An Efficient Framework for Secure Communication in Internet of Drone Networks Using Deep Computing

The rapid deployment of the Internet of Drones (IoD) across different fields has brought forth enormous security threats in real-time data communication. To overcome authentication vulnerabilities, this paper introduces a secure lightweight framework integrating deep learning-based user behavior analysis and cryptographic protocols. The proposed framework is verified through AVISPA security verification against replay, man-in-the-middle, and impersonation attacks. Performance analysis via NS2 simulations based on changing network parameters (5–50 drones, 1–20 users, 2–8 ground stations) validates enhancements in computation overhead, authentication delay, memory usage, power consumption, and communication effectiveness in comparison with recent models such as LDAP, TAUROT, IoD-Auth, and LEMAP, thereby establishing our system as an optimal choice for safe IoD operation.