A Data Intrusion Tolerance Model Based on an Improved Evolutionary Game Theory for the Energy Internet

Malicious attacks against data are unavoidable in the interconnected, open and shared Energy Internet (EI), Intrusion tolerant techniques are critical to the data security of EI. Existing intrusion tolerant techniques suffered from problems such as low adaptability, policy lag, and difficulty in determining the degree of tolerance. To address these issues, we propose a novel adaptive intrusion tolerance model based on game theory that enjoys two-fold ideas: 1) it constructs an improved replica of the intrusion tolerance model of the dynamic equation evolution game to induce incentive weights; and 2) it combines a tournament competition model with incentive weights to obtain optimal strategies for each stage of the game process. Extensive experiments are conducted in the IEEE 39-bus system, whose results demonstrate the feasibility of the incentive weights, confirm the proposed strategy strengthens the system's ability to tolerate aggression, and improves the dynamic adaptability and response efficiency of the aggression-tolerant system in the case of limited resources.