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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Li, Xin Qu, Zhaoyang Yu, Tong Xie, Ming Yu, Fu Ding, Wei Northeast Electric Power University School of Electrical Engineering Jilin132012 China People's Bank of China Qinghai Provincial Branch Xining810000 China China Southern Power Grid Guangxi Power Grid Co. Ltd. Research Institute Nanning530023 China China Guizhou Power Grid Co. Ltd. Bijie Power Supply Bureau Bijie551700 China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China

ISBN: (纸本)9798350391367

With the increasing intelligence of power IoT terminal devices, the massive volume of data transmission, and the widespread adoption of shared services, traditional terminal boundary security mechanisms are unable to keep up with the rapid development of network technologies and applications. Additionally, they fail to trace and source network attacks, leading to frequent organized and purposeful malicious attack incidents. To address these issues, this paper proposes a research method for risk modeling and traceability of boundary attacks on power IoT terminals based on complex networks. First, based on the node information of power IoT terminal devices and complex network theory, a terminal boundary attack network (TBAN) is established. Next, using indicators related to complex network theory, such as degree, in-degree, and out-degree, the terminal boundary attack network is analyzed. Furthermore, a staged TBAN is proposed to analyze the attack risks at different times. Then, combining causal theory, a method for tracing and sourcing terminal boundary attacks based on a causal Bayesian network is proposed to identify and trace malicious attacks. Finally, validation and testing analysis are conducted using the actual deployment of power IoT terminal devices. The proposed analysis method transforms the spatial characteristics of the power IoT terminal boundary into a temporally sequential TBAN, which not only visually reflects attack risks but also reveals the temporal relationships of terminal boundary attacks between lines. © 2024 IEEE.

关键词： Internet of things

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$k$k-TEVS: A $ k$k-Times E-Voting Scheme on Blockchain With Supervision

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IEEE Transactions on Dependable and Secure computing 2024年第3期22卷 2326-2337页

作者： Yang Liu Debiao He Min Luo Lianhai Wang Cong Peng School of Cyber Science and Engineering Wuhan University Wuhan China Institute of Information Technology Shenzhen Institute of Information Technology Shenzhen China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shanghai Technology Innovation Centre of Distributed Privacy-Preserving Artificial Intelligence Matrix Elements Technologies Shanghai China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

The e-vote is regarded as a way to express the opinion that the voters ask for. Actually, the e-vote could be applied wildly like questionnaire, survey and feedback. Moreover, the coexistences of efficiency and security as well as transparency and privacy could be considered as building blocks in the e-vote system. The blockchain could provide a public access board to reduce the storage costs for the field consisted of the vote group manager (GM) with its vote assistants (VA). Particularly the $k$ -times anonymous authentication ( $k$ -TAA) could also be a practical approach to preserve voters’ privacy and reduce the computation costs during the vote process. However, the e-vote scheme with pure $k$ -TAA strategy could damage either the supervision of voting or the efficiency and consistency of authentication process. What’s more, the impacts of dishonest voters couldn’t be stopped until the vote end. To tackle these problems, we apply the accumulator technology to add or revoke the voters at any time and extend the framework of $k$ -TAA with the update process for the e-vote on blockchain for supervision ( $ k$ -TEVS). In our scheme, the voter updates his membership witness and proves the fact that he is still a valid member with respective VA under the latest accumulator value. What’s more, this witness update operation is not contained in the authentication process, which means that the authentication process is still constant and efficient. Moreover, our add or delete update process with signature of knowledge needs only one pairing operation. For the security, we prove that the relaxed anonymity still holds in the $ k$ -TEVS framework. Finally, We implement $ k$ -TEVS scheme, the Emura’s work [1] and the Huang’s work [2] for comparison. Then we make time cost and communication cost experiments, which present the feasibility and practicality of this scheme.

