article · IEEE Transactions on Vehicular Technology
The Internet of Vehicles connects vehicles to networks and external entities within intelligent transport systems, yet rising connectivity brings critical cybersecurity risks. Existing intrusion detection systems frequently fail against zero-day attacks that exploit unknown vulnerabilities. To address this, a framework called Zero-X combines deep neural networks with open-set recognition to detect both known and unknown cyberattacks. The system incorporates blockchain technology to support decentralised, trusted federated learning. This setup allows connected and autonomous vehicles alongside security operation centres to share threat insights without exposing their sensitive private data. Evaluated on two modern network traffic datasets, the framework demonstrated a high detection rate alongside a low false positive rate, outperforming existing security approaches.
Modern transport networks rely heavily on constant digital communication, making connected vehicles prime targets for previously unseen digital threats. By detecting unknown cyberattacks collaboratively without leaking private vehicle data, this research helps protect critical road infrastructure and passenger safety from sophisticated intrusions that exploit novel vulnerabilities.
This research is relevant to automotive manufacturers, fleet operators, and automotive security operations centres seeking intrusion detection tools. The architecture is situated at an applied research stage, validated on two benchmark network traffic datasets rather than live vehicle fleets. Real-world commercialisation would require integration into automotive communication hardware and live testing across dynamic road environments.
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The Internet of Vehicles (IoV) is a crucial technology for Intelligent Transportation Systems (ITS) that integrates vehicles with the Internet and other entities. The emergence of 5 G and the forthcoming 6 G networks presents an enormous potential to transform the IoV by enabling ultra-reliable, low-latency, and high-bandwidth communications. Nevertheless, as connectivity expands, cybersecurity threats have become a significant concern. The issue has been further exacerbated by the rising number of zero-day (0-day) attacks, which can exploit unknown vulnerabilities and bypass existing Intrusion Detection Systems (IDSs). In this paper, we propose Zero-X, an innovative security framework that effectively detects both 0-day and N-day attacks. The framework achieves this by combining deep neural networks with Open-Set Recognition (OSR). Our approach introduces a novel scheme that uses blockchain technology to facilitate trusted and decentralized federated learning (FL) of the Zero-X framework. This scheme also prioritizes privacy preservation, enabling both CAVs and Security Operation Centers (SOCs) to contribute their unique knowledge while protecting the privacy of their sensitive data. To the best of our knowledge, this is the first work to leverage OSR in combination with privacy-preserving FL to identify both 0-day and N-day attacks in the realm of IoV. The in-depth experiments on two recent network traffic datasets show that the proposed framework achieved a high detection rate while minimizing the false positive rate. Comparison with related work showed that the Zero-X framework outperforms existing solutions.
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DOI: 10.1109/tvt.2024.3385916
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