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International Conference on Networking and Network Applications (NaNA)

作者： Weicong Huang Jieying Zhou Minghui Jin Zheng Han Sijia Wei State Grid Laboratory of Power cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co. LTD. Nanjing China National Power Dispatching and Control Center State Grid Corporation of China Beijing China State Grid Shanghai Municipal Electric Power Company Shanghai China

With the increasing frequency of data circulation and sharing in power monitoring systems, traditional network-centric protection strategies are insufficient to resist the growing risk of data leakage. This article analyzes the security risks in existing typical data circulation and sharing scenarios based on the demand for data security sharing in power monitoring systems. A data security protection technology framework covering the source layer, interaction layer, and terminal layer has been proposed from the perspectives of basic protection capabilities and key protection technologies, as well as an evaluation method for the protection technology capabilities of data circulation and sharing. This provides a reference idea for effectively enhancing the data circulation and sharing protection capabilities of power monitoring systems.

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Research and Application of Distribution Network Dispatching Operation Based on Digital Twinning technology

Research and Application of Distribution Network Dispatching...

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Information and Communication technology (ICTech), International Conference of

作者： Junfeng Qiao Zhujian Ou Lin Peng Fuyun Zhu Aihua Zhou Pei Yang State Grid Laboratory of Power cyber-Security Protection & Monitoring Technology State Grid Smart Grid Research Institute Co.ltd. Nanjing Jiangsu China Nantong Power Supply Company State Grid Jiangsu Electric Power Co. LTD. Nanjing Jiangsu China

In the electric distribution grid, the access of distributed electric power generation also brings new technical challenges. From the perspective of operation, after the distributed generation is related to the electric distribution grid, due to the uncertainty of wind energy, solar energy and other new energy, there will be higher harmonics in the distribution network that affect the power quality and power supply reliability. On the other hand, it is necessary to consider the economic cost and reasonably distribute the output status of different units in the distribution network to achieve the unity of economic and ecological benefits. While, it has occasion to research the dispatching method of electric distribution grid with distributed generation. This paper uses digital twinning technology to build virtual equipment modeling scheme, which integrates equipment basic data, system single line diagram and grid geographic coordinate data. With rapid processing governance as the core and electric grid topology computing function, it realizes the whole process management of electric distribution grid dispatching and management, and provides production management personnel with distribution network digital simulation platform and auxiliary decision-making function.

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Classification and Grading for power Sensitive Data Based on Hybrid Data Distribution Learning

Classification and Grading for Power Sensitive Data Based on...

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IEEE Joint International Information technology and Artificial Intelligence Conference (ITAIC)

作者： Xiuli Huang Yujia Zhai Congcong Shi Yiwen Jiang Shijun Zhang State Grid Laboratory of Power cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co. LTD. Nanjing China Big Data Center of State Grid Corporation of China Beijing China State Grid Jibei Electric Power Co. LTD. Beijing China

power industry data contain various sensitive information, and the leakage of such data will cause critical damage to both the company and the country. However, the unbalanced and various data types make the traditional data classification and grading method fail to achieve enough performance. To better classify and grade the power sensitive data, we propose a Hybrid Data Distribution Learning Network, which simultaneously learns the distribution of multiple data types, including Character-based, Text-based, and Numerical data. Furthermore, we use an optimized nonlinear Support Vector Machine model to detect the grade of power data based on the learned data distribution. The experimental results show that our method outperforms other baselines and achieves a precision of 0.994, a recall of 0.985, and an F 1 of 0.989. The visualization results also show that our method effectively learns the distribution of multiple types of power data.

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Online Network traffic anomaly detection method combining OS-ELM and SADE

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IEEE Access 2023年 1-1页

作者： Dai, Shiyong Zhao, Yufei Huang, Jinhua Wang, Xuxian Zhao, Guifei Zhang, Lei Xie, Ming Guo, Ziyue China Southern Power Grid Key Laboratory of Power Grid Automation Laboratory Guangzhou Guangdong China State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co.Ltd Beijing China Beijing Institute of Electronic Information Technology Beijing China Power Research Institute of State Grid Gansu Electric Power Company Gansu China Guangxi Power Grid Co. Ltd Nanning Guangxi China Qingyuan Power Supply Bureau of Guangdong Power Grid Co. Ltd Qingyuan Guangdong China

