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2021 5th International Conference on Electrical, Mechanical and Computer Engineering, ICEMCE 2021

作者： Li, Huifeng Li, Tiecheng Yao, Qigui Li, Weixun Yang, Libo Sun, Guanghui State Grid Hebei Electric Power Research Institute Hebei Shijiazhuang China State Grid Hebei Electric Power Co.Ltd Hebei Shijiazhuang China Global Energy Interconnection Research Institute Co.Ltd Jiangsu Nanjing China State Grid Key Laboratory of Information and Network Security Jiangsu Nanjing China

The security threat of power monitoring system is increasing, it is valuable to carry out effective risk assessment, which helps to find the security threats of the system in time, then carry out security management and avoid disasters. This paper presents a risk assessment method of power monitoring system based on Fuzzy Analytic Hierarchy Process (FAHP) and CVSS. Firstly, Considering the structure and security requirements of power monitoring system, the analytic hierarchy process (AHP) model is established, and CVSS is used to quantify the evaluation indicators. Then, the fuzzy consistent judgment matrix is constructed by using the results of index quantification by using AHP and fuzzy comprehensive evaluation theory, and the power monitoring system is comprehensively evaluated from the bottom to the top. Finally, the risk evaluation value and safety level of the system are obtained. The example shows that the method can effectively overcome the difficulty of AHP in checking the consistency of judgment matrix, and can effectively quantify the system risk, and provide reference and basis for risk management decision-making of power monitoring system. © 2021 Institute of Physics Publishing. All rights reserved.

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Monocular Camera-Based Visual Truck Type Recognition

SSRN

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SSRN 2024年

作者： Luo, Haibo Ye, Huaisheng Feng, Chao Shi, Xin Xie, Guoqi School of Computer and Data Science Minjiang University Fuzhou350018 China College of Computer and Data Science Fuzhou University Fuzhou350018 China College of Network and Information Security Fujian Agriculture and Forestry University Fuzhou350018 China Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University Changsha410082 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

Truck overload and over-limit are the primary causes of infrastructure damage and traffic safety accidents. In the past 2 years, researchers have started to deploy intelligent Internet of Things system at the source of truck delivery to enforce permit control. Truck type recognition is the core of such a system. However, existing methods suffer from limitations such as low recognition accuracy, high costs, difficult maintenance, and the need for road surface modifications. This study proposes a monocular camera-based visual truck type recognition method. First, the optimized lightweight deep neural network based on machine vision is utilized to detect truckaxles and recognize wheel hub categories. Next, a tracking algorithm and the video streamcontext information are employed to complete the robust statistics of the axle by neighborhoodcounting. Finally, the proposed method further achieves truck type recognition by leveragingavailable prior knowledge. Experimental results demonstrate that this method exhibits superiortime efficiency compared with existing algorithms, and it reduces average processing time by92.37% to 96.24% while ensuring high accuracy. Moreover, the method has been deployed onembedded devices with low computing power to enable real-time operation onsite. © 2024, The Authors. All rights reserved.

关键词： Object detection

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SONICUMOS: An Enhanced Active Face Liveness Detection System via Ultrasonic and Video Signals

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IEEE Transactions on Mobile computing 2025年

作者： Wu, Yihao Jiang, Peipei Cheng, Jianhao Zhao, Lingchen Shen, Chao Wang, Cong Wang, Qian Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Wuhan University School of Cyber Science and Engineering Hubei430072 China Laboratory for AI-Powered Financial Technologies Ltd. 999077 Hong Kong Xi'An Jiaotong University MOE Key Laboratory for Intelligent Networks and Network Security Xi'an710049 China Xi'An Jiaotong University School of Cyber Science and Engineering Xi'an710049 China City University of Hong Kong Department of Computer Science 999077 Hong Kong

