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Proceedings of the 2023 3rd Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum

作者： Lu Chen Yuanyuan Ma Tao Zhang State Grid Laboratory of Power cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co. Ltd China

ISBN: (纸本)9798400716362

In order to ensure the trusted access of power collection terminals in a disaster recovery environment, this paper proposes a cross-domain authentication scheme based on blockchain. Firstly, an improved PBFT algorithm based on reputation value election is designed, which increases the calculation of node reputation value and improves the dynamic addition and removal of consensus nodes and the election of master nodes based on node reputation value and failure conditions. Then, a cross-domain authentication model for power collection terminals based on blockchain is constructed. The blockchain certificate format and cross-domain authentication protocol are designed, and the security and efficiency are analyzed. The analysis results show that the scheme has security properties such as resistance to distributed attacks, and the communication overhead is lower than that of the traditional PBFT algorithm.

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Dynamic access control model based on attribute reachability 5

Dynamic access control model based on attribute reachability

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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 ...

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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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Research on Business Consistency Verification Techniques for Energy Big Data Privacy protection

Research on Business Consistency Verification Techniques for...

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International Conference on Computer Engineering and Applications (ICCEA)

作者： Xiuli Huang Pengfei Yu Yujia Zhai State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co. LTD Nanjing China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

In this paper, we study the consistency verification technology for multi-party collaborative privacy-preserving operations of energy big data, and propose a consistency assessment algorithm based on minimum hash signatures for the privacy and consistency challenges in energy big data processing. The algorithm realizes the assessment of the consistency of the target dataset through data compression and signature design of the data source, avoids direct access to the original data, and guarantees data privacy and consistency of processing results. The experimental results show that the algorithm is able to effectively assess data consistency on energy big datasets such as electricity and gas consumption, and the results are close to the exact algorithm with high credibility and efficiency.

关键词： Data privacy Soft sensors Electricity Data compression Collaboration Big Data Reliability engineering

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A 5G Slice Traffic Anomaly Detection Method Based on Convolution Neural Network

A 5G Slice Traffic Anomaly Detection Method Based on Convolu...

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Chen Lu Zhang Tao Ma Yuanyuan State Grid Laboratory of Power cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co.Ltd Nanjing China

As the network security threats caused by the Internet of Things (IoT) continue to increase, the attack surface continues to expand and the network heterogeneity increases, and the use of virtualization technology and distributed architecture increases. Protecting the future network will become a challenging field. This paper proposes the approach of converting 5G slice traffic into images for analysis by a CNN (Convolutional Neural Network) model, and uses NAS (Neural Architecture Search) to detect anomalous network traffic. In order to improve the accuracy and efficiency of the intrusion detection system, this paper uses SDS(Software Defined security) as a means to design a scalable, high-efficiency 5G traffic defense system. In the multiclass classification, the accuracy of CNN model is 94.9\%. The results from this method are promising as the model has performed in multiclass classification with an accuracy of 96.4\%. Furthermore, we suggest the optimal CNN design for the better performance through numerous experiments.

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Privacy-preserving and verifiable secure data aggregation for a national carbon emissions monitoring platform

Privacy-preserving and verifiable secure data aggregation fo...

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2023 International Conference on Computer Application and Information security, ICCAIS 2023

作者： Huang, Xiuli Zhu, Hongbin Yu, Pengfei Zhai, Yujia State Grid Laboratory of Power cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co. Ltd. No.8 NanRui Road Gulou District Jiangsu Nanjing China Big Data Center of State Grid Corporation of China China Reinsurance Building No.18 Luoma St. Xicheng District Beijing China

ISBN: (纸本)9781510675032

To obtain carbon peaking and carbon neutrality goals, it is necessary to establish a national carbon emission platform for monitoring and reporting carbon dioxide equivalent. Energy data contains a good deal of important data and hence is required to be securely shared. Secure data aggregation solutions are an effective method that enables the process of adding up the contributions from several parties without revealing their inputs. Most of these solutions can be achieved using Paillier encryption, which inherently has multiple rounds of communication for aggregating ciphertexts. In this work, we present a privacy-preserving and verifiable secure data aggregation for a national carbon emissions monitoring service system, which can be verifiable encryption. To achieve verification, we apply zero-knowledge proofs to enable underlying threshold encryption and bilinear aggregate signature to detect the validity of the summation results. Finally, we show the correctness, verification, and informal security analysis of our solution. Compared with the existing scheme, our work can reduce the number of rounds for aggregating ciphertexts and is suitable for the national carbon emission platform. © 2024 SPIE.

