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检索条件"机构=State Grid Laboratory of Power Cyber-security Protection and Monitoring Technology"

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power Internet of Things security Evaluation Method Based on Fuzzy Set Theory 15th

Power Internet of Things Security Evaluation Method Based on...

引用

15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Wang, Yuman Wu, Hongbin Wang, Yilei Wang, Zixiang Zhu, Xinyue Qian, Kexiang SGRI Power Grid Digitizing Technology Department State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co. Ltd. Beijing102200 China Internet Technology Center State Grid Zhejiang Electric Power Co. Ltd. Research Institute Hangzhou310000 China School of Computer Science Northeast Electric Power University Jilin Jilin City132012 China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100000 China

ISBN: (纸本)9789819994113

Aiming at the problems of high fuzziness and low integrity of the current power Internet of Things network security evaluation index system, the fuzzy set theory is used to evaluate the security issues of power IoT terminal, protocol, strategy, business operation, business management, employee operation and user operation of the power Internet of Things from two aspects of the power Internet of Things itself and manpower, the fuzzy judgment matrix is constructed, and the weight of index items is determined by AHP. To establish a comprehensive and reasonable power Internet of Things safety evaluation index system, overcome the shortcomings of single and quantitative analysis of power Internet of Things network risk, and realize the determination of fuzzy index weight combining quantitative and qualitative, so as to improve the accuracy of power Internet of Things safety evaluation. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Anomaly Detection Model for Data Interactions Based on Temporal Features in Electric power Data Centers 8

Anomaly Detection Model for Data Interactions Based on Tempo...

引用

8th International Conference on Communication, Image and Signal Processing, CCISP 2023

作者： Yu, Pengfei Zhou, Xiaoming Chen, Longdan Qi, Jun Bai, Liang State Grid Smart Grid Research Institute Co. LTD State Grid Laboratory of Power cyber-Security Protection and Monitoring Technology Nanjing China State Grid Liaoning Electric Power Supply Company Limited Liaoning Shenyang China Beijing University Of Posts and Telecommunications Beijing China State Grid Liaoning Electric Power Supply Co. LTD. Information & Telecommunication Branch Liaoning Shenyang China

ISBN: (纸本)9798350305838

The current power data centers face the risk of data leakage during the data exchange process. Given the increasing scale and complexity of power data centers, traditional methods for identifying anomalies in the temporal characteristics of data interactions are not applicable. However, there is currently no specific model available for identifying abnormal data interaction behavior in the context of power data centers. Directly using existing models would result in low efficiency and fail to meet the requirements of power data center systems. This article proposes a model, VAE-GAN, for identifying abnormal data interaction behavior with multivariate temporal characteristics specifically designed for power data center business scenarios. The Variational Auto Encoder (VAE) architecture allows the model to learn in an unsupervised manner. The use of Generative Adversarial Networks (GAN) and their architecture helps address the drawbacks of VAE networks in terms of smoothness and reconstruction ability in latent spaces, thereby improving the model's generation and reconstruction performance. Experiments were conducted on a dataset provided by a power company to study the effectiveness of the VAE-GAN method in power data center business scenarios. The results demonstrate its usability in the context of power data centers. © 2023 IEEE.

关键词： Electronic data interchange

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Automated security Penetration Testing Based on Machine Learning

Automated Security Penetration Testing Based on Machine Lear...

引用

Electronic Information Engineering and Computer Communication (EIECC), International Conference on

作者： Peng Zhang Wenhui Wang Daojuan Zhang Hongbin Wu Longxi Han Qi Zhao State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology China Electric Power Research Institute Co. Ltd. Beijing China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

Penetration testing is a crucial measure for preventing cyberspace attacks. Typically, penetration testing is conducted manually, which presents challenges such as reliance on experience, high costs, and low efficiency. This study proposes an automated penetration testing method that integrates machine learning algorithms with Metasploit to achieve low-code generation and execution of penetration testing cases. This approach enhances testing efficiency, reduces costs, and mitigates dependence on experience. The study introduces a decision-making algorithm to facilitate the selection of exploit files during the generation of penetration testing cases and leverages Metasploit to implement basic penetration testing functionalities. Experimental results show that the algorithm achieves an accuracy of 73.16%, and the method successfully meets the objective of automated penetration testing.