SONICUMOS is an enhanced behavior-based face liveness detection system that combines ultrasonic and video signals to sense the 3D head gestures. As face authentication becomes increasingly prevalent, the need for a reliable liveness detection system is paramount. Traditional behavior-based liveness detection methods (e.g., eye-blinking, nodding, etc.), which are widely deployed in mission-critical scenarios like finance and banking applications today, are prone to advanced media-based facial forgery attacks. SONICUMOS aims to incorporate the traditional behaviorbased method for active liveness detection without introducing extra user burden. By employing ultrasonic signals, SONICUMOS capitalizes on the head gestures, significantly raising the security bar. Our approach utilizes the frequency-modulated continuouswave (FMCW) ultrasonic radar for robust 3D gesture recognition compatible with face authentication. We also propose a new dual-feature fusion network that integrates audio and video features at the feature level to increase detection accuracy and resilience against numerous attacks. Our prototype has been tested on seven off-the-shelf Android/iOS smartphones, achieving an overall detection accuracy of 95.83% at an equal error rate (EER) of 4.96% when dealing with 3D impersonation attacks © 2025 IEEE.

关键词： Frequency modulation

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security Baseline Evaluation and Standard Research of power Intelligent Internet of Things Terminal 6

Security Baseline Evaluation and Standard Research of Power ...

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6th International Conference on Signal and Image Processing, ICSIP 2021

作者： Chen, Tao Fei, Jiaxuan Guo, Zhimin Lv, Zhou Yuan, Tao State Grid Xinjiang Electric Power Company Internet Department Urumqi China Global Energy Interconnection Research Institute Co. Ltd. State Grid Key Laboratory of Information and Network Security Nanjing China State Grid Henan Electric Power Company State Grid Henan Electric Power Research Institute Zhengzhou China State Grid Urumqi Power Company Urumqi China

ISBN: (纸本)9780738133737

The power Internet of Things is the evolution direction of the power industry. While improving the convenience of power grid operation, it also brings emerging network security risks to traditional industries. Baseline assessment is based on a set of minimum security requirements specification sets as a method to grasp the security status of information systems. The application of security baseline assessment in the Internet of Things is still few. This article first studies the security standards of the Internet of Things, establishes a security baseline, designs a set of security baseline evaluation models for power intelligent Internet of Things terminals, and gives the operation process and scoring algorithm. The analysis result shows that our method is suitable for security evaluation of power intelligent internet of things terminal. © 2021 IEEE.

关键词： Internet of things

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MedX-Net: Hierarchical Transformer with Large Kernel Convolutions for 3D Medical Image Segmentation

MedX-Net: Hierarchical Transformer with Large Kernel Convolu...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Lin Lu Qingzhi Zou Key Laboratory of Computing Power Network and Information Se-curity Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Due to the exceptional performance of Transform-ers in 2 $D$ medical image segmentation, recent work has also introduced them into 3D medical segmentation tasks. For instance, Swin UNETR and other hierarchical Transformers have reintroduced prior knowledge from several convolutional networks, further enhancing the volume segmentation capabilities of models. The efficacy of these hybrid methodologies is primarily attributed to the substantial quantity of parameters and the nonlocal self-attention mechanism with a large receptive field. We argue that the behavior of these methods' large receptive fields can be simulated by employing fewer parameters through the utilization of depth-wise convolutions with large kernel. Within this manuscript, we introduce a lightweight volume segmentation model called MedX-Net, which uses convolutional network modules to simulate hierarchical Transformers for robust volume segmentation. Firstly, inspired by the hierarchical Transformer module of Swin UNETR, we investigate large-kernel depth-wise convolutions with different sizes to achieve a reduced model parameter count while maintaining a large global receptive field. Secondly, we replace the multilayer perceptron (MLP) in the hierarchical Transformer module with Inverted Bottleneck with Depthwise Convolution Enhancement(DWCE) to improve model performance with fewer activation and normalization layers, further reducing the parameter count. We validate the effectiveness and efficiency of our model for volume segmentation on three public datasets: Synapse, BTCV and ACDC. On the Synapse dataset, compared to Swin UNETR, our model achieves an improvement from 83.48% to 87.21% in Dice score. Compared to the result of 86.57% achieved by nnFormer, our model achieves superior performance while reducing the model parameter count by 64%.

