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

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

引用

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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User Identification based on Behavior Feature Analysis

User Identification based on Behavior Feature Analysis

引用

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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Multi-source System Log Behavior Pattern Mining Method Based on FP-Growth 23

Multi-source System Log Behavior Pattern Mining Method Based...

引用

Proceedings of the 2023 International Conference on Communication Network and Machine Learning

作者： Daojuan Zhang Tianqi Wu Xiaoming Zhou Bo Hu Wenjie Zhang State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute co. Ltd China State Grid Liaoning Electric Power Supply Co. Ltd. China

ISBN: (纸本)9798400716683

By analyzing and mining system logs, it is possible to effectively discover behavioral characteristics and anomalies of network users or systems. Through association analysis, patterns and correlations between different log sources can be identified, thereby enabling the detection of various intrusion behaviors in computer networks. To address the challenge of handling massive and complex log data, this paper proposes a multi-source system log behavior pattern mining method based on FP-Growth. This method efficiently extracts frequent patterns and abnormal behaviors from large-scale system logs. Firstly, to ensure data quality and facilitate subsequent data mining processes, we perform necessary structuring and cleansing of raw logs and transform them into a format suitable for data analysis. Subsequently, given the variations in structure and content across different types of logs, we conduct distinct feature extraction for each type of log to retain essential information and generate transaction items. The data is then organized into datasets based on temporal partitions, forming a transaction database as input for the association rule mining algorithm. Finally, utilizing the FP-Growth association rule mining algorithm, this paper explores the relationships between entries from various types of single and multi-source logs. Based on metrics such as support, appropriate association rules are selected for behavior pattern mining. Experimental results demonstrate that the proposed method effectively uncovers typical behavior patterns.

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Attack Path Extraction based on VGAE and DBSCAN

Attack Path Extraction based on VGAE and DBSCAN

引用

cyber Science and technology Congress (cyberSciTech), IEEE

作者： Ziang Lu Lu Chen Yong Li Guangxin Guo Tengyan Wang State Grid Laboratory of Power cyber-Security Protection and Monitoring Technology China Electric Power Research Institute Co. Ltd Nanjing China State Grid Beijing Electric Power Research Institute Beijing China

ISBN: (数字)9798331532093

ISBN: (纸本)9798331532109

Mining and analyzing potential attack paths in networks based on alarm information can provide references for accurate and effective network defense measures. However, current research lacks thorough extraction and integration of deep features from alarm information. In this work, we propose APE-VD, an attack path extraction model based on VGAE and DBSCAN. It involves constructing network attack graphs based on host connectivity information and alarm types. Additionally, it creates temporal attack graphs by combining alarm data from different time slices with flow relationships. Using variational autoencoders, the temporal attack graphs are reduced in dimensionality to obtain alarm flow features. DBSCAN clustering is utilized to mine outlier nodes. To enhance clustering accuracy and robustness, second-order temporal risk features are extracted based on outlier nodes as prior information. Further, variational autoencoders and DBSCAN clustering are applied again. After filtering based on risk thresholds, forward and backward retrieval of nodes is conducted to derive attack paths. Simulation results on widely used log datasets demonstrate that APE-VD can efficiently uncover attack paths within limited computational complexity, aiding in reconstructing attack scenarios.

关键词： Accuracy Filtering Simulation Autoencoders Feature extraction Robustness Real-time systems Encoding Data mining Streams

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

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

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

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

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.

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Research and Application of Electronic Data Retrieval in Material Supply Chain Enhanced by Large Language Models and Knowledge Graph

Research and Application of Electronic Data Retrieval in Mat...

引用

Information technology, Communication Ecosystem and Management (ITCEM), International Conference on

作者： Xing Ge Yafei Liu Pei Yang Xin Sun Junfeng Qiao Luyao Qu Jingyi Qiu State Grid Jiangsu Electric Power CO. LTD. Material Branch Nanjing China State Grid Laboratory of Power cyber-Security Protection and Monitoring Technology & China Electric Power Research Institute Beijing China

