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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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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

2024 International Conference on Advanced Control Systems and Automation Technologies, ACSAT 2024

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

ISBN: (纸本)9798331541668

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. © 2024 IEEE.

关键词： Carbon sequestration

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User Identification based on Behavior Feature Analysis 7

User Identification based on Behavior Feature Analysis

引用

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

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

ISBN: (纸本)9798350382877

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. © 2023 IEEE.

关键词： Deep learning

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A Semi-Supervised Clustering-Based Approach for Sensing and Classifying Unknown Attacks in power Systems 7

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

引用

7th Asian Conference on Artificial Intelligence technology, ACAIT 2023

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

ISBN: (纸本)9798350359145

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. © 2023 IEEE.

关键词： Genetic algorithms

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Semi-supervised power Microservices Log Anomaly Detection Based on BiLSTM and BERT with Attention 2

Semi-supervised Power Microservices Log Anomaly Detection Ba...

引用

2nd International Conference on Advanced Electronics, Electrical and Green Energy, AEEGE 2023

作者： Zaojian, Dai Yong, Li Mu, Chen Liang, Chen Wengao, Fang Ziang, Lu State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co.Ltd. Nanjing China

ISBN: (纸本)9798350322866

The software industry has seen a rise in the size and intricacy of software in recent years, leading to a growing concern about how to accurately identify, analyze, and pinpoint abnormal behavior that may occur during system operation. power system microservices are becoming increasingly popular as a way to manage and monitor the complex and distributed attributes of modern power grids. System logs of power system microservices are used to document the status of power system and important occurrences at crucial points in order to help with troubleshooting performance problems and malfunctions, which plays a crucial role in maintaining the stability and reliability of power grids. Traditional anomaly detection methods based on white-box testing require access to the system source code and may not be applicable. Current abnormality detection methods based on system logs primarily focus on extracting log template characteristics, but they pay little attention to the semantic meaning of logs. In order to detect anomalies from system logs more effectively, this work proposes a log execution path anomaly detection method based on Bi-directional Long Short-Term Memory (BiLSTM) with attention. The semantics of log key is also taken into consideration using BERT model for semantic information extraction. Additionally, attention mechanism is used to adjust feature weights at different time steps and select important words. In training phase, a sliding window generates log key sequences from parsed logs to train the model. In detection phase, the model takes as input the parsed log sequence and outputs the probability distribution of the next log key. Experimental results indicate that the proposed method has outperformed conventional approaches on several metrics, including recall rate and F1 score. © 2023 IEEE.

关键词： Anomaly detection

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A 5G Slice Traffic Anomaly Detection Method Based on Convolution Neural Network 8

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

引用

8th International Conference on Signal and Image Processing, ICSIP 2023

作者： Lu, Chen Tao, Zhang Yuanyuan, Ma State Grid Smart Grid Research Institute Co. Ltd State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology Nanjing210003 China

ISBN: (纸本)9798350397932

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. © 2023 IEEE.

关键词： Neural network models

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An instruction level attack detection method for new power load system based on LSTM

An instruction level attack detection method for new power l...

引用

2024 International Conference on Smart grid and Artificial Intelligence, SGAI 2024

作者： Yao, Qigui Zhang, Xiaojian Fei, Jiaxuan Yao, Minglu Li, Ye State Grid Smart Grid Research Institute Co. Ltd. Nanjing210003 China State Grid Laboratory of Power Cyber-security Protection and Monitoring Technology China State Grid Tianjin Electric Power Company Tianjin300384 China

To realize the efficient instruction level attack detection of the new power load resource management system under the load resource coordination and control interactive service scenario, this article proposes a novel LSTM adaptive sequence classification detection method combining MTM feature modeling and GAN countermeasure sample generation. In this method, the system operation instruction model based on MTM is established, and the generalized instruction features are expressed in detail. Simultaneously, large quantities of confrontation samples are generated by the GAN Network, which is similar to the real samples, and the problem of data imbalance in model training is solved. Finally, this paper proposes an adaptive coefficient-based LSTM for system instruction feature learning and testing and realizes the detection and recognition of hidden instruction level attacks in the new power load resource management system, which is more accurate and efficient than other methods. © Published under licence by IOP Publishing Ltd.

关键词： Long short-term memory

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Microservice Anomaly monitoring Method based on Multi-dimensional Spatiotemporal Feature Reconstruction 4

Microservice Anomaly Monitoring Method based on Multi-dimens...

引用

4th International Symposium on Artificial Intelligence and Intelligent Manufacturing, AIIM 2024

作者： Zeng, Jing Lai, Ji Li, Shuo Li, Xian Yang, Ruxia Information Communication Branch State Grid Jibei Electric Power Co. Ltd Beijing China State Grid Laboratory of Power cyber-Security Protection and Monitoring Technology China Electric Power Research Institute Nanjing China

ISBN: (纸本)9798331541729

In a modern microservice architecture, the large-scale distribution of services and high concurrency access make anomaly monitoring particularly complex. Traditional monitoring methods usually rely on a single data dimension, such as request delay or resource utilization, making capturing complex abnormal patterns challenging. Therefore, this paper proposes a microservice anomaly monitoring method based on multidimensional spatiotemporal feature reconstruction, aiming to reconstruct the complete feature space by integrating different data sources to improve the accuracy and robustness of anomaly detection. This paper's method first extracts adequate data, such as service response time and service call chain from the tracking dataset, with time and space as the core and constructs it into a multi-dimensional feature matrix, fully considering the spatial and temporal correlation between microservices. Second, this paper introduces a deep learning model combining convolutional neural networks and gated recurrent cells to capture dynamic dependence and feature interactions in spatiotemporal dimensions. The convolutional neural network module extracts the spatial dependence through the convolution operation, while the gating cycle unit module captures the changing trend of the time series. By paralleling two modules and introducing the attention mechanism, the model is optimized with the reconstruction error, and finally, the network abnormality is judged by setting the threshold. © 2024 IEEE.

