Cyber Deception for Mission Surveillance via Hypergame-Theoretic Deep Reinforcement Learning

TL;DR

This paper introduces HT-DRL, a hypergame-theoretic deep reinforcement learning method for cyber deception in UAV missions, effectively diverting cyberattacks and improving mission success while reducing energy use.

Contribution

It proposes a novel HT-DRL framework that integrates hypergame theory with deep reinforcement learning for strategic cyber deception in UAV systems.

Findings

HT-DRL outperforms non-HD methods by up to two times in mission performance.

The approach achieves low energy consumption while maintaining effective deception.

Performance analysis under various attack strategies demonstrates robustness.

Abstract

Unmanned Aerial Vehicles (UAVs) are valuable for mission-critical systems like surveillance, rescue, or delivery. Not surprisingly, such systems attract cyberattacks, including Denial-of-Service (DoS) attacks to overwhelm the resources of mission drones (MDs). How can we defend UAV mission systems against DoS attacks? We adopt cyber deception as a defense strategy, in which honey drones (HDs) are proposed to bait and divert attacks. The attack and deceptive defense hinge upon radio signal strength: The attacker selects victim MDs based on their signals, and HDs attract the attacker from afar by emitting stronger signals, despite this reducing battery life. We formulate an optimization problem for the attacker and defender to identify their respective strategies for maximizing mission performance while minimizing energy consumption. To address this problem, we propose a novel approach, called HT-DRL. HT-DRL identifies optimal solutions without a long learning convergence time by taking the solutions of hypergame theory into the neural network of deep reinforcement learning. This achieves a systematic way to intelligently deceive attackers. We analyze the performance of diverse defense mechanisms under different attack strategies. Further, the HT-DRL-based HD approach outperforms existing non-HD counterparts up to two times better in mission performance while incurring low energy consumption.