  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 5G and the forthcoming 6G 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 ZeroX 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.
