| Title |
HoneyTwin: Securing smart cities with machine learning-enabled SDN edge and cloud-based honeypots |
| ID_Doc |
39422 |
| Authors |
Alani, MM |
| Title |
HoneyTwin: Securing smart cities with machine learning-enabled SDN edge and cloud-based honeypots |
| Year |
2024 |
| Published |
|
| DOI |
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 |
Smart city; Security; Machine learning; Honeypot; Edge |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001202793600001 |
| WoS Category |
Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
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