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

2026-03-21

AI Summary

This work addresses the vulnerability of unmanned aerial vehicle (UAV) mission systems to denial-of-service (DoS) attacks by proposing an active defense mechanism based on cyber deception. Specifically, high-signal-strength decoy UAVs (HDs) are deployed to lure attackers away from genuine UAVs performing critical missions such as surveillance, search-and-rescue, or payload delivery. The study innovatively integrates hypergame theory into a deep reinforcement learning framework, yielding the HT-DRL method for joint optimization of offensive and defensive strategies. Experimental results demonstrate that this approach not only significantly enhances defense efficacy and mission success rates—achieving up to twice the performance of non-decoy baselines—but also reduces system energy consumption while maintaining operational performance. This work represents the first effective application of hypergame-driven intelligent deception strategies in cooperative UAV defense.

Technology Category

• Multiagent Systems: Adversarial Agents
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Cyber Deception
• Denial-of-Service (DoS) attacks
• Unmanned Aerial Vehicles (UAVs)
• Mission Surveillance
• Cyber Defense

Innovation

• Cyber Deception
• Hypergame Theory
• Deep Reinforcement Learning
• Honey Drones
• UAV Security