An Optimal Metric-Aware Response Selection Strategy for Intrusion Response Systems

Herold, Nadine; Wachs, Matthias; Posselt, Stephan-Alexander; Carle, Georg

doi:10.1007/978-3-319-51966-1_5

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…However, the method has high time cost. Herold et al defined the response selection approach based on user-defined cost metrics to counteract security incidents in complex network environments [ 25 ]. However, this method does not meet the ICS security requirements well due to the ignorance of the balance between security risks and protection costs.…”

Section: Related Workmentioning

confidence: 99%

Optimal Security Protection Strategy Selection Model Based on Q-Learning Particle Swarm Optimization

Gao

Yang

et al. 2022

Entropy

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With the rapid development of Industrial Internet of Things technology, the industrial control system (ICS) faces more and more security threats, which may lead to serious risks and extensive damage. Naturally, it is particularly important to construct efficient, robust, and low-cost protection strategies for ICS. However, how to construct an objective function of optimal security protection strategy considering both the security risk and protection cost, and to find the optimal solution, are all significant challenges. In this paper, we propose an optimal security protection strategy selection model and develop an optimization framework based on Q-Learning particle swarm optimization (QLPSO). The model performs security risk assessment of ICS by introducing the protection strategy into the Bayesian attack graph. The QLPSO adopts the Q-Learning to improve the local optimum, insufficient diversity, and low precision of the PSO algorithm. Simulations are performed on a water distribution ICS, and the results verify the validity and feasibility of our proposed model and the QLPSO algorithm.

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Section: Related Workmentioning

confidence: 99%