  Modern automotive functions are controlled by a large number of small
computers called electronic control units (ECUs). These functions span from
safety-critical autonomous driving to comfort and infotainment. ECUs
communicate with one another over multiple internal networks using different
technologies. Some, such as Controller Area Network (CAN), are very simple and
provide minimal or no security services. Machine learning techniques can be
used to detect anomalous activities in such networks. However, it is necessary
that these machine learning techniques are not prone to adversarial attacks. In
this paper, we investigate adversarial sample vulnerabilities in four different
machine learning-based intrusion detection systems for automotive networks. We
show that adversarial samples negatively impact three of the four studied
solutions. Furthermore, we analyze transferability of adversarial samples
between different systems. We also investigate detection performance and the
attack success rate after using adversarial samples in the training. After
analyzing these results, we discuss whether current solutions are mature enough
for a use in modern vehicles.
