Research and Application of Key Technologies of Network Security Situation Awareness for Smart Grid Power Control Systems

doi:10.16183/j.cnki.jsjtu.2021.S2.017

Abstract

Abstract:

The network security situational awareness (NSSA) technology, which can perceive the potential network security risks globally and dynamically, is receiving more and more attention.With the help of machine learning, artificial intelligence, big data, and the other technologies, the network security situation awareness solution of power control system can learn from the process of the long-term and massive network security situation data, gain insight into the internal logical relationship implied in the data, and realize the abnormal behavior identification, intrusion intention understanding, and impact assessment of various activities in the power business network. First, the basic concept and the logical block diagram of NSSA are introduced. Then, the current situation and the risk of network security of power control system are summarized. Next, aimed at these risks and deficiencies, the key technologies involved in the network security situation awareness platform from the perspective of practice are expounded, which include the multidimensional security event correlation analysis model,the abnormal traffic and abnormal behavior detection method based on “baseline learning”,the attack chain recognition model based on attack scenario,and the power remote control security technology based on “address self verification”. Finally, the situation awareness solution and its application in power monitoring systems are stated and prospected.

Key words: network security, situation awareness, situation cognition, situation prediction, power control system

CLC Number:

TP393.08

ZHANG Liang, QU Gang, LI Huixing, JIN Haochun. Research and Application of Key Technologies of Network Security Situation Awareness for Smart Grid Power Control Systems[J]. Journal of Shanghai Jiao Tong University, 2021, 55(S2): 103-109.

Figures/Tables 4

References 7

[1]	柯宗贵, 杨育斌, 麦思文. 基于大数据的网络安全态势感知解决方案[J]. 信息技术与标准化, 2019(9): 21-22.
	KE Zonggui, YANG Yubin, MAI Siwen. Network security situational awareness solution based on big data[J]. Information Technology & Standardization, 2019(9): 21-22.
[2]	龚正虎, 卓莹. 网络态势感知研究[J]. 软件学报, 2010, 21(7): 1605-1619.
	GONG Zhenghu, ZHUO Ying. Research on cyberspace situational awareness[J]. Journal of Software, 2010, 21(7): 1605-1619.
[3]	BRADSHAW J M, CARVALHO M, BUNCH L, et al. Sol: An agent-based framework for cyber situation awareness[J]. KI-Künstliche Intelligenz, 2012, 26(2): 127-140. doi: 10.1007/s13218-012-0179-2 URL
[4]	LIU N, WANG D G, HUANG X M, et al. Research on network security situation awareness technology based on artificial immunity system[C]// 2009 International Forum on Information Technology and Applications. Chengdu, China: IEEE, 2009: 472-475.
[5]	ZOU F T, LI L S, WU Y, et al. Detecting domain-flux malware using DNS failure traffic[J]. International Journal of Software Engineering and Knowledge Engineering, 2018, 28(2): 151-173. doi: 10.1142/S0218194018400016 URL
[6]	ZOU F T, ZHANG S Y, LI L S, et al. Detecting malware based on expired command-and-control traffic[J]. International Journal of Distributed Sensor Networks, 2017, 13(7): 155014771772079.
[7]	ZOU F T, ZHANG S Y, RAO W X, et al. Detecting malware based on DNS graph mining[J]. International Journal of Distributed Sensor Networks, 2015, 2015:1-12.

[1]	JIANG Yilin^{1, 2∗}(蒋伊琳), LI Xiang^{1, 2} (李向), ZHANG Haoping³ (张昊平). Emitter Beam State Sensing Based on Convolutional Neural Network and Received Signal Strength [J]. J Shanghai Jiaotong Univ Sci, 2024, 29(6): 1017-1022.
[2]	SHI Yiwei1 (史一炜), LIU Zeyu1 (刘泽宇), FENG Donghan1∗ (冯冬涵), ZHOU Yun1∗ (周云), ZHANG Kaiyu2 (张开宇), LI Hengjie3 (李恒杰). Electric vehicle charging situation awareness for charging station ultra-short-term load forecast [J]. J Shanghai Jiaotong Univ Sci, 2023, 28(1): 28-38.
[3]	LI Yin, GUO Jianbin, ZHANG Shaowei, WANG Kunyun, CI Huipeng. Safety Modeling Method of Human-Machine System Based-on JACK and Situation Awareness [J]. Air & Space Defense, 2023, 6(1): 29-37.
[4]	WU Nan, CHENG Zhetao, DU Liang, SHEN Yingping. Network Security Inference Method Based on Dempster-Shafer Theory [J]. Journal of Shanghai Jiao Tong University, 2021, 55(S2): 77-81.
[5]	ZHANG Ying，CHEN Zhi，LI Yiliang，JIN Yuan. Optimization Design for Reactor Power Control System of HPR1000 [J]. Journal of Shanghai Jiaotong University, 2019, 53(Sup.1): 55-60.
[6]	WEN Zhicheng1,2,CHEN Zhigang1,DENG Xiaoheng1,LIU Anfeng1. Network Security Situation Awareness Method Based on Multi-Source and Multi-Level Information Fusion [J]. Journal of Shanghai Jiaotong University, 2015, 49(08): 1144-1152.
[7]	HE Jun* (何俊), ZHENG Shi-hui (郑世慧). Intrusion Detection Model with Twin Support Vector Machines [J]. Journal of shanghai Jiaotong University (Science), 2014, 19(4): 448-454.
[8]	ZHANG Keli1,LI Zhongxian1,2,YANG Yixian1. A Trust Model for Identifying and Tracing Malicious Anonymous Feedback Providers [J]. Journal of Shanghai Jiaotong University, 2014, 48(07): 899-906.
[9]	ZHANG Lei,CAO Zhenfu . An AttributeBased Encryption Scheme Suitable for Distributed Network [J]. Journal of Shanghai Jiaotong University, 2010, 44(11): 1507-1512.
[10]	ZHANG Baowen1,LUO Zheng2,XUE Zhi1,YIN Ying1 . A Network Risk Assessment Model Based on Network Global Privilege Graph [J]. Journal of Shanghai Jiaotong University, 2010, 44(09): 1197-1200.