关键词： Convolutional neural networks

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A New Way of (Decentralized) Identity Authentication and Continuous Authentication Following X3DH Framework

A New Way of (Decentralized) Identity Authentication and Con...

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2024 International Conference Optoelectronic Information and Optical Engineering, OIOE 2024

作者： Zhao, Yufei Wang, Xinzhe Zhang, Daojuan Wu, Tianqi Gao, Lifang Chen, Ling State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology China Electric Power Research Institute Co. Ltd. Beijing China State Grid Hebei Information and Telecommunication Branch Hebei China School of Cyber Science and Engineering Sichuan University Chengdu China

ISBN: (数字)9781510688209

ISBN: (纸本)9781510688193

Authentication and key establishment are fundamental goals of modern cryptography. There are plenty of works that focus on secure and efficient design of them. Among them, the Diffie-Hellman key agreement protocol is the most widely used ones. They daily used Transport Lay security (TLS) protocol that are the basis of HTTPS is what protect us when we are surfing. However, as we all known, the protocol is not secure against man in the middle attack, so various kinds of authentication methods are proposed. With the development of technique for digital identity, we found the new needs to integrate authentication in the Diffie-Hellman kind of key agreement protocol. In this paper, we revisit the classical problem of authentication and key establishment. Inspired by the Signal protocol, we propose a new scheme of authentication with identity, we also note that although the decentralized identity is another kind of identity infrastructure, by the use of credential that is constructed with identity attributes, our scheme supports both centralized and decentralized identity system. With the development of Internet of Things (IoT), secure and efficient authentication and key establishment for sensors with limited computation power is also an urgent need. To this end, we realize a new authentication way and integrate it to X3DH protocol, which is the key component of Signal protocol. Besides, we consider the function of continual authentication that is very necessary in long-term secure communication. Last but not the least, we also give detailed analysis on security and computation, which shows the security and efficiency of our scheme. © 2025 SPIE.

关键词： Information security Internet of things Receivers Data communications Network security power grids Sensors Symmetric key encryption Telecommunications Transportation security

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Containerization of the security Application on Electric power IoT Terminal 4

Containerization of the Security Application on Electric Pow...

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4th International Conference on Information Science, Parallel and Distributed Systems, ISPDS 2023

作者： Wang, Tengyan Shao, Zhipeng Li, Nige Xu, Zheng State Grid Smart Grid Research Institute Co. Ltd State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology Nanjing China State Grid Shandong Electric Power Company Jinan China

ISBN: (纸本)9798350337181

The development of Internet of Things (IoT) technology brings convenience to the development of the electric power business but also brings security risks. Moreover, owing to the massive heterogeneity of IoT terminals, traditional security protection measures are restricted in terms of deployment, adaptation, operation, maintenance, and other aspects. Docker containers take up fewer resources and are easy to transplant;therefore, they are suitable for deploying secure ap-plications in the IoT computing environment with limited resources and power. This study fo-cuses on the container virtualization technology of the security applications on electric power IoT terminals. We propose a general process for application containerization to standardize the work done in each stage of containerization. The union file system is used to build a multilayer container architecture to reduce resource usage and improve deployment speed. Thus, the secu-rity applications can be run in containers to realize their real-time deployment and real-time op-eration in the heterogeneous intelligent electric power IoT terminals to achieve the security ca-pabilities of on-demand orchestration and timely response of electric power IoT terminals. © 2023 IEEE.

关键词： Internet of things

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Research on power System cyber security Defense based on ATT&CK Framework 3

Research on Power System Cyber Security Defense based on ATT...

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3rd International Conference on Energy Engineering and power Systems, EEPS 2023

作者： Wang, Tengyan Ma, Yuanyuan Shao, Zhipeng Xu, Zheng State Grid Smart Grid Research Institute Co. Ltd State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology Nanjing China State Grid Shandong Electric Power Company Jinan China

ISBN: (纸本)9798350313857

Advanced persistent threat (APT) attack is one of the most serious threats to power system cyber security. ATT&CK framework integrates the known historical and practical APT attack tactics and techniques to form a general language for describing hacker behavior and an abstract knowledge base framework for hacker attacks. Combined with the ATT&CK for ICS framework, this paper combed the known attack techniques used by viruses or hacker groups aimed at cyberattacks on infrastructure, especially power systems. Then found the corresponding mitigations for each attack technique, and merged them. Next, we listed the high frequency and important mitigations for reference. At last, we proposed a cyber security defense model suitable for ICS to provide a reference for security teams on how to apply ATT&CK and other similar cyberattack frameworks. © 2023 IEEE.

关键词： cybersecurity

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