Learning Near-Optimal Intrusion Responses Against Dynamic Attackers

Lazy Defenders Are Almost Optimal against Diligent Attackers

Most work building on the Stackelberg security games model assumes that the attacker can perfectly observe the defender’s randomized assignment of resources to targets. This assumption has been challenged by recent papers, which designed tailor-made algorithms that compute optimal defender strategies for security games with limited surveillance. We analytically demonstrate that in zero-sum secu...

Defending against Roadside Attackers

Communication between vehicles is a very promising technology to reduce fatalities and injuries in road traffic. Vehicles spontaneously form communication networks where they exchange messages to warn surrounding vehicles. Theoretically, this system is open to any communication node that is equipped with the required communication technology. This openness demands appropriate security mechanism...

Defending against multiple different attackers

One defender defends, and multiple heterogeneous attackers attack, an asset. Three scenarios are considered: the agents move simultaneously; the defender moves first; or the attackers move first. We show how the agents’ unit costs of defense and attack, their asset evaluations, and the number of attackers influence their investments, profits, and withdrawal decisions. Withdrawal does not occur ...

Near-optimal Regret Bounds for Reinforcement Learning Near-optimal Regret Bounds for Reinforcement Learning

This technical report is an extended version of [1]. For undiscounted reinforcement learning in Markov decision processes (MDPs) we consider the total regret of a learning algorithm with respect to an optimal policy. In order to describe the transition structure of an MDP we propose a new parameter: An MDP has diameter D if for any pair of states s, s there is a policy which moves from s to s i...

Protecting Complex Infrastructures Against Strategic Attackers