关键词： Machine learning algorithms Costs Accuracy Decision making cyberspace Machine learning security Penetration testing Testing

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Research on digital modeling technology based on multi-source data of power grid 3

Research on digital modeling technology based on multi-sourc...

引用

3rd International Conference on Electronics, Electrical and Information Engineering, ICEEIE 2023

作者： He, Zhimin Yu, Hai Qiao, Junfeng Peng, Lin Wang, He Shen, Lianteng Li, Zhe Du, Xiaodong State Grid Smart Grid Research Institute Co. LTD Jiangsu Nanjing210003 China State Grid Laboratory of Power cyber-Security Protection and Monitoring Technology Jiangsu Nanjing210003 China China Electric Power Research Institute Beijing100000 China State Grid Hebei Electric Power Co. Ltd. Electric Power Science Research Institute Hebei Shijiazhuang050000 China

ISBN: (纸本)9781510671423

In this paper, the digital modeling technology based on multi-source data of power grid is studied. First, the theoretical system of power grid digital twin model is established to provide theoretical reference for the modeling and analysis of power grid digital twin and further application. Secondly, the construction of power grid digital twin model based on physics-guided deep learning framework, interface learning framework and probability graph model is studied to create the foundation and prerequisite for subsequent applications, and the foundation and core elements of power grid digital twin. Thirdly, the assembly and fusion of power grid equipment level model, power equipment cluster level model and power system level model based on multi-source heterogeneous data fusion technology are studied to realize multi-level digital twin model fusion for power grid. Finally, the paper studies the dynamic balance of update cost and accuracy to update the digital twin model of power grid, and realizes the flexible and dynamic update of power grid twin model. © 2023 SPIE.

关键词： Deep learning

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Anomaly Detection of Terminal access based on LSTM and ResNet 2

Anomaly Detection of Terminal access based on LSTM and ResNe...

引用

2nd International Symposium on Computer Applications and Information Systems, ISCAIS 2023

作者： Li, Nige Li, Yong Lu, Ziang Li, Duanchao Ding, Jinjin State Grid Smart Grid Research Institute Co. Ltd. 8 Nanrui Road Jiangsu Nanjing210003 China State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology 8 Nanrui Road Jiangsu Nanjing210003 China State Grid Anhui Electric Power Co. LTD. 9 Huangshan Road Anhui Hefei230022 China

ISBN: (纸本)9781510666665

Massive wireless debugging terminals and complex and diverse access requirements pose significant challenges to the secure access of substation terminal equipment. It is crucial to detect anomaly network traffic to ensure the security of terminal access to the substation. At present, network traffic anomaly detection based on traditional deep learning often has the problem of low computational efficiency or weak representation ability. Given the low computational efficiency of traditional deep learning, the residual network is used to extract spatial features of data, which can effectively improve convergence speed and time efficiency. Aiming at the problem of weak representation ability of traditional machine learning methods, the long short-term memory network (LSTM) is used to improve the representation ability to learn while learning the temporal characteristics of traffic and prevent the gradient from disappearing and network degradation. Experimental results show that compared with the traditional deep learning method, the accuracy of the proposed method is improved, the F1 score reaches 90.09, and the AUC is up to 0.981. By improving anomaly detection accuracy, the paper further guarantees terminal security. © 2023 SPIE.

关键词： Anomaly detection

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Evolution and Challenges of the New-Type power System: Transformation under the Carbon Neutrality Goal

Evolution and Challenges of the New-Type Power System: Trans...

