A zero-loss equivalence class of cost estimators makes decision-focused learning fail in shortest-path network interdiction games, and training on interdicted scenarios (A-DFL) collapses this class and restores performance.
Learning Attacker's Bounded Rationality Model in Security Games
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
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.
fields
cs.GT 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Decision-Focused Learning in Network Interdiction Games
A zero-loss equivalence class of cost estimators makes decision-focused learning fail in shortest-path network interdiction games, and training on interdicted scenarios (A-DFL) collapses this class and restores performance.