Abstract The Configurable Markov Decision Process framework includes two entities: a
Reinforcement Learning agent and a configurator that can modify some environmental …

Protecting critical infrastructure from intentional damage requires foreseeing the strategies of
possible attackers. We formulate this problem as a Stackelberg Security Game. A defender …

The rapid increase in cybercrime, causing a reported annual economic loss of $600 billion
(Lewis 2018), has prompted a critical need for effective cyber defense. Strategic criminals …

Recent works have growingly shown that Cyber deception can effectively impede the
reconnaissance efforts of intelligent cyber attackers. Recently proposed models to optimize …

Real-world security scenarios sometimes involve multiple defenders: security agencies of
two or more countries might patrol the same border areas, and domestic security agencies …

The Stackelberg game model, where a leader commits to a strategy and the follower best
responds, has found widespread application, particularly to security problems. In the security …

In this work, we investigate a security game between an attacker and a defender. We
formulate a zero-sum game in which the payoff matrix has a special structure which results …

Reinforcement Learning (RL) algorithms have been successfully applied to real world
situations like illegal smuggling, poaching, deforestation, climate change, airport security …

In a stochastic Stackelberg asymmetric security game, the strong Stackelberg equilibrium
(SSE) strategy is a popular option for the defender to get the highest utility against an …

Protecting critical infrastructure from intentional damage requires foreseeing the strategies of
possible attackers. The problem faced by the defender of such infrastructure can be …