引用

Advanced Control Systems and Automation Technologies (ACSAT), International Conference on

作者： Tengyan Wang Nige Li Lu Chen Mu Chen Nanjing Branch of China Electric Power Research Institute Co. Ltd State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology Nanjing China

ISBN: (数字)9798331541668

ISBN: (纸本)9798331541675

Amid the severe backdrop of global climate change, achieving carbon peaking and neutrality has emerged as a universal consensus and the cornerstone of energy policies worldwide. As the largest energy consumer and carbon emitter, China’s profound transformation of its power system holds paramount significance for global climate governance. This paper delves into the backdrop, characteristics, current development status, multifaceted challenges, and future trends of China’s newtype power system. It aims to provide theoretical underpinnings and practical guidance for accelerating the realization of the “dual carbon” (carbon peaking and neutrality) goals. Additionally, this research explores the application of advanced control and automation technologies in the power system transformation. For example, the intelligent grid technologies, such as the Internet of Things and big data analytics, play a crucial role in enhancing the grid’s sensing and control capabilities, which are essential aspects of advanced control systems. Moreover, the research on energy storage technology and its integration into the power system involves innovative control strategies and optimization algorithms, directly contributing to the field of automation technologies in the power sector.

关键词： Climate change Automation Carbon neutral Control systems Market research power systems Sensors Carbon Sustainable development Meteorology

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Design and Implementation of Identity Authentication Architecture System Fusing Hardware and Software Features

Design and Implementation of Identity Authentication Archite...

引用

Electronic technology and Information Science (ICETIS), International Conference on

作者： Ziang Lu Tengyan Wang Xin Liu Nige Li State Grid Laboratory of Power cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co.Ltd Nanjing China State Grid Shandong Electric Power Research Institute Jinan China

ISBN: (数字)9798350388343

ISBN: (纸本)9798350388350

Distributed resource access in the new power system will also occur in large-scale growth, due to the variety, large-scale, and in a complex open physical environment, in the relatively weak security protection measures, it is very easy to become the entry point for illegal personnel to implement network attacks. This paper proposes a fusion of software and hardware identity authentication architecture system, software fingerprint part based on network probing extracted message features, statistical features and time difference sequence features extracted based on bypass listening using direct splicing fusion to generate software fingerprints of electric power intelligent terminals, and then so that a variety of feature information can be taken into account to improve the recognition of the fingerprint features, hardware fingerprints part to take the extraction of the signal leading code part of the signal for statistical feature analysis, and finally based on the integration of the signal leading code part of the signal for statistical feature analysis. The hardware fingerprint part takes the part of extracting the leading code of the signal for statistical feature analysis, and finally based on the integrated learning fusion of software and hardware fingerprints as the device fingerprint features, which can largely solve the security authentication problem of electric power equipment based on HPLC and Modbus communication, and achieve more than 95% recognition ability under most of the machine learning classification methods.

关键词： Radio frequency Codes Authentication Computer architecture Fingerprint recognition Feature extraction Software

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Dynamic access control model based on attribute reachability 5

Dynamic access control model based on attribute reachability

引用

5th International Conference on Artificial Intelligence and Computer Science, AICS 2023

作者： Luo, Chen Yu, Pengfei Wei, Sijia Zhou, Xiaoming Qi, Jun Zhang, Wenjie State Grid Smart Grid Research Institute Co. Ltd. Jiangsu Nanjing210003 China State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology Jiangsu Nanjing210003 China State Grid Liaoning Electric Power Supply Co. Ltd. Information & Telecommunication Branch Liaoning Shenyang110006 China State Grid Liaoning Electric Power Supply Company Limited Liaoning Shenyang110006 China

ISBN: (纸本)9781510668621

Attribute-based access control models are widely used in permission management for resource access. By mining access control lists of policies, it can significantly reduce the cost of policy management and streamline the composition of policy rules. However, as resources increase, access policies will become complex. Uncontrolled attribute will lead to policy conflicts and thus policy mining will no longer be reliable. To address this issue, we propose a dynamic access control model based on attribute reachability. Firstly, we analysed the accessibility of attributes to ensure the reliability of authorised attributes. Secondly, we propose a multi-dimensional attribute management mechanism based on precondition-limited attributes, which enables permission passing and inheritance. On the basis of reachability access policy, we finally achieved secure policy mining changes by traversing user permission relationship tuples and constructing candidate rule seeds. © 2023 SPIE.

