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Title	Honeytwin: Securing Smart Cities With Machine Learning-Enabled Sdn Edge And Cloud-Based Honeypots
ID_Doc	29254
Authors	Alani M.M.
Year	2024
Published	Journal of Parallel and Distributed Computing, 188
DOI	http://dx.doi.org/10.1016/j.jpdc.2024.104866
Abstract	With the promise of higher throughput, and better response times, 6G networks provide a significant enabler for smart cities to evolve. The rapidly-growing reliance on connected devices within the smart city context encourages malicious actors to target these devices to achieve various malicious goals. In this paper, we present a novel defense technique that creates a cloud-based virtualized honeypot/twin that is designed to receive malicious traffic through edge-based machine learning-enabled detection system. The proposed system performs early identification of malicious traffic in a software defined network-enabled edge routing point to divert that traffic away from the 6G-enabled smart city endpoints. Testing of the proposed system showed an accuracy exceeding 99.8%, with an F-1 score of 0.9984.
Author Keywords	Edge; Honeypot; Machine learning; Security; Smart city

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