关键词： Blockchains Authentication Costs Protocols security Privacy Surveys Electronic voting Computer science information security

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Efficient Sensitive Data Identification Using Multi-Protocol Automatic Parsing and Vector Classification Based on Feature Fusion

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第22期48卷 55-62页

作者： Hu, Jian Zhang, Lina Lu, Guangqian Xu, Wenqian Jiang, Yixin Yunnan Power Grid Information Center China Southern Power Grid Kunming650217 China Electric Power Research Institute China Southern Power Grid Guangzhou510663 China Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou510663 China

At this stage, data has become essential and important information in people's daily life, and how to automate the identification and classification of sensitive attributes of structured datasets in the production environment, etc., has also become a key issue in data privacy protection. This paper proposes an efficient recognition technique for sensitive data based on feature fusion. The method can effectively improve the accuracy of sensitive data identification, realize the identification and classification of sensitive attributes, and take into account the correlation and association between attributes. Moreover, based on the artificial intelligence method, a high-precision and fine-grained identification technology for cross-domain encrypted traffic is constructed, which can support more than five abnormal data access behaviors that can be identified by the data security supervision system, and can support the recovery of traffic with business data not less than 500Mbps. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Data accuracy

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TSF-Net:Abnormal Video Behavior Detection Framework Based on Two-Stream Appearance Motion Semantic Fusion 23

TSF-Net:Abnormal Video Behavior Detection Framework Based on...

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Proceedings of the 2023 International Conference on information Education and Artificial Intelligence

作者： Bonan Li Rui Cao Xingang Wang Hong Zhang Jinyan Zhou China Radio and Television Shandong Network Co Ltd China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) China

ISBN: (纸本)9798400716157

Video anomalous behavior detection aims to detect rare and random anomalous events, which usually deviate from expectations. Existing detection methods do not consider the appearance semantics and motion semantics of the events in conjunction, so they lead to poor detection results. Since the appearance semantics and motion semantics of normal events are highly consistent and correspond, while the semantic consistency of abnormal events is low. Based on this, this paper proposes a two-stream fusion hybrid network for abnormal behavior detection. The appearance semantics and motion semantics of events are recorded separately using a multi-scale fused self-encoder to generate corresponding reconstructed appearance frames and reconstructed motion frames. Afterwards, the two frames are fused to generate the final prediction frame. We introduce temporal attention to enhance the model's ability to represent motion semantics and capture normal behavior with effective regular motion. Experiments on three standard public datasets show that the method proposed in this paper can accurately detect anomalous events with a performance comparable to state-of-the-art methods.

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Maximizing Area Coverage in Privacy-Preserving Worker Recruitment: A Prior Knowledge-Enhanced Geo-indistinguishable Approach

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IEEE Transactions on information Forensics and security 2025年 20卷 5138-5151页

作者： Zhang, Pengfei Cheng, Xiang Zhang, Zhikun Zhu, Youwen Zhang, Ji Anhui University of Science and Technology State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining the School of Computer Science and Engineering Anhui University of Science and Technology Huainan232001 China Yunnan University of Finance and Economics Yunnan Key Laboratory of Service Computing Yunnan 650221 China Key Laboratory of Equipment Data Security and Guarantee Technology Ministry of Education Key Laboratory of Computing Power Network and Information Security Ministry of Education China Beijing University of Posts and Telecommunications China Zhejiang University China Nanjing University of Aeronautics and Astronautics China University of Southern Queensland China

Worker recruitment for area coverage maximization, typically requires participants to upload location information, which can deter potential participation without proper protection. While existing studies resort to geo-indistinguishability to address this concern, they primarily focus on either specific task locations (Target Coverage) or operate under pre-defined recruitment quotas for an interested region (Area Coverage). These focuses not only yield suboptimal area coverage when scaled but also fail to leverage valuable prior knowledge in the form of participants’ noisy historical registered locations, to enhance both location obfuscation and worker identification processes. To address these limitations, we present WILTON, which optimizes area coverage under geo-indistinguishability by recruiting the minimum number of participants through the strategic utilization of noisy prior knowledge. In WILTON, to generate obfuscated locations, we propose a probabilistic and weight-aware input perturbation mechanism, which groups and weights prior locations rather than using only personal prior locations. To privately identify the recruited workers, we design a grid-based worker identification method, which provides a worst-case performance guarantee of ratio (Formula Presented) to the optimum. We provide a theoretical analysis of the privacy, utility, and complexity guarantees of WILTON. Experimental results over two real-world datasets and one synthetic dataset show that WILTON surpasses the state-of-the-arts by at least 8% in area coverage improvement. © 2005-2012 IEEE.

