in the blockchain domain has positioned it as a key component of the digital economy. However, as the application scope of smart contracts expands, security issues have become increasingly evident, resulting in substantial economic losses. Current vulnerability detection methods largely depend on analyzing complete smart contract source code, which struggles to filter out extensive redundant information when identifying individual vulnerabilities. This leads to an insufficient exploration of the characteristics of smart contract code, which in turn results in lower overall accuracy of detection methods. In this context, the study introduces a method that transforms smart contract source code into a control flow graph, filtering out the main basic blocks based on key information about timestamp vulnerabilities. Subsequently, individual operation audits are conducted on these basic blocks, combined with the graphical structure of the control flow graph, utilizing gated graph neural networks for detecting timestamp vulnerabilities in smart contracts. Comparative experiments were performed on two public datasets, and the results indicate that compared to state-of-the-art detection methods, the proposed approach has improved the accuracy and F1 score for detecting timestamp vulnerabilities in smart contracts by 1.65% and 6.51%, providing valuable application prospects for the security of smart contracts. © 2024 Knowledge systems Institute Graduate School. All rights reserved.

关键词： Graph neural networks

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Machine Learning is All You Need: A Simple Token-Based Approach for Effective Code Clone Detection 24

Machine Learning is All You Need: A Simple Token-Based Appro...

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44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

作者： Feng, Siyue Suo, Wenqi Wu, Yueming Zou, Deqing Liu, Yang Jin, Hai Huazhong University of Science and Technology China Nanyang Technological University Singapore Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security China Jinyinhu Laboratory Wuhan430074 China School of Cyber Science and Engineering Hust Wuhan430074 China School of Computer Science and Technology Hust Wuhan430074 China

ISBN: (纸本)9798400702174

As software engineering advances and the code demand rises, the prevalence of code clones has increased. This phenomenon poses risks like vulnerability propagation, underscoring the growing importance of code clone detection techniques. While numerous code clone detection methods have been proposed, they often fall short in real-world code environments. They either struggle to identify code clones effectively or demand substantial time and computational resources to handle complex clones. This paper introduces a code clone detection method namely Toma using tokens and machine learning. Specifically, we extract token type sequences and employ six similarity calculation methods to generate feature vectors. These vectors are then input into a trained machine learning model for classification. To evaluate the effectiveness and scalability of Toma, we conduct experiments on the widely used BigCloneBench dataset. Results show that our tool outperforms token-based code clone detectors and most tree-based clone detectors, demonstrating high effectiveness and significant time savings. © 2024 ACM.

关键词： Machine learning

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Corrigendum to “Design of sub-synchronous oscillation proportional resonance damping controller for doubly-fed wind turbines based on impedance analysis” [Int. J. Electr. Power Energy Syst. 162 (2024) 110333]

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International Journal of Electrical Power & Energy systems 2025年 164卷

作者： Shiyao Qin Zehui Wang Shaolin Li Renjie Chen Lingjie Jia Xiaotao Peng China Electric Power Research Institute Haidian District Beijing China Hubei Key Laboratory of Power Equipment & System Security for Integrated Energy School of Electrical Engineering and Automation Wuhan University Wuhan China School of Electrical engineering and Automation Wuhan University Wuhan China Wuhan Iron & Steel Design & Research Institute China

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Nanomaterials-based gas sensors of SF_(6)decomposed species for evaluating the operation status of high-voltage insulation devices

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High Voltage 2019年第4期4卷 242-258页

作者： Hao Cui Xiaoxing Zhang Jun Zhang Ying Zhang State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing UniversityChongqing 400044People's Republic of China School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlanta 30332GAUSA School of Electrical and Electronic Engineering Hubei University of TechnologyHubei 430068People's Republic of China

In recent years,many novel sensing nanomaterials were explored experimentally and theoretically as chemical gas sensors to detect sulphur hexafluoride(SF_(6))decomposed species with rapid response,high sensitivity and selectivity,as well as low cost,in order to estimate and guarantee the operation status of SF_(6)insulation ***,there has been no systemic review yet to comprehensively understand their sensing mechanism and conclude their sensing *** kinds of nanomaterials include traditional carbon-based materials such as carbon nanotube and graphene,metal oxides such as SnO_(2),ZnO and TiO_(2),transition metal dichalcogenides such as MoS_(2)monolayer and MoTe_(2)monolayer as well as graphene-like InN monolayer.A systemic review would be significant to give a profound insight into the exploration of nanomaterial-based chemical gas sensors in the field of electrical engineering and to help youth scholars first stepping in this field rapidly master the progress ***,the authors attempt to complete this review *** are hopeful that this review could provide some guidance to fulfil such purpose in the near future,and it would be full of perspective to explore more novel nanomaterials as defect indicators in SF_(6)insulated ***,it is their hope that this review could broaden the sensing application of admirable nanomaterials in many fields such as environmental monitoring and pollutant scavenging.

