Hyperdimensional Computing for Intrusion Detection in the Internet of Vehicles

With the rapid development of the Internet of Vehicles (IoV), ensuring the security of vehicular networks has become a critical challenge. Machine Learning (ML) and Deep Learning (DL) techniques are widely used to develop classifierbased Intrusion Detection Systems (IDS). However, traditional methods often suffer from high computational complexity, limiting their effectiveness in real-time detection. Hyperdimensional Computing (HDC), a brain-inspired machine learning paradigm, offers a compelling combination of high precision with exceptional robustness and training efficiency. In this paper, we present Hyperdimensional Intrusion Detection in IoV (HIDIV), a lightweight and efficient framework designed explicitly for IoV security. HIDIV introduces a dynamic error update module, enabling faster convergence and higher accuracy than conventional HDC methods. Experimental results demonstrate that HIDIV not only outperforms traditional HDC in accuracy but also improves training and inference speeds by approximately $43.4 \%$ and $33.2 \%$, respectively, compared to state-of-the-art machine learning methods, while maintaining comparable accuracy.