Citation

Attaullah, Hafiz Muhammad and Ehsan, Muhammad and Basheer, Shakila and Alluhaidan, Ala Saleh (2026) GenFed-IDS: A Lightweight Federated Generative AI Framework for UAV Anomaly Detection in Rescue Operations. IEEE Transactions on Consumer Electronics, 20 (20). p. 1. ISSN 0098-3063

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Abstract

Unmanned Aerial Vehicles are increasingly deployed in consumer applications such as logistics, disaster recovery, and surveillance, yet their wireless communication links remain highly vulnerable to cyber-attacks. Traditional intrusion detection systems (IDS) often struggle in UAV environments due to resource constraints, dynamic network conditions, and scarcity of labeled datasets. To address these challenges, we propose a Generative AI-enabled lightweight IDS framework tailored for UAV communication networks. The framework integrates hybrid Convolutional Neural Network–Gated Recurrent Unit (CNN-GRU) autoencoders with generative augmentation and knowledge distillation, achieving high accuracy while maintaining computational efficiency. Explainability is incorporated via SHapley Additive exPlanations (SHAP) analysis to ensure trustworthy and interpretable decision-making. Experimental evaluations on a multimodal UAV dataset demonstrate state-of-the-art performance with 99.49% accuracy, 99.48% recall, and AUROC of 0.9999, alongside a student model that reduces model size, inference latency, and memory footprint by nearly 50%. Comparative results confirm that the proposed framework outperforms recent IDS baselines in both detection capability and lightweight deployment, offering a practical and scalable solution for next-generation UAV communication networks.

Item Type:	Article
Uncontrolled Keywords:	IDS, UAV, Generative AI, Federated Learning, Variational Autoencoder, Cyber-Physical Security
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	03 Mar 2026 00:46
Last Modified:	03 Mar 2026 00:46
URII:	http://shdl.mmu.edu.my/id/eprint/15407

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