  Modern vehicles rely on a fleet of electronic control units (ECUs) connected
through controller area network (CAN) buses for critical vehicular control.
With the expansion of advanced connectivity features in automobiles and the
elevated risks of internal system exposure, the CAN bus is increasingly prone
to intrusions and injection attacks. As ordinary injection attacks disrupt the
typical timing properties of the CAN data stream, rule-based intrusion
detection systems (IDS) can easily detect them. However, advanced attackers can
inject false data to the signal/semantic level, while looking innocuous by the
pattern/frequency of the CAN messages. The rule-based IDS, as well as the
anomaly-based IDS, are built merely on the sequence of CAN messages IDs or just
the binary payload data and are less effective in detecting such attacks.
Therefore, to detect such intelligent attacks, we propose CANShield, a deep
learning-based signal-level intrusion detection framework for the CAN bus.
CANShield consists of three modules: a data preprocessing module that handles
the high-dimensional CAN data stream at the signal level and parses them into
time series suitable for a deep learning model; a data analyzer module
consisting of multiple deep autoencoder (AE) networks, each analyzing the
time-series data from a different temporal scale and granularity, and finally
an attack detection module that uses an ensemble method to make the final
decision. Evaluation results on two high-fidelity signal-based CAN attack
datasets show the high accuracy and responsiveness of CANShield in detecting
advanced intrusion attacks.
