Deep Contractive Autoencoder Based Anomaly Detection for In Vehicle Controller Area Network (CAN)
With the emerging wireless technology integrated into modern vehicles, this introduces an enormous number of vulnerabilities for adversaries to compromise the vehicle internal system. Nonetheless, the attacks can be alleviated using anomaly detection mechanism which have been proven to be effective in monitoring and detecting attacks. In this paper, we developed an anomaly detection using an unsupervised deep learning based approach, known as Deep Contractive Autoencoders (DCAEs) The DCAEs, which is one of the regularize autoencoders model imposed a different penalty term to the CAN data representation in order to encourage robustness towards small changes. To accomplish this purpose, we captured CAN bus data from three different vehicles, pre processed them using the max absolute normalization, and evaluated the model over three types of attacks. Finally, the experimental results demonstrated that DCAEs yield a 91 100% detection rate which outperformed other variants of regularized autoencoders.