Autoencoder-Based Anomaly Detection on CAN Bus Networks with Surrogate Decision-Tree Interpretability

Abstract

Modern vehicles depend on dozens of Electronic Control Units (ECUs) that exchange messages over the Controller Area Network (CAN) bus. Because CAN lacks authentication and encryption, it remains susceptible to message-injection attacks. We present an empirical study of reconstruction-based CAN intrusion detection across four autoencoder families—ANN, CNN, LSTM, and Transformer—trained exclusively on attack-free traffic, together with a post-hoc surrogate-tree interface designed for analyst triage under low-prevalence operation. An AnomalyWrapper adapted from the Trustee framework [1] converts each autoencoder’s reconstruction errors into binary labels; a high-fidelity surrogate decision tree is then fitted to approximate those labels, and individual root-to-leaf decision paths are exposed as per-alert explanations. The LSTM autoencoder achieves an ROC-AUC of 0.996 and a false-positive rate of 0.9 % at a Normal-only threshold (μ+3σ). Because precision-recall measures depend on class prevalence, we evaluate under resampled anomaly rates of 1 %, 0.1 %, and 0.01 %, showing that recall remains stable while precision degrades—a well-known base-rate phenomenon that underscores the need for interpretable triage. Code and models: [GitHub repository].