Generation of Cyber-security Reinforcement Strategies for Smart Grid Based on the Attribute-based Attack Graph

  • Bo Zhang Nanjing University of Science and Technology
  • Qianmu Li Nanjing University of Science and Technology
  • Yiying Zhang China University of Mining & Technology
  • Xuan Liu Illinois Institute of Technology
  • Zhen Ni Nanjing University of Science and Technology


A smart grid is a kind of energy cyber-physical system (ECPS) with the interdependency of information and physicality.A cyber-attack gravely threatens the safe and stable operation of a physical power grid. Cyber-security reinforcement of smartgrid has become a research issue. However, the information network scale of a smart grid is massive, and the generation ofsecurity reinforcement strategies has become a problem. Therefore, a generation method of security reinforcement strategiesbased on an attribute-based attack graph was proposed in this study. The method defined a smart grid based on premise andconsequence attributes to form an attribute-based attack graph. With this graph, the method for the generation of securityreinforcement strategies was transferred to the minimum dominating set of the attribute-based attack graph and solved torealize space reduction in the security reinforcement strategies. An algorithm for the generation of security reinforcementstrategies was designed based on the greedy algorithm, and strategies for large-scale cyber security reinforcement of thesmart grid were determined to eliminate the complexity and difficulty of this problem effectively. Through a simulation analysisof a large-scale node network, the efficiency of the generation method of reinforcement strategies based on the attributebasedattack graph and minimum dominating set was verified. Results show that the proposed method can be used forsecurity reinforcement of large-scale complicated networks of a smart grid.


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How to Cite
ZHANG, Bo et al. Generation of Cyber-security Reinforcement Strategies for Smart Grid Based on the Attribute-based Attack Graph. Journal of Power Technologies, [S.l.], v. 96, n. 3, p. 170--177, oct. 2016. ISSN 2083-4195. Available at: <>. Date accessed: 27 sep. 2021.
Energy Engineering and Technology

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