Path analysis attack prediction method for electric power CPS

被引：0
|
作者：
Xia Z. [1 ,2 ,3 ]
Li W. [1 ,2 ]
Jiang L. [1 ,2 ]
Xu M. [3 ]
机构：
[1] Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha
[2] School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha
[3] School of Computer, National University of Defense Technology, Changsha
来源：
Qinghua Daxue Xuebao/Journal of Tsinghua University | 2018年 / 58卷 / 02期
关键词：
Attack probability graph; Cross-origin attack probability; Cross-origin mean time; Electric CPS security; Path prediction;
D O I：
10.16511/j.cnki.qhdxxb.2018.26.012
中图分类号：
O212 [数理统计];
学科分类号：
摘要：
The electric power industry needs to defend against multi-step cross-domain attacks seeking to damage electric power CPS. This paper presents path analysisa electric power CPS attack prediction method that defines a common attack graph based on a probability attack graph model. The Cross-origin attack probability and the cross-origin mean time to compromise are used to quantify the exploit difficulty and the attacker proficiency for offensive and defensive actions to protect the power infrastructure. When attacks are detected in real time, the improved Dijkstra algorithm will enumerate possible follow-up attack paths. The two quantitative indicators are combined to predict the greatest threat attack path. Simulations show that this method can more effectively predict the attack path as a good defensive strategy for electric power CPS security management. © 2018, Tsinghua University Press. All right reserved.
引用
收藏
页码：157 / 163
页数：6
相关论文
共 17 条
  • [1] Liu J.N., Wen J., Survey on smart grid security, Netinfo Security, 5, pp. 78-84, (2016)
  • [2] Sun C.C., Liu C.C., Xie J., Cyber-physical system security of a power grid: State-of-the-art, Electronics, 5, 3, (2016)
  • [3] Langner R., Stuxnet: Dissecting a cyberwarfare weapon, IEEE Security & Privacy, 9, 3, pp. 49-51, (2011)
  • [4] Khalid H.M., Peng J.C.H., A Bayesian algorithm to enhance the resilience of WAMS applications against cyber attacks, IEEE Transactions on Smart Grid, 7, 4, pp. 2026-2037, (2016)
  • [5] Chen X.J., Fang B.X., Tan Q.F., Et al., Inferring attack intent of malicious insider based on probabilistic attack graph model, Chinese Journal of Computers, 37, 1, pp. 62-72, (2014)
  • [6] Chen T.M., Sanchez-Aamoutse J.C., Buford J., Petri net modeling of cyber-physical attacks on smart grid, IEEE Transactions on Smart Grid, 2, 4, pp. 741-749, (2011)
  • [7] Zhang H.W., Yu D.K., Han J.H., Et al., Defense policies selection method based on attack-defense signaling game model, Journal on Communications, 37, 5, pp. 51-61, (2016)
  • [8] Idika N., Bhargava B., Extending attack graph-based security metrics and aggregating their application, IEEE Transactions on Dependable and Secure Computing, 9, 1, pp. 75-85, (2012)
  • [9] Jajodia S., Noel S., Kalapa P., Et al., Cauldron mission-centric cyber situational awareness with defense in depth, Proceeding of Military Communications Conference, pp. 1339-1344, (2011)
  • [10] Wang L.Y., Islam T., Long T., Et al., An attack graph-based probabilistic security metric, Data and Applications Security XXII, pp. 283-296, (2008)
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