TY - GEN
T1 - Detecting In-vehicle CAN Message Attacks Using Heuristics and RNNs
AU - Tariq, Shahroz
AU - Lee, Sangyup
AU - Kim, Huy Kang
AU - Woo, Simon S.
N1 - Funding Information:
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).
Funding Information:
Acknowledgement. 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).
Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85061389818&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-12085-6_4
DO - 10.1007/978-3-030-12085-6_4
M3 - Conference contribution
AN - SCOPUS:85061389818
SN - 9783030120849
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 39
EP - 45
BT - Information and Operational Technology Security Systems - 1