AI-Enhanced Digital Twin Framework for Cyber-Resilient 6G Internet-of-Vehicles Networks

Yigit, Yagmur; Maglaras, Leandros; Buchanan, William J.; Canberk, Berk; Shin, Hyundong; Duong, Trung Q.

Abstract

Digital twin technology is crucial to the development of the sixth-generation (6G) Internet of Vehicles (IoV) as it allows the monitoring and assessment of the dynamic and complicated vehicular environment. However, 6G IoV networks have critical challenges in network security and computational efficiency, which need to be addressed. Existing digital twin technologies in 6G IoV networks often suffer from limitations such as reliance on static models and high computational demands, leading to unstable attack detection and inefficiencies. Their results for attack detection performance metrics, precision, detection rate, and F1-Score are insufficient for 6G IoV. Moreover, these systems concentrate all computational processes within the digital twin’s service layer, leading to inefficiencies. To address these challenges, we introduce a novel artificial intelligence (AI) enhanced digital twin framework designed to significantly improve 6G IoV network security and computational efficiency under dynamic conditions. Our framework employs an advanced feature engineering module that uses feature selection methods and stacked sparse autoencoders (ssAE) to reduce feature dimensions within the cyber twin layer, effectively distributing the overall computational load. It also utilises an online learning module which enables a network-aware attack detection mechanism for precise attack detection. The proposed solution exhibits a stable performance of around 98% success rate regarding attack detection metrics against two datasets. Specifically, our solution reduces system latency by 12%, energy consumption by 15%, RAM usage by 20%, and improves packet delivery rates by 6.1%. These findings underscore the potential of our framework to enhance the robustness and responsiveness of 6G IoV systems, offering a significant contribution to vehicular network security and management.

Citation

Yigit, Y., Maglaras, L., Buchanan, W. J., Canberk, B., Shin, H., & Duong, T. Q. (2024). AI-Enhanced Digital Twin Framework for Cyber-Resilient 6G Internet-of-Vehicles Networks. IEEE Internet of Things, 11(22), 36168-36181. https://doi.org/10.1109/jiot.2024.3455089

• Journal Article Type: Article
• Acceptance Date: Aug 27, 2024
• Online Publication Date: Sep 10, 2024
• Publication Date: 2024
• Deposit Date: Sep 17, 2024
• Publicly Available Date: Sep 17, 2024
• Print ISSN: 2327-4662
• Electronic ISSN: 2327-4662
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 11
• Issue: 22
• Pages: 36168-36181
• DOI: https://doi.org/10.1109/jiot.2024.3455089
• Keywords: AI, Security, Digital Twin, IoV, ITS, VANET