Network traffic anomaly detection methods can detect traffic that is significantly different from normal traffic by analyzing network traffic, and are seen as an effective means to detect unknown new attacks because they do not rely on static feature codes. However, most of the current network traffic anomaly detection methods have low accuracy and high false alarm rate. In this paper, an OS-ELM (Online Sequential Extreme Learning Machine) network traffic anomaly detection method is proposed. First, we use SMOTE to balance the data samples to improve the classification detection performance. In order to reduce the high dimensionality between data features and eliminate the redundancy, the Stacked Denoising Autoencoder (SDAE) network is adopt to reduce the dimension of the feature vectors. Finally, we test the real network traffic and analyze the effect of model structure and external noise on the detection performance, and the experimental results verify the correctness of our scheme. Compared with other detection methods based on data reconstruction, the proposed method has higher detection accuracy and better detection performance. Author

关键词： Intrusion detection

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Research and Implementation of Operation monitoring and Resource Scheduling Methods Based on the Digital Twin Model of the power grid

Research and Implementation of Operation Monitoring and Reso...

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International Conference on Applied Machine Learning (ICAML)

作者： Junfeng Qiao Hai Yu Zhimin He Lianteng Shen Xiaodong Du State Grid Laboratory of Power cyber-Security Protection & Monitoring Technology State Grid Smart Grid Research Institute Co. LTD Nanjing China China Electric Power Research Institute Co. LTD Beijing China State Grid Hebei Electric Power Co. LTD Shijiazhuang China

The digital twin model of the power grid has been widely applied in fields such as power equipment status acquisition and power equipment fault analysis. The use of digital twin models to virtual equipment status parameters provides strong model support for the acquisition and display of power equipment status. The digital twin model describes the physical entity in the power grid as digital indicators, and superimposes effective business analysis logic, making it widely provide digital services for data using departments. Similarly, the virtualization and visualization of device status parameters can also provide technical support for monitoring power operation status and scheduling power resources. This article proposes an operation monitoring and resource scheduling method based on the digital twin model of the power grid, which abstracts the status data of power grid equipment into key indicators of the digital twin model. Then, high-performance data collection and transmission technology is used to push the status data of power grid equipment and power grid operation status information to the power management department in a way that is easy to observe and edit. By adopting this approach, power managers do not need to conduct on-site surveys to monitor the operating status of electricity, and then develop resource scheduling plans to schedule electricity resources and achieve secondary distribution, providing better power supply services for electricity customers.

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Deep Learning-Based Control Model for Electronic Evidence Access Rights

Deep Learning-Based Control Model for Electronic Evidence Ac...

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Information Science, Parallel and Distributed Systems (ISPDS), International Conference on

作者： Peng Chen Yongliang Li Xianzhou Gao State Grid Ningxia Electric Power Co.Ltd. Ningxia China State Grid Ningxia Electric Power Co.Ltd. Electric Power Research Institute Ningxia China State Grid Smart Grid Research Institute Co. Ltd. Nanjing China State Grid Laboratory of Power cyber-Security Protection and Monitoring Technology Nanjing China

With the rapid development of digitalization and intelligence in smart grids, how to identify untrusted users in the electric power system and implement user access control based on electronic evidence has become an important research topic. A credible assessment needs to be made on the subject users accessing electronic evidence. In this paper, we propose a deep learning-based user multi-dimensional attribute feature extraction access control model, which considers multi-dimensional attribute features such as user identity, behaviour, and device information, and determines whether the user is a legitimate access user by using CNN-Transformer deep learning framework to extract user features and determine their trust value. Through simulation experiments, it is proved that the access permission control model proposed in this paper has high accuracy and reliability and can meet the access requirements of different electronic evidence users.

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Research on Digital Twin Modeling technology for Distribution Networks Integrating Multiple Digital Resources

Research on Digital Twin Modeling Technology for Distributio...

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Intelligent Computing, Communication & Convergence (ICI3C), International Conference on

作者： Junfeng Qiao Aihua Zhou Lin Peng Hang Su Zhonghao Qian Min Xu Sen Pan Pei Yang State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co. LTD Nanjing China State Grid Jiangsu Electric Power Co. LTD Xuzhou Power Supply Company Xuzhou China State Grid Jiangsu Electric Power Co. LTD Nantong Power Supply Company Nantong China

ISBN: (数字)9798350343922

ISBN: (纸本)9798350343939

The sources of digital resources in the distribution network mainly include three aspects: equipment ontology, production operation, and management. The data is described in various forms and stored in various types of systems and data platforms. However, with the continuous development and upgrading of distribution network business, the fusion of diverse data resources to drive decision-making has become a current development trend. Therefore, how to integrate and apply diverse digital resources is the key to the current development of distribution networks. In response to this issue, the article proposes a digital twin modeling technique for distribution networks that integrates multiple digital resources. Firstly, build a cross platform data mapping system based on the production and operation data management rules of the distribution network, and then use data access and integration technology to integrate diverse data resources into a unified data platform. Utilize the unified data model of the power grid to align multiple data resources and map the aligned data resources to the digital twin model. Simultaneously defining data synchronization update interfaces to ensure real-time and accurate data in the digital twin model. Finally, a typical scenario application of the distribution network based on the digital twin model is carried out to verify the effectiveness and practicality of the model.

