A survey of deep learning-based intrusion detection in automotive applications

Modern automobiles depend on internal vehicle networks (IVNs) to control systems from the anti-lock brakes to the transmission to the locks on the doors. Many IVNs, particularly the Controller Area Network (CAN) bus, were developed with little regard for security, since the IVNs of the past were isolated from the outside world. In the present day, the assumption of isolation no longer applies. Cellular service, Wi-Fi, and Bluetooth are just a few examples of the connectivity of contemporary automobiles. Researchers have explored a number of automotive security enhancements, but such enhancements are often roadblocked by implementation challenges, complexity, and expense. An intrusion detection system (IDS) is a promising automotive security enhancement that requires little, if any, adjustment to a vehicle's existing infrastructure. Deep learning techniques can augment the capability of automotive IDSs, improving detection accuracy and precision. This paper provides a comprehensive overview of deep learning-based IDSs in automotive networks. We assemble various deep learning schemes, categorize them according to their topologies and techniques, and highlight their distinct contributions. In addition, we analyze each scheme's evaluation in terms of datasets, attack types, and metrics. We summarize the results of the schemes and assess the advantages and disadvantages of different deep learning architectures.