关键词： Carbon dioxide

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Concept and Research Framework for Coordinated Situation Awareness and Active Defense of cyber-physical power Systems Against cyber-attacks

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Journal of Modern power Systems and Clean Energy 2021年第3期9卷 477-484页

作者： Ming Ni Manli Li Jun’e Li Yingjun Wu Qi Wang the NARI Group Corporation(State Grid Electric Power Research Institute) NARI Technology Co.Ltd.and the State Key Laboratory of Smart Grid Protection and ControlNanjing 211106China the School of Cyber Science and Engineering Wuhan Universityand the Key Laboratory of Aerospace Information Security and Trusted Computing of the Ministry of EducationWuhan 430072China the College of Energy and Electrical Engineering Hohai UniversityNanjing 211100China the School of Electrical Engineering Southeast UniversityNanjing 210096China

Due to the tight coupling between the cyber and physical sides of a cyber-physical power system(CPPS),the safe and reliable operation of CPPSs is being increasingly impacted by cyber *** situation poses a challenge to traditional security defense systems,which considers the threat from only one side,i.e.,cyber or *** cope with cyberattacks,this paper reaches beyond the traditional one-side security defense systems and proposes the concept of cyber-physical coordinated situation awareness and active defense to improve the ability of *** example of a regional frequency control system is used to show the validness and potential of this ***,the research framework is presented for studying and implementing this ***,key technologies for cyber-physical coordinated situation awareness and active defense against cyber-attacks are introduced.

关键词： cyber-physical power system(CPPS) cyber security cyber-attack situation awareness active defense

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Research on interconnection technology of heterogeneous platforms for privacy computing for energy big data

Research on interconnection technology of heterogeneous plat...

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Energy and power Engineering, Control Engineering (EPECE), International Conference on

作者： Yujia Zhai Xiuli Huang Pengfei Yu State Grid Laboratory of Power cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co. LTD Nanjing China

ISBN: (数字)9798350366099

ISBN: (纸本)9798350366105

Energy data, especially power data, has the advantages of covering a wide range of industries, high value density, and good accuracy, relying on energy big data can widely describe all kinds of terminal production and life activities. However, the privacy computing platforms of various vendors often adopt different technical architectures and algorithm protocols in the early implementation process, which makes the implementation of various platforms have great differences, resulting in the direct interconnection between heterogeneous privacy computing platforms of various organizations. Aiming at the interoperability needs and problems of energy big data privacy computing platforms with different technical architectures, this paper decouple the management plane and data plane of the privacy computing platform, and in accordance with the principle of business priority, proposes a new privacy computing interconnection model of “unified business collaboration component + flexible configuration algorithm engine” suitable for energy big data. Through the design of the east-west and North-South interface framework of the privacy computing platform and the security access principle of the algorithm engine, the whole process control, cross-platform interconnection, and internal and external network penetration modeling and analysis of energy big data collaboration with the outside world are realized, which meets the key data security protection requirements of the state grid such as distributed computing and storage of data inside and outside the platform. It is of great significance to promote energy big data to empower external institutions such as governments, banks and operators.

关键词： Data privacy Privacy Computational modeling Government Collaboration Computer architecture Big Data

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User Identification based on Behavior Feature Analysis

User Identification based on Behavior Feature Analysis

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2023 7th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： Yuwen Dai Peng Zhang Tianqi Wu Hongbin Wu Daojuan Zhang Zhuo Lv Cen Chen State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute co. Ltd Beijing China State Grid Laboratory of Power Monitoring System Cyber Security Technical Standard Verification State Grid Henan Electric Power Research Institute Beijing China

The issue of identity management and information security in cyberspace is becoming increasingly important, and the issue of how to effectively identify through user behavior urgently needs to be addressed. At present, biometric recognition technologies have certain limitations. This article combines deep learning methods and uses the BERT model to extract input text information features to generate word vectors. It uses a bidirectional GRU network with local and global attention mechanisms for training, and finally uses a softmax classifier to classify the user identity recognition model. The model in this article has significantly improved its performance in user identity recognition tasks based on behavioral features.

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