Detecting In-vehicle CAN Message Attacks Using Heuristics and RNNs
In vehicle communications, due to simplicity and reliability, a Controller Area Network (CAN) bus is used as the de facto standard to provide serial communication between Electronic Control Units (ECUs). However, prior research reveals that several network-level attacks can be performed on the CAN bus due to the lack of underlying security mechanism. In this work, we develop an intrusion detection algorithm to detect DoS, fuzzy, and replay attacks injected in a real vehicle. Our approach uses heuristics as well as Recurrent Neural Networks (RNNs) to detect attacks. We test our algorithm with in-vehicle data samples collected from KIA Soul. Our preliminary results show the high accuracy in detecting different types of attacks.
We thank anonymous reviews for providing helpful feedback to improve this work. We also thank Korea Internet & Security Agency (KISA) and Korean Institute of Information Security & Cryptology (KIISC) for the release of CAN dataset. This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the “ICT Consilience Creative Program” (IITP-2015-R0346-15-1007) supervised by the IITP (Institute for Information & communications Technology Promotion) and Basic Science Research Program through the NRF of Korea (NRF-2017R1C1B5076474).
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