Citation

Kainat, . and Khan, Muazzam A. and Ullah, Safi and Ullah, Fasee and Khan, Arfat Ahmad and Kamal, Shahid and Aldhyani, Theyazn H. H. and Ali, Farman and Kwak, Daehan (2026) Proactive Threat Hunting and Anomaly Detection in Intelligent Green Transportation: A Federated Edge-AI Framework for In-Vehicle Consumer Networks. IEEE Transactions on Consumer Electronics. p. 1. ISSN 0098-3063

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Abstract

The evolution of Intelligent Green Transportation Systems relies on connected consumer vehicles, which face significant cybersecurity threats that target their internal networks. Traditional cloud-centric security architectures face latency and privacy risks that are incompatible with the real-time, safetycritical requirements of consumer vehicles. This study proposes IDIV-FL, a Federated Edge-AI framework for proactive threat hunting and anomaly detection in the in-vehicle networks. A Convolutional Neural Network (CNN) is utilized at a central server, distributed to consumer vehicles, and iteratively trained on local data. Consumer vehicles share only updated weights, which are aggregated using the FedAvg algorithm to form a global model. Using the CICIoV2024 dataset (in binary, decimal, and hexadecimal formats), IDIV-FL achieved maximum accuracy on binary data and up to 99.6% attack detection on decimal and hexadecimal data, with sub-category classification, the accuracy reaching 99.01% (decimal) and 99.16% (hexadecimal). This demonstrates a scalable, low-latency solution that enhances security for consumer vehicles, supporting proactive threat hunting and privacy-preserving anomaly detection in next-generation consumer electronic ecosystems, while enabling trustworthy and sustainable intelligent transportation.

Item Type:	Article
Uncontrolled Keywords:	Anomaly Detection, Convolutional Neural Network
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	04 Aug 2026 03:39
Last Modified:	04 Aug 2026 03:39
URII:	http://shdl.mmu.edu.my/id/eprint/16471

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