ISBN: (数字)9798331542696

ISBN: (纸本)9798331542702

In response to the new goals of building green and modern smart supply chains, the electric power equipment supply chain is experiencing a shift toward digital intelligence and low-carbon, environmentally friendly development [1]. However, traditional search platforms based on relational databases face challenges in handling vast amounts of multimodal electronic data. These platforms often suffer from low search accuracy, limited cross-dimensional correlation analysis capabilities, and inefficiencies, making them inadequate for constructing comprehensive big data platforms that integrate and share information across the entire green, modern, smart supply chain. This paper introduces an innovative electronic data retrieval method designed to meet the data retrieval needs of material supply chains. By integrating large language models with knowledge graph technology, it proposes an electronic data retrieval system that leverages vector database technology for text embedding of multimodal data. Additionally, artificial intelligence is used to enable knowledge retrieval and augmented generation, significantly enhancing data retrieval capabilities within the specialized domain of material supply chains.

关键词： Green buildings Large language models Supply chains Knowledge graphs Relational databases Data retrieval Vectors power systems Information technology Faces

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An Intelligent Multi-Source Data Clustering and Analysis Method for AC/DC Hybrid power grid Planning with Increased Complexity 6

An Intelligent Multi-Source Data Clustering and Analysis Met...

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6th IEEE International Electrical and Energy Conference, CIEEC 2023

作者： Lan, Tu Zhou, Aihua Diao, Ruisheng Sun, Fangyuan Zeng, Ruiyuan Shen, Xiaofeng Huang, Chenhong Zheng, Zhen ZJU-UIUC Institute Zhejiang University 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 State Key Laboratory of Clean Energy Utilization Zhejiang Haining314400 China Qingpu Power Supply Company Stage Grid Shanghai Municipal Electric Power Company Shanghai201799 China

ISBN: (纸本)9798350346671

In order to better analyze the response characteristics of an AC/DC hybrid power grid with increased complexity following various types of disturbances, this paper proposes an intelligent method for clustering dynamic responses of multi-source data obtained from actual measurements or high-fidelity simulators. Base cases for AC/DC hybrid power grid planning students are created first, and a list of high-impact contingencies is searched using a graph algorithm. Then, the electromechanical-electromagnetic hybrid simulation method is adopted to create multi-source simulation data in terms of dynamic voltage, frequency, etc. An unsupervised learning algorithm and cluster analysis are then conducted to extract key features of dynamic response characteristics of the AC/DC hybrid power grid and create specific cluster labels. Advanced analysis, e.g., fast evaluation of risks from multi-source steady-state data, can then be enabled for further analysis. The proposed approach's effectiveness is verified on the actual AC/DC power grid model in East China. © 2023 IEEE.

关键词： Dynamic response

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Lightweight Blockchain-based Identity Authentication Scheme for power Terminal

Lightweight Blockchain-based Identity Authentication Scheme ...

引用

Electrical Engineering and Control Science (IC2ECS), International Conference on

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

In recent years, the construction of the power Internet of Things continues to advance, and the number of power terminal equipment is growing. However, the computing power and storage capacity of these terminal devices are limited, and the current blockchain-based identity authentication scheme has high requirements for computing and storage resources, limiting the participation of power terminal equipment with weak computing power and small storage space. Aiming at the problem that resource-limited power terminal equipment cannot join the blockchain, a lightweight consensus algorithm for power terminals is proposed, and a power terminal authentication scheme based on the lightweight consensus algorithm is designed to make up for the defect that resource-limited power terminal equipment does not support the use of blockchain. The security analysis shows that the scheme can effectively resist many kinds of network attacks and meet the security requirements of power terminal equipment. Performance analysis shows that the scheme has low computation cost and conforms to the principle of lightweight.

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

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

引用

Artificial Intelligence and Intelligent Manufacturing (AIIM), International Symposium on

作者： Jing Zeng Ji Lai Shuo Li Xian Li Ruxia Yang 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: (数字)9798331541729

ISBN: (纸本)9798331541736

In a modern microservice architecture, the largescale 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.

关键词： Adaptation models Attention mechanisms Microservice architectures Computer architecture Feature extraction Robustness Spatiotemporal phenomena Convolutional neural networks monitoring Anomaly detection

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Containerization of the security Application on Electric power IoT Terminal

Containerization of the Security Application on Electric Pow...

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

作者： Tengyan Wang Zhipeng Shao Nige Li Zheng Xu 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 Company Jinan China

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.

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