Self-Supervised Anomaly Detection for In-Vehicle Network Using Noised Pseudo Normal Data
Hyun Min Song, Huy Kang Kim
Abstract
As the risk of cyber and safety threats to vehicle systems has increased, the anomaly detection in in-vehicle networks (IVN) has received the attention of researchers. Although, machine-learning-based anomaly detection methods have been proposed, there are limitations in detecting unknown attacks that the model has not learned because general supervised learning-based approaches depend on training dataset. To solve this problem, we propose a novel self-supervised method for IVN anomaly detection using noised pseudo normal data. The proposed method consists of two deep-learning models of the generator and the detector, which generates noised pseudo normal data and detects anomalies, respectively. Firstly, the generator is trained with only normal network traffic to generate pseudo normal traffic data. Then, the anomaly detector is trained to classify normal traffic and noised pseudo normal traffic as normal and abnormal, respectively. The experimental results demonstrate that the anomaly detection models, trained with the proposed method, not only significantly improved in the detection of unknown attacks, but also outperformed other semi-supervised learning-based methods.