Self-Attention Bottleneck Network for Self-Supervised Anomaly Detection in CAN Data

With the many advancements in automobile technology, there has been a sharp increase in the number of sensors and systems present in vehicles. This has enabled a rapid increase in the features and capabilities available in modern automobiles but has also vastly increased their vulnerability surface. Attackers are now able to remotely attack and control some facets of modern automobiles, which creates dangerous situations for drivers and passengers. In order to address this, this paper proposes a self-attention bottleneck network utilizing an encoder-decoder architecture. This is used as an anomaly detection system (ADS) that can detect anomalous behavior present within CAN bus communications. We evaluated this approach using a publicly available CAN bus car hacking dataset and show that our architecture is able to achieve an accuracy of over 99% for detecting anomalies present in CAN bus data.