关键词： Access control models

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Deep Integration and Learning of Measurement Data in Active Distribution power Networks for Load Flow Estimation Considering Incomplete Information 6

Deep Integration and Learning of Measurement Data in Active ...

引用

6th IEEE International Electrical and Energy Conference, CIEEC 2023

作者： Diao, Ruisheng Zhou, Aihua Zeng, Ruiyuan Wang, Anqi Shen, Xiaofeng Shun, Jingde Gu, Hua ZJU-UIUC Institute Zhejiang University State Key Laboratory of Clean Energy Utilization Zhejiang Haining314400 China State Grid Smart Grid Research Institute Co. LTD. State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology Jiangsu Nanjing210003 China ZJU-UIUC Institute Zhejiang University Haining China Qingpu Power Supply Company Stage Grid Shanghai Municipal Electric Power Company Shanghai201799 China

ISBN: (纸本)9798350346671

To reach the goals of carbon peaking by 2030 and carbon neutrality by 2060 in China, a growing penetration of PV generation is integrated into the modern distribution power grid, causing challenges in operating the grid to meet security requirements due to the increased dynamics and stochastics. A deep neural network (DNN)-based method is proposed in this paper for estimating load flow solutions in active distribution power networks that typically suffer from incomplete measurements. Adaptive DNN models are trained from massive historical observations obtained from actual measurements or high-fidelity simulations, whose hyperparameters are tuned automatically. Comprehensive case studies are conducted on the IEEE 123-node feeder model with renewable generation considering various levels of missing information as inputs, which validate the effectiveness and robustness of the proposed method. © 2023 IEEE.

关键词： Electric power distribution

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A Semi-Supervised Clustering-Based Approach for Sensing and Classifying Unknown Attacks in power Systems

A Semi-Supervised Clustering-Based Approach for Sensing and ...

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Artificial Intelligence technology (ACAIT), Asian Conference on

作者： Yong Li Zhipeng Shao Tao Zhang Yuanyuan Ma State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Enstitute Co. Ltd Nanjing China

ISBN: (数字)9798350359145

ISBN: (纸本)9798350359152

To solve the problem of perceiving and classifying unknown attacks in power systems, the study proposes a method for perceiving and classifying unknown attacks in power systems based on semi-supervised clustering. The method first uses a hybrid algorithm of improved genetic algorithm (GA) and improved multi-grain cascade forest (GC-forest) power network system unknown attack for perception and detection, then increases the number of training data by marking extension algorithm, provides specific information of detected attack or event using the idea of semi-supervised clustering, and finally filters the clustered unknown attack or event using similarity metric method . The analysis of the performance results testing showed that the perceptual detection of unknown attacks by the improved GC-Forest and the improved Genetic Algorithm (GA) had a check-all rate, precision and accuracy of 97.1% and 96.9%, 98.9% and 98.6%, 99.0% and 98.7%, respectively, and the average computing time was 3.12s and 3.68, respectively. for the classification of unknown attack types, the semi-supervised clustering-based (SSL) classification method has 100%, 95% and 95% classification accuracy for normal, non-power network attacks and power network attacks, respectively, while the adaptive classification method has 100%, 92%-98% and 90%-95%, respectively. Taken together, these data illustrate that the research can efficiently and accurately sense and classify unknown attacks on power systems and provide some reference value for fault sensing and classification in other areas.

关键词： Measurement Radio frequency Clustering algorithms Training data Forestry Classification algorithms power systems

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