Q-learning based strategy analysis of cyber-physical systems considering unequal cost

This paper proposes a cyber security strategy for cyber-physical systems (CPS) based on Q-learning under unequal cost to obtain a more efficient and low-cost cyber security defense strategy with misclassification interference. The system loss caused by strategy selection errors in the cyber security of CPS is often considered equal. However, sometimes the cost associated with different errors in strategy selection may not always be the same due to the severity of the consequences of misclassification. Therefore, unequal costs referring to the fact that different strategy selection errors may result in different levels of system losses can significantly affect the overall performance of the strategy selection process. By introducing a weight parameter that adjusts the unequal cost associated with different types of misclassification errors, a modified Q-learning algorithm is proposed to develop a defense strategy that minimizes system loss in CPS with misclassification interference, and the objective of the algorithm is shifted towards minimizing the overall cost. Finally, simulations are conducted to compare the proposed approach with the standard Q-learning based cyber security strategy method, which assumes equal costs for all types of misclassification errors. The results demonstrate the effectiveness and feasibility of the proposed research.