Multihop Intruder Node Detection Scheme (MINDS) for Secured Drones' FANET Communication

Abstract

Unmanned aerial vehicles (UAVs) are becoming integral to time-sensitive logistics and intelligent mobility systems due to their flexibility, low deployment cost, and real-time connectivity. However, their open and dynamic communication environment—typically organized as flying ad hoc networks (FANETs)—makes them highly vulnerable to a wide spectrum of cyber threats. To address this, we propose a novel multihop intrusion node detection scheme (MINDS) powered by an AI-driven ensemble learning model, X-CID, optimized for lightweight drone networks. The proposed system integrates a decentralized multi-hop architecture with intra- and inter-cluster communication validation, enabling real-time anomaly detection across the physical, communication, and architectural layers of UAV systems. To improve detection performance under resource constraints, feature selection is applied using the Pearson correlation coefficient (PCC), and model hyperparameters are fine-tuned using randomized search cross-validation. Trained and evaluated on three benchmark datasets (WSN-DS, NSL-KDD, CICIDS2017) covering 24 distinct attack types, X-CID outperforms traditional models in F1-score (up to 99.84%), accuracy (up to 99.70%), and achieves low false alarm rates with competitive latency. The proposed approach ensures robust, scalable, and energy-efficient security for autonomous drone communication, making it suitable for critical missions in logistics, disaster response, and aerial surveillance.