Learning Attacker's Bounded Rationality Model in Security Games
This work addresses security game scenarios where attackers are not perfectly rational, potentially improving defense strategies in domains like cybersecurity.
The paper tackles the problem of modeling attackers' bounded rationality in Stackelberg Security Games by proposing a neuroevolutionary method (NESG) that uses a strategy evaluation neural network (SENN) trained on historical data without prior knowledge of the opponent. Experimental results on 90 benchmark games show NESG outperforms state-of-the-art methods against not perfectly rational opponents, with superior computational scalability.
The paper proposes a novel neuroevolutionary method (NESG) for calculating leader's payoff in Stackelberg Security Games. The heart of NESG is strategy evaluation neural network (SENN). SENN is able to effectively evaluate leader's strategies against an opponent who may potentially not behave in a perfectly rational way due to certain cognitive biases or limitations. SENN is trained on historical data and does not require any direct prior knowledge regarding the follower's target preferences, payoff distribution or bounded rationality model. NESG was tested on a set of 90 benchmark games inspired by real-world cybersecurity scenario known as deep packet inspections. Experimental results show an advantage of applying NESG over the existing state-of-the-art methods when playing against not perfectly rational opponents. The method provides high quality solutions with superior computation time scalability. Due to generic and knowledge-free construction of NESG, the method may be applied to various real-life security scenarios.