Reinforcement Learning-Based Countermeasures Against Attacking UAV Swarms

Abstract

The development of autonomous swarm behavior for UAV swarms has increased significantly in recent years. Many applications such as collective transport, exploration of unknown territory or target search and delivery benefit from the flexibility, scalability and robustness of the swarm approach. Besides new application possibilities, these characteristics might also be used for malicious or dangerous purposes like autonomous target-oriented attacks. To date, research is lacking intelligent countermeasures to intervene in attacking UAV swarms. Typical defense mechanisms employ attack-defense confrontation which increases the risk of collateral damage as drones might fall from the sky. Rather than creating a confrontation, we focus on developing countermeasures to intelligently mislead or delay attacks on a target. Therefore, we explore two multi-agent deep reinforcement learning strategies for defender UAVs to intervene in target-oriented attacks of intelligent UAV swarms. Both strategies are based on the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm and aim at preventing or at least delaying attacks. Via simulations we model and evaluate the performance of both methods and compare it to a baseline approach.