NeuroCAN: Contextual Anomaly Detection in Controller Area Networks

Abstract

The Controller Area Network (CAN) is an established standard for inter-connecting onboard Electronic Control Units (ECUs) in a vehicle. Through sensors and actuators, ECUs maintain critical vehicle functions such as transmission and engine control. However, security was never a part of CAN design and hence ECUs are susceptible to a wide range of attacks. Thus, in recent years, several anomaly detection systems have been proposed for the CAN bus in order to detect anomalies caused by adversarial attacks or misbehaving sensors. These systems generally try to detect deviations from individual sensor's expected behavior. As such, they are ineffective against attacks that target multiple sensors to accomplish a collective desired behavior without changing the expected behavior of each individual sensor. In this paper, we focus on detecting such attacks by identifying contextual CAN anomalies in realtime. To this end, we present NeuroCAN, a deep learning-based detection system that utilizes Linear embeddings and Long Short Term Memory (LSTM) units to learn the spatio-temporal correlations among sensor data on the CAN bus at a frame level. By exploiting such correlations, NeuroCAN is able to detect contextual anomalies that are otherwise difficult to detect by analyzing individual sensor data. We evaluate NeuroCAN using two publicly available CAN datasets and compare it against existing approaches. Our results show that NeuroCAN achieves over 95% detection accuracy and performs significantly better than the existing baselines.