Adversarial Deep Reinforcement Learning to Mitigate Sensor and Communication Attacks for Secure Swarm Robotics

Abstract

The success of swarm robotics depends on the precision and reliability of the sensors they use, as well as the accuracy of their communication links and technologies. However, these components are vulnerable to security and safety threats. Adversaries could potentially hijack control of a swarm by tampering with the data these sensors and communication systems relay. This is concerning during the state estimation process that monitors tshe dynamics of the swarm's collective behavior, necessitating swift and effective countermeasures. In this scenario, we introduce an adversarial deep reinforcement learning algorithm designed to strengthen the resilience of swarm robot dynamics against such malicious interventions. The adversary's strategy involves injecting corrupted data into the swarm's sensor readings, aiming to disrupt the optimal spacing that ensures safe and efficient operation within the swarm. The attacker jeopardizes not only the physical safety of the robots but also their ability to perform tasks cohesively, potentially leading to operational failures or reduced efficiency by doing so. Conversely, the swarm seeks to defend against these attacks by dynamically adjusting its formation to maintain the necessary inter-robot distances, thus minimizing the impact of any data manipulation. This adversarial interaction between the swarm and potential attackers is analyzed through a game-theoretical lens, incorporating advanced deep learning tools for enhanced strategic insight. To predict and counteract the effects of such data tampering, each robot within the swarm employs Long-Short-Term-Memory (LSTM) and Generative Adversarial Network (GAN) models. These models help predict the potential variations in spacing caused by the swarm's reactions to external interventions and feed this information into the algorithm. The goal of the system is to minimize these distance variations, ensuring the swarm's robust operation despite adversarial attempts to disrupt it. Meanwhile, attackers leveraging the algorithm aim to maximize the disruption to the swarm's spatial dynamics, creating a continuous strategic policy that underpins the importance of advanced, adaptive defensive mechanisms in of swarm robotics.