An intelligent and adaptive security framework for UAV swarms: a cross-layer approach integrating highly reliable...

The proliferation of unmanned aerial vehicle (UAV) swarms in mission-critical applications for 6G and the Internet of Things (IoT) introduces significant security vulnerabilities stemming from their dynamic, distributed, and resource-constrained nature. Traditional security paradigms are often inadequate for these complex cyber-physical systems. This paper proposes a novel, cross-layer security framework that ensures robust and lightweight operation for UAV swarms. The framework is founded on a novel Entropy-Derived Physically Unclonable Function (EPUF) based on DRAM, which employs a data-driven characterization process designed to achieve near 100% reliability in simulation through a data-driven characterization process, which is validated through extensive simulation, addressing a critical limitation of conventional PUFs. To counteract sophisticated threats, we formulate the key management problem as a Markov Decision Process (MDP) and introduce a deep reinforcement learning (DRL) agent that dynamically optimizes key update frequency, balancing security posture against energy consumption. Furthermore, we leverage a lightweight, permissioned blockchain as a decentralized trust anchor for public key management, providing an immutable and resilient ledger and enhancing the principles of distributed and edge intelligence. The core authentication protocol's security is formally verified using the ProVerif tool and Belief Logic, proving its robustness against a Dolev-Yao adversary. Experimental simulations demonstrate that our framework significantly outperforms conventional methods, reducing authentication latency and energy consumption by over 95% compared to PKI-based schemes while effectively mitigating replay and impersonation attacks.