用生成模型检测汽车网络异常，小样本下仍精准识别攻击
An Anomaly Detection System Based on Generative Classifiers for Controller Area Network
- 基于变分贝叶斯的深度潜变量模型构建因果概率图
- 在公开数据集上准确率与F1值超越现有主流模型
- 适合资源受限场景下的车载系统安全防护
随着现代车辆中电子系统日益复杂且普遍，保障车载网络的安全至关重要，尤其因其多为安全关键系统。研究已证明现代车辆易受各类攻击，攻击者可借此控制并破坏安全关键电子系统。因此，学术界提出了多种入侵检测系统（IDS）以应对车辆网络中的网络攻击。本文提出一种新型基于生成分类器的入侵检测系统（IDS），专用于汽车网络中的控制器局域网（CAN）异常检测。该系统利用变分贝叶斯方法，通过深度潜变量模型构建条件概率的因果图，并采用自编码器架构估算条件概率，最终通过贝叶斯推断得出预测概率。在公开的Car-hacking数据集上的对比实验表明，所提分类器在提升检测准确率和F1分数方面优于现有先进模型。该系统在训练数据有限条件下依然表现优异，为车载系统提供了更强的安全保障。
原文摘要 · Abstract (English)
As electronic systems become increasingly complex and prevalent in modern vehicles, securing onboard networks is crucial, particularly as many of these systems are safety-critical. Researchers have demonstrated that modern vehicles are susceptible to various types of attacks, enabling attackers to gain control and compromise safety-critical electronic systems. Consequently, several Intrusion Detection Systems (IDSs) have been proposed in the literature to detect such cyber-attacks on vehicles. This paper introduces a novel generative classifier-based Intrusion Detection System (IDS) designed for anomaly detection in automotive networks, specifically focusing on the Controller Area Network (CAN). Leveraging variational Bayes, our proposed IDS utilizes a deep latent variable model to construct a causal graph for conditional probabilities. An auto-encoder architecture is utilized to build the classifier to estimate conditional probabilities, which contribute to the final prediction probabilities through Bayesian inference. Comparative evaluations against state-of-the-art IDSs on a public Car-hacking dataset highlight our proposed classifier's superior performance in improving detection accuracy and F1-score. The proposed IDS demonstrates its efficacy by outperforming existing models with limited training data, providing enhanced security assurance for automotive systems.
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