A Learning-based Honeypot Game for Collaborative Defense in UAV Networks
Yuntao Wang, Zhou Su, Abderrahim Benslimane, Qichao Xu, Minghui Dai,, and Ruidong Li

TL;DR
This paper introduces a game-theoretic and reinforcement learning-based framework to incentivize UAVs to collaborate in sharing defense data via honeypots, improving cybersecurity in UAV networks.
Contribution
It presents a novel incentive mechanism combining contract theory and reinforcement learning for collaborative defense in UAV networks with private information.
Findings
Enhances UAV collaboration in honeypot defense.
Improves attack detection through incentivized data sharing.
Outperforms existing solutions in simulation results.
Abstract
The proliferation of unmanned aerial vehicles (UAVs) opens up new opportunities for on-demand service provisioning anywhere and anytime, but it also exposes UAVs to various cyber threats. Low/medium-interaction honeypot is regarded as a promising lightweight defense to actively protect mobile Internet of things, especially UAV networks. Existing works primarily focused on honeypot design and attack pattern recognition, the incentive issue for motivating UAVs' participation (e.g., sharing trapped attack data in honeypots) to collaboratively resist distributed and sophisticated attacks is still under-explored. This paper proposes a novel game-based collaborative defense approach to address optimal, fair, and feasible incentive mechanism design, in the presence of network dynamics and UAVs' multi-dimensional private information (e.g., valid defense data (VDD) volume, communication delay,…
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Taxonomy
TopicsUAV Applications and Optimization · Security in Wireless Sensor Networks · Opportunistic and Delay-Tolerant Networks
