  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.
