Authors: Muhammet Anil Yagiz, Pedram MohajerAnsari, Mert D. Pese, Polat Goktas
Abstract: In the evolving landscape of autonomous vehicles, ensuring robust in-vehicle
network (IVN) security is paramount. This paper introduces an advanced
intrusion detection system (IDS) called KD-XVAE that uses a Variational
Autoencoder (VAE)-based knowledge distillation approach to enhance both
performance and efficiency. Our model significantly reduces complexity,
operating with just 1669 parameters and achieving an inference time of 0.3 ms
per batch, making it highly suitable for resource-constrained automotive
environments. Evaluations in the HCRL Car-Hacking dataset demonstrate
exceptional capabilities, attaining perfect scores (Recall, Precision, F1 Score
of 100%, and FNR of 0%) under multiple attack types, including DoS, Fuzzing,
Gear Spoofing, and RPM Spoofing. Comparative analysis on the CICIoV2024 dataset
further underscores its superiority over traditional machine learning models,
achieving perfect detection metrics. We furthermore integrate Explainable AI
(XAI) techniques to ensure transparency in the model’s decisions. The VAE
compresses the original feature space into a latent space, on which the
distilled model is trained. SHAP(SHapley Additive exPlanations) values provide
insights into the importance of each latent dimension, mapped back to original
features for intuitive understanding. Our paper advances the field by
integrating state-of-the-art techniques, addressing critical challenges in the
deployment of efficient, trustworthy, and reliable IDSes for autonomous
vehicles, ensuring enhanced protection against emerging cyber threats.
Source: http://arxiv.org/abs/2410.09043v1