关键词： insulation high guarantee

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Intersecting-Boundary-Sensitive Fingerprinting for Tampering Detection of DNN Models 41

Intersecting-Boundary-Sensitive Fingerprinting for Tampering...

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41st International Conference on Machine Learning, ICML 2024

作者： Bai, Xiaofan He, Chaoxiang Ma, Xiaojing Zhu, Bin Benjamin Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China Microsoft United States School of Computer Science and Technology Huazhong University of Science and Technology China Cluster and Grid Computing Lab China

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. Copyright 2024 by the author(s)

关键词： HTTP

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On the security of Smart Home systems:A Survey

引用

Journal of Computer Science & Technology 2023年第2期38卷 228-247页

作者：袁斌万俊吴宇晗邹德清金海 School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Distributed System Security Huazhong University of Science and Technology Wuhan 430074China Hubei Engineering Research Center on Big Data Security Huazhong University of Science and Technology Wuhan 430074China National Engineering Research Center for Big Data Technology and System Huazhong University of Science and TechnologyWuhan 430074China Services Computing Technology and System Lab Huazhong University of Science and TechnologyWuhan 430074China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen 518057China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Lab Huazhong University of Science and TechnologyWuhan 430074China

Among the plethora of IoT(Internet of Things)applications,the smart home is one of the ***,the rapid development of the smart home has also made smart home systems a target for ***,researchers have made many efforts to investigate and enhance the security of smart home *** a more secure smart home ecosystem,we present a detailed literature review on the security of smart home ***,we categorize smart home systems’security issues into the platform,device,and communication *** exploring the research and specific issues in each of these security areas,we summarize the root causes of the security flaws in today's smart home systems,which include the heterogeneity of internal components of the systems,vendors'customization,the lack of clear responsibility boundaries and the absence of standard security ***,to better understand the security of smart home systems and potentially provide better protection for smart home systems,we propose research directions,including automated vulnerability mining,vigorous security checking,and data-driven security analysis.

关键词： IoT(Internet of Things) smart home IoT security smart home security

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Application Research of Vertical Federated Learning Technology in Banking Risk Control Model Strategy 21

Application Research of Vertical Federated Learning Technolo...

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21st IEEE International Symposium on Parallel and distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Luo, Yong Lu, Zhi Yin, Xiaofei Lu, Songfeng Weng, Yiting Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan China China Minsheng Banking Corp. Ltd. Beijing China Shenzhen Huazhong University of Science and Technology Research Institute Wuhan China Shu Lun Pan Beijing China

ISBN: (纸本)9798350329223

This study centers on the application of vertical federated learning technology in the context of Internet banking loans, with a particular focus on innovations in data privacy protection, risk control model algorithms, and secure multi-party computation. Currently, banking risk control strategies mainly rely on traditional data processing technologies, which often fall short in protecting user privacy and ensuring data usage efficiency. We adopt vertical federated learning technology, offering an innovative solution for the Internet banking loan scenario. Firstly, regarding data privacy protection, we propose a differential privacy mechanism to safeguard user-sensitive data. Secondly, we innovatively apply risk control model algorithms, facilitating collaborative modeling across multiple Internet loan platforms through federated learning. Furthermore, we introduce secure multi-party computation technology to ensure the secure transmission of data and confidentiality of computation processes during federated learning. Through empirical experiments on real Internet loan datasets, we validate the effectiveness and feasibility of our proposed methods. After implementing our risk control model, the credit approval rate increased from 3.44% to 18.2%, with a single-day high reaching 25.53%. The average loan amount increased by 7,700 yuan, and the average interest rate slightly declined by 0.48%, marking a significant improvement and breakthrough compared to traditional risk control models. This study offers innovative solutions for data privacy protection and risk control in the Internet loan scenario, providing safer and more reliable services for financial institutions and users. Moreover, our methods possess high practicality and promotional value. The potential widespread impact on the industry is profound. © 2023 IEEE.

关键词： Risk management

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