关键词： Image segmentation Solid modeling Visualization Three-dimensional displays Current transformers Multilayer perceptrons Convolutional neural networks Kernel Synapses Biomedical imaging

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Federated Coordinate Descent for Privacy-Preserving Multiparty Linear Regression

arXiv

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arXiv 2022年

作者： Leng, Xinlin Li, Chenxu Xu, Weifeng Sun, Yuyan Wang, Hongtao School of Control and Computer Engineering North China Electric Power University Baoding071051 China School of Computer Science and Technology Shandong University of Finance and Economics Jinan250014 China Beijing Key Laboratory of IoT Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Hebei Key Laboratory of Knowledge Computing for Energy & Power Baoding071051 China

Federated linear regressions have been developed and applied in various domains, where multiparties collaboratively and securely perform optimization algorithms, e.g., Gradient Descent, to learn a set of optimal model weights. However, traditional Gradient-Descent based methods fail to solve problems which contain L1regularization in objective functions, such as lasso regression. In this paper, we present Federated Coordinate Descent, a new distributed scheme called FCD, to address this issue securely under multiparty scenarios. Specifically, through secure aggregation and noise perturbations, our scheme guarantees that: (1) no local information is leaked to other parties, and (2) global model weights are not exposed to cloud servers. The proposed FCD scheme fills the gap between multiparty secure learning and coordinate descent methods, and is applicable for general linear regressions, including linear, ridge, and lasso regressions. Theoretical analyses prove the security guarantees of our scheme against semi-honest attackers. Experimental results on three UCI datasets demonstrate that regressions under our FCD scheme are effective as centralized models. The performance evaluation on synthetic dataset also reveals that our scheme achieves efficient properties on computation cost and communication *** Codes 68Q32 Copyright © 2022, The Authors. All rights reserved.

关键词： Gradient methods

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Research on security Risk Analysis and Protective Measures of power Internet of Things 5

Research on Security Risk Analysis and Protective Measures o...

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5th IEEE information Technology, networking, Electronic and Automation Control Conference, ITNEC 2021

作者： Zou, Zhenwan Li, Yang Yang, Huiting Shi, Congcong Yang, Ruxia Electric Power Research Institute of State Grid Xinjiang Electric Power Co. Ltd. Urumqi China State Grid Xinjiang Electric Power Co. Ltd. Urumqi China Global Energy Interconnection Research Institute Co. Ltd. Nanjing210003 China State Grid Key Laboratory of Information and Network Security Nanjing210003 China

ISBN: (纸本)9781665415989

With the advancement of the construction of the power Internet of Things, the transformation of network applications to interconnection and data applications to interactive sharing has brought new challenges to power grid security protection. This paper conducts an in-depth analysis and research on the network security risks faced by all levels of the power Internet of Things, and proposes corresponding security protection measures and safety development suggestions to provide security service guarantees for various power Internet of Things businesses. © 2021 IEEE.

关键词： Risk analysis

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Efficient Batch Verification and Privacy-Preserving Data Aggregation Scheme in V2G networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Wang, Congcong Wang, Chen Shen, Jian Vasilakos, Athanasios V. Wang, Bin Wang, Weizheng Department of Software Engineering Nanjing University of Information Science and Technology Nanjing210044 China the Zhejiang Key Laboratory of Digital Fashion and Data Governance the Zhejiang Provincial International Cooperation Base for Science and Technology on Cloud Computing Security and Data Aggregation Zhejiang Sci-Tech University Hangzhou310018 China Zhejiang Provincial Innovation Center of Advanced Textile Technology Shaoxing312000 China College of Computer Science and Information Technology IAU Saudi Arabia Grimstad4879 Norway Network and Data Security Hangzhou310053 China Computer Science Department City University of Hong Kong Hong Kong the Division of Computer Science University of Aizu Fukushima965–8580 Japan

