Citation

Ahmed, Tahir H. and Tiang, Jun Jiat and Mahmud, Azwan and Chung, Gwo Chin (2023) Detection and Mitigation of SQL and Jamming Attacks on Switched Beam Antenna in V2V Networks Using Federated Learning. In: 2023 IEEE Symposium on Industrial Electronics & Applications (ISIEA), 15-16 July 2023, Kuala Lumpur, Malaysia.

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Abstract

This research article proposes a federated learning approach for detecting and mitigating SQL injection and jamming attacks on switched beam antenna in V2V networks. The proposed approach utilizes the collective intelligence of multiple nodes in the network to train a machine learning model for detecting malicious traffic patterns. We evaluate the performance of the proposed approach using both simulated and real-world V2V network data, and demonstrate its effectiveness in improving network security and performance. Our results show that the proposed approach can significantly reduce the latency and throughput overhead associated with conventional security mechanisms, while maintaining high levels of accuracy in detecting attacks.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Federated Learning, V2V Networks, Switched Beam Antenna, SQL Injection Attacks, Jamming Attacks, Network Security, Machine Learning.
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics > TK7871 Electronics--Materials
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	05 Oct 2023 06:05
Last Modified:	05 Oct 2023 06:05
URII:	http://shdl.mmu.edu.my/id/eprint/11742

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