1. Optimization mechanism of attack and defense strategy in honeypot game with evidence for deception.
- Author
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SONG Lihua, JIANG Yangyang, XING Changyou, and ZHANG Guomin
- Abstract
Using game theory to optimize honeypot behavior is an important method in improving defender's trapping ability. Existing work tends to use over simplified action spaces and consider isolated game stages. A game model named HoneyED with expanded action spaces and covering comprehensively the whole interaction process between a honeypot and its adversary was proposed. The model was focused on the change in the attacker's beliefs about its opponent's real identity. A pure-strategy-equilibrium involving belief was established for the model by theoretical analysis. Then, based on the idea of deep counterfactual regret minimization (Deep-CFR), an optimization algorithm was designed to find an approximate hybrid-strategy-equilibrium. Agents for both sides following hybrid strategies from the approximate equilibrium were obtained. Theoretical and experimental results show that the attacker should quit the game when its belief reaches a certain threshold for maximizing its payoff. But the defender's strategy is able to maximize the honeypot's profit by reducing the attacker's belief to extend its stay as long as possible and by selecting the most suitable response to attackers with different deception recognition abilities. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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