关键词： Location awareness Accuracy Voltage fluctuations power quality Distribution networks Production Data models Real-time systems Digital twins Synchronization

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cyber-Attack Detection Approach In Photovoltaic Microgrids Using LSTM-based Autoencoders

Cyber-Attack Detection Approach In Photovoltaic Microgrids U...

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power Engineering (ICPE), International Conference on

作者： Quan Liu Shiang Guo Shenjian Qiu Jiaxuan Fei Bo Zhang Xiang Yu Kaikai Pan Yangzhou Power Supply Branch State Grid Jiangsu Electric Power Co. Ltd. Yangzhou China Zhejiang University School of Electrical Engineering Hangzhou China State Grid Laboratory of Power cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co. LTD. Nanjing China

Photovoltaic (PV) microgrids have been exposed to serious cyber threats for the integration of information and communication technologies (ICT) and renewable energy. In this article, a data-driven detection approach using an LSTM-based autoencoder has been developed against cyberattacks including fake data injection attacks (FDIA) and denial of service (DoS) attacks in PV microgrids. The proposed detection algorithm applies the unsupervised learning method that uses an LSTM neural network to capture the temporal feature and autoencoder to get reconstructed residuals of given input sequence data of measurements. By comparing residuals with the given threshold, the decision can be made on whether the system is attacked. The proposed algorithm has been evaluated for its performance in the PV microgrid model considering PV generation, energy storage system, and grid-tied function under various cyberattacks.

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A Web Attack Detection Method Based on DistilBERT and Feature Fusion for power Micro-Application Server

A Web Attack Detection Method Based on DistilBERT and Featur...

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Advanced Electronics, Electrical and Green Energy (AEEGE), International Conference on

作者： Li Nige Chen Lu Zhao Lei Teng Zhenning Wang Zhiqiang Shao Yiyang Gui Xiaolin State Grid Smart Grid Research Institute Co. Ltd. State Grid Laboratory of Power cyber-Security Protection and Monitoring Technology Nanjing China Shaanxi Province Key Laboratory of Computer Network Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an China

The widespread use of power micro-applications has significantly expanded the attack surface of their server side, thus increasing the risk of vulnerability attacks such as SQL injection, XSS, and CRLF injection. To ensure the security of power systems, these attacks must be detected precisely and timely. Therefore, this paper proposes a server-side web attack detection method based on DistilBERT and feature fusion for HTTP and HTTPS requests. The method treats HTTP and HTTPS requests as text data, specifically, extracts deep semantic features using DistilBERT, and fuses them with well-designed empirical features to comprehensively characterize and classify the request. Consequently, anomalous requests can be detected. The experimental results show that the accuracy, precision, recall, and F1 score of the method on HTTP CSIC 2010 and FWAF datasets are close to or higher than 99%. Compared with other methods as far as we known, this method has better performance and efficiency.

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Design of an Automated security protection System for power System

Design of an Automated Security Protection System for Power ...

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Computer Engineering and Intelligent Control (ICCEIC) International Conference on

作者： Jun Xia Jun Li Xinfan Jiang Bo Zhou Zesheng Xi State Grid Hunan Electric Power Co. Ltd Changsha China State Grid Hunan Electric Power Co. Ltd. Hunan Energy Big Data Intelligence Ltd Changsha China State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co. Ltd Nanjing China

In situations involving power system applications, a design plan for a new kind of automated security protection system has been put out. The self-repair vulnerability module, attack detection module, security policy configuration automation module, and front-end monitoring interface have all been designed for this article. This prototype system can secure device privacy and system stability while generating security policies automatically, responding to attacks quickly, and operating and maintaining expenses. This mostly entails the creation of self-repair and mining techniques for terminal vulnerabilities as well as detection techniques for malicious terminal-based assaults. In order to automatically create security protection device linkage methods, the automation module is configured to accept vulnerability and attack alert information. Each module opens its own database or offers interface forms for interactive operation and administration, while the front-end monitoring interface is created concurrently.

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