HoneyTwin: Securing smart cities with machine learning-enabled SDN edge and cloud-based honeypots

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.