In recent years, against the backdrop of smart grids, vehicle-to-grid (V2G) interaction has become a hot topic. With the increasing number of new energy vehicles, handling massive amounts of electricity transaction data has become an urgent problem in need of resolution. Ensuring the security of the vast quantities of electricity transaction data has emerged as a significant challenge. Therefore, two data aggregation schemes are proposed in this paper, both supporting batch verification and privacy protection in centralized and distributed scenarios, respectively. Firstly, an improved Paillier cryptosystem is utilized to achieve lightweight encryption in the proposed schemes. In addition, a novel distributed data aggregation structure that combines a large number of geographically distributed fog nodes with centralized cloud servers is introduced. This structure not only meets the requirements of lightweight encryption and resistance against single point failures but also reduces the communication complexity of distributed data aggregation to O(n). According to the security proofs and experimental analysis, it can be demonstrated that both proposed schemes are effective and secure, meeting the lightweight requirements of modern smart grid systems. © 2025 IEEE. All rights reserved,

关键词： Data aggregation

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Intelligent decoding method for Ground-penetrating radar based on improved YOLOv5

Intelligent decoding method for Ground-penetrating radar bas...

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Computer, Big Data and Artificial Intelligence (ICCBD+AI), International Conference on

作者： ZeYu Chen YongKang Wang HongLiang Liu Chuan Wang XinBo Jiang Ning Zhang Zhen Huang Lei Jiang 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 Technological Innovation Department Shandong Hi-Speed Group Co Ltd JiNan China School of Qilu Transportation Shandong University JiNan China No.1 Engineering Development Co. Ltd. of China Railway 14Th Bureau Group RiZhao China

ISBN: (数字)9798331533991

ISBN: (纸本)9798331534004

Ground-penetrating radar (GPR) is a non-invasive detection technique that effectively gathers information about subsurface materials. In tunneling projects, GPR can provide a fast and accurate forecast of the geological conditions in front of tunnel boring, which provides an important reference basis for tunnel construction. Currently, the interpretation of GPR images relies heavily on manual expertise, resulting in low efficiency and minimal automation. This creates an urgent need for effective and accurate identification of poor geological conditions depicted in radar images. In this study, we present the GPR-YOLOv5 model, which is designed to detect unfavorable geologic features ahead of the tunnel face from GPR images. We enhance this model with the CA attention mechanism, GSConv module, ghostC3 module, and the VariFocal loss function to improve decoding accuracy while minimizing computational load. Additionally, we integrate this algorithm with established modern object detection techniques, and our experimental results demonstrate its effectiveness.

关键词： YOLO Accuracy Ground penetrating radar Attention mechanisms Geology Computational modeling Radar imaging Tunneling Decoding Load modeling

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network security Situational Awareness Model Based on Threat Intelligence 14th

Network Security Situational Awareness Model Based on Threat...

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14th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2021

作者： Zhang, Hongbin Yin, Yan Zhao, Dongmei Liu, Bin Gao, Hongbin School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050018 China Hebei Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang050024 China School of Economics and Management Hebei University of Science and Technology Shijiazhuang050000 China Research Center of Big Data and Social Computing Hebei University of Science Shijiazhuang05000 China

ISBN: (纸本)9783030898137

In order to deal with the problems that the increasing scale of the network in the real environment leads to the continuous high incidence of network attacks, the threat intelligence was applied to situational awareness, and the situational awareness model based on random game was constructed. Threat perception of the target system was performed by comparing the similarity between the exogenous threat intelligence and the internal security events of the system. At the same time, internal threat intelligence was generated based on the threat information inside the system. In this process, game theory was used to quantify the current network security situation of the system, evaluate the security status of the network. Finally, the prediction of the network security situation was realized. The experimental results show that the network security situation awareness method based on threat intelligence can reflect the changes in the network security situation and predict attack behaviors accurately. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Game theory

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