Resource-Constrained CAN Intrusion Detection with Distilled Decision Trees

Abstract

Controller Area Network (CAN) is the dominant in-vehicle bus, yet its broadcast design and absent authentication leave it exposed to injection and spoofing attacks. Existing deep-learning intrusion detection systems achieve strong accuracy but depend on ML inference frameworks incompatible with the resource budgets of production Electronic Control Units (ECUs). We present a systems-oriented pipeline that trains four neural architectures—Transformer, CNN, LSTM, and ANN—on a five-class labeled CAN dataset and distills each into a framework-free C++ decision tree via the Trustee protocol. We observe and address a systematic false-alarm collapse on Normal-class traffic: standard Trustee produces around 30% FPR-N because Normal frames constitute only 5.4% of training data. A class-balanced Trustee variant, which oversamples Normal by factor α=5, reduces FPR-N to 13.8±1.4% (three Trustee runs) while preserving 91.1±0.6% accuracy and 0.918±0.006 macro-F1 at 2.1% CPU on a Raspberry Pi 4, versus 22.6% for TF-Lite int8 and 59.4% for TensorFlow on the same board. We additionally report α and depth ablations, three-seed teacher statistics, and per-class recall for all models.Code: https://github.com/Amirmasoud-Pourmiri/Lightweight-Neural-Network-Distillation-for-Real-Time-CAN-Bus-Intrusion-Detection-in-Vehicles