The proliferation of unmanned aerial vehicles (UAVs) opens up new opportunities for on-demand service provisioning anywhere and anytime, but also exposes UAVs to a variety of cyber threats. Low/medium interaction honeypots offer a promising lightweight defense for actively protecting mobile Internet of things, particularly UAV networks. While previous research has primarily focused on honeypot system design and attack pattern recognition, the incentive issue for motivating UAV's participation (e.g., sharing trapped attack data in honeypots) to collaboratively resist distributed and sophisticated attacks remains unexplored. This paper proposes a novel game-theoretical collaborative defense approach to address optimal, fair, and feasible incentive design, in the presence of network dynamics and UAVs' multi-dimensional private information (e.g., valid defense data (VDD) volume, communication delay, and UAV cost). Specifically, we first develop a honeypot game between UAVs and the network operator under both partial and complete information asymmetry scenarios. The optimal VDD-reward contract design problem with partial information asymmetry is then solved using a contract-theoretic approach that ensures budget feasibility, truthfulness, fairness, and computational efficiency. In addition, under complete information asymmetry, we devise a distributed reinforcement learning algorithm to dynamically design optimal contracts for distinct types of UAVs in the time-varying UAV network. Extensive simulations demonstrate that the proposed scheme can motivate UAV's cooperation in VDD sharing and improve defensive effectiveness, compared with conventional schemes.

翻译：无人驾驶飞行器（UAV）的普及为随时随地按需提供服务开辟了新机遇，但也使无人机面临各种网络威胁。低/中等交互蜜罐为主动保护移动物联网（尤其是无人机网络）提供了一种有前景的轻量级防御手段。尽管先前的研究主要聚焦于蜜罐系统设计与攻击模式识别，但如何激励无人机参与协作（例如共享蜜罐中捕获的攻击数据）以共同抵御分布式与复杂化攻击的激励问题仍未得到充分探索。本文提出了一种新颖的博弈论协作防御方法，以应对网络动态性及无人机多维私有信息（如有效防御数据量、通信延迟和无人机成本）下的最优、公平且可行的激励设计。具体而言，我们首先在部分与完全信息不对称场景下构建了无人机与网络运营商之间的蜜罐博弈。针对部分信息不对称问题，采用契约理论方法解决了最优有效防御数据奖励契约设计问题，确保了预算可行性、真实性、公平性及计算效率。此外，在完全信息不对称条件下，我们设计了一种分布式强化学习算法，用于在时变无人机网络中动态地为不同类型无人机设计最优契约。大量仿真结果表明，与传统方案相比，所提方案能激励无人机在有效防御数据共享中的协作，并提升防御有效性。