  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, and
UAV cost). Specifically, we first develop a honeypot game between UAVs under
both partial and complete information asymmetry scenarios. We then devise a
contract-theoretic method to solve the optimal VDD-reward contract design
problem with partial information asymmetry, while ensuring truthfulness,
fairness, and computational efficiency. Furthermore, under complete information
asymmetry, we devise a reinforcement learning based distributed method to
dynamically design optimal contracts for distinct types of UAVs in the
fast-changing network. Experimental simulations show that the proposed scheme
can motivate UAV's collaboration in VDD sharing and enhance defensive
effectiveness, compared with existing solutions.

Computer Science and Game Theory