Hindering Search and Rescue Missions with Selective Wireless Jamming Attacks

In search and rescue missions, agents are tasked with locating targets in dynamic and potentially adversarial environments. These agents commonly operate under energy constraints and rely on wireless communication susceptible to intentional interference by attackers. Thus, it is important to study the impact of attackers deploying stealthy wireless jamming attacks to disrupt such missions. To this end, this paper introduces a Markov Decision Process (MDP) model designed for the attacker, determining when to jam communication based on the evolving state of the mission. A reinforcement learning-based approach is developed to identify intelligent and selective jamming policies, particularly in scenarios where the identification of optimal strategies is computationally prohibitive due to the expansive state space. These policies judiciously optimize the tradeoff between the cost of the attack and the expected damage inflicted. We evaluate the performance of these policies in environments of varying sizes. We demonstrate that our approach achieves optimal performance in tractable setups and outperforms alternative heuristics as the complexity of the state space increases.