用变分自编码器压缩特征，实现汽车网络高精度低延迟入侵检测。
Transforming In-Vehicle Network Intrusion Detection: VAE-based Knowledge Distillation Meets Explainable AI
- 通过VAE压缩特征空间，仅1669参数实现高效知识蒸馏。
- 在多个攻击类型下达到100%召回率与精确率，漏检率为0。
- 结合SHAP解释性技术，让模型决策过程可理解，适合车载部署。
在自动驾驶车辆不断发展的背景下，保障车载网络（IVN）安全至关重要。本文提出一种新型入侵检测系统KD-XVAE，采用基于变分自编码器（VAE）的知识蒸馏方法，在提升性能的同时显著降低复杂度：模型仅含1669个参数，单批推理时间仅为0.3毫秒，适用于资源受限的车载环境。在HCRL Car-Hacking数据集上的测试表明，该模型在多种攻击类型（包括DoS、Fuzzing、Gear Spoofing和RPM Spoofing）下均取得完美表现，召回率、精确率和F1分数均为100%，误报率（FNR）为0%。在CICIoV2024数据集上的对比实验进一步证明其优于传统机器学习模型，各项检测指标均达完美。此外，本研究融合可解释人工智能（XAI）技术，利用SHAP值分析各潜在维度的重要性，并映射回原始特征，提升决策透明性。该工作通过整合前沿技术，解决了自动驾驶车辆中高效、可信、可靠入侵检测系统的部署难题，有效应对新兴网络威胁。
原文摘要 · Abstract (English)
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.
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