Protection Strategy Selection Model Based on Genetic Ant Colony Optimization Algorithm

Xinzhan Li, Yang Zhou (), Xin Li, Lijuan Xu and Dawei Zhao ()
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Xinzhan Li: Shandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
Yang Zhou: Shandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
Xin Li: Shandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
Lijuan Xu: Shandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
Dawei Zhao: Shandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China

Mathematics, 2022, vol. 10, issue 21, 1-24

Abstract: Industrial control systems (ICS) are facing an increasing number of sophisticated and damaging multi-step attacks. The complexity of multi-step attacks makes it difficult for security protection personnel to effectively determine the target attack path. In addition, most of the current protection models responding to multi-step attacks have not deeply studied the protection strategy selection method in the case of limited budget. Aiming at the above problems, we propose a protection strategy selection model based on the Genetic Ant Colony Optimization Algorithm. The model firstly evaluates the risk of ICS through the Bayesian attack graph; next, the target attack path is predicted from multiple angles through the maximum probability attack path and the maximum risk attack path; and finally, the Genetic Ant Colony Optimization Algorithm is used to select the most beneficial protection strategy set for the target attack path under limited budget. Compared with the Genetic Algorithm and Ant Colony Optimization Algorithm, the Genetic Ant Colony Optimization Algorithm proposed in this paper can handle the local optimal problem well. Simulation experiments verify the feasibility and effectiveness of our proposed model.

Keywords: Bayesian attack graph; genetic algorithm; ant colony optimization algorithm; reinforcement learning; protection strategy (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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