关键词： Knowledge acquisition

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Lightweight fuzzy testing method based on stake points of power control system

Lightweight fuzzy testing method based on stake points of po...

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International Conference on Computer Engineering and Applications (ICCEA)

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

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

Cyber threats are extremely frequent, and fuzz testing is an effective method to discover vulnerabilities. Regarding the optimization of fuzz testing cost, firstly, the cost distribution of each module during the fuzz testing process was studied. Then, based on the problems of damaged coverage information and excessive additional overhead in existing lightweight fuzz testing methods, a lightweight fuzz testing method based on stubs is proposed, which can be flexibly implemented while retaining fine-grained coverage information. The cancellation and recovery of stub points effectively reduces the execution overhead of fuzz testing. Finally, this method accepts the protocol program of the power system, effectively reducing the execution overhead of fuzz testing.

关键词： Costs Codes Protocols power control Life estimation Fuzzing power systems

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

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

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IEEE PES Asia-Pacific power and Energy Engineering Conference (APPEEC)

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

ISBN: (数字)9798350386127

ISBN: (纸本)9798350386134

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.

关键词： Resistance Photovoltaic systems Simulation Stability criteria power system dynamics Switches power system stability Wind power generation Frequency control Output feedback

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

Enhancing Power System Stability Against DoS Attacks Through...

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IEEE PES Asia-Pacific power and Energy Engineering Conference (APPEEC)

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

ISBN: (数字)9798350386127

ISBN: (纸本)9798350386134

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.

关键词： Fault tolerance Technological innovation Hardware-in-the-loop simulation Decentralized control power system stability Cyber-physical systems Stability analysis Distributed power generation Computer crime Testing

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Comprehensive Feature Processing Based on Attention Mechanism for Co-Salient Object Detection

Comprehensive Feature Processing Based on Attention Mechanis...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Guohua Lv Mao Yuan Zengbin Zhang Zhengyang Zhang Zhenhui Ding Guangxiao Ma Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Shandong Key Laboratory of Ubiquitous Intelligent Computing Jinan China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Co-salient object detection (CoSOD) aims to detect common salient objects across multiple related images. However, existing methods often struggle with limited attention coverage, missing some co-salient objects. To address this, we propose a two-stage feature processing module (FPM) comprising comprehensive feature extraction module (CFE) and feature enhancement module (FEM). CFE extracts comprehensive cosalient features while reducing background noise, and FEM enhances feature representation and adjusts attention weights for full object coverage. Additionally, we introduce an adversarial learning module (ALM) to improve prediction quality by reducing noise in the co-salient regions. Extensive experiments on three benchmark datasets—CoCA, CoSOD3k, and CoSal2015—demonstrate that our model significantly outperforms state-of-the-art methods. The source code is available at https://***/yaobaimiao/CFPAM.

关键词： Attention mechanisms Source coding Noise Object detection Benchmark testing Feature extraction Adversarial machine learning Finite element analysis Background noise Speech processing

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Research on Classification Method of Sensitive Structural Data of Electric power 12

Research on Classification Method of Sensitive Structural Da...

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12th IEEE International Conference on Electronics information and Emergency Communication, ICEIEC 2022

作者： Guan, Xiaojuan Zhou, Cheng Cao, Wantian Security State Grid Smart Grid Research Institute Co. Ltd State Grid Key Laboratory of Information & Network Power Grid Digitizing Technology Department Jiangsu Province Nanjing China

ISBN: (数字)9781665407540

ISBN: (纸本)9781665407540

This paper analyzes the necessity of classification of sensitive data from the importance of electric power structured data, and puts forward the classification identification method of structural sensitive data of electric power enterprises from data survey, data classification to construction of sensitive field database. © 2022 IEEE.

关键词： Sensitive data

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