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primary topic: FedWGCA: A Federated Learning Based UAV Intrusion Detection with Gradient Clipping and Attention-Based Neural Networks

FedWGCA: A Federated Learning Based UAV Intrusion Detection with Gradient Clipping and Attention-Based Neural Networks
type: AcademicArticle
type: Article
abstract: Unmanned Aerial Vehicles (UAVs) are progressively employed in various applications, including surveillance and logistics. However, their rising usage is coupled by increased cybersecurity threats. Conventional intrusion detection systems (IDS) frequently inadequately address specific problems presented by UAV networks, including dynamic operational environments, varied data distributions, and severe resource constraints. Federated Learning (FL) has emerged as a viable alternative, offering a decentralized approach to collaborative training of intrusion detection models while preserving data privacy. However, FL is not without its shortcomings, including poisoning and backdoor attacks, which can impair the accuracy and reliability of the models, thereby exposing UAVs to advanced cyber threats. This research attempts to design resilient FL-based frameworks that address these drawbacks, boosting the security and resilience of UAV networks in the face of emerging cyber hazards. We present our proposed weighted gradient clipping aggregation (FedWGCA) framework to mitigate the impact of malicious updates during the model training process. Experimental studies show that our FedWGCA outperforms state-of-the-art methods, surpassing FedAvg, FedNova, FedOpt, and FedProx by up to 7.23 in accuracy, 9.99 in precision, and 10.14 in recall. For enhancing resilience in our FL architecture, we further present our robust attention neural network, ANET, which outperforms XGBoost by 0.90 and DNN by 7.10, showcasing its superior precision and reduced false positives in local training. © 2020 IEEE.
date: 2025
is part of: IEEE Open Journal of the Computer Society
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository

Institute of Electrical and Electronics Engineers Inc.
type: Organization

IEEE Open Journal of the Computer Society
type: Collection
issn: 26441268

Md Fahim-Ul-Islam
type: Person
given name: Md
family name: Fahim-Ul-Islam

Halimaton Saadiah Hakimi
type: Person
given name: Halimaton Saadiah
family name: Hakimi

Siti Sarah Maidin
type: Person
given name: Siti Sarah
family name: Maidin

Amitabha Chakrabarty
type: Person
given name: Amitabha
family name: Chakrabarty

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primary topic: FedWGCA: A Federated Learning Based UAV Intrusion Detection with Gradient Clipping and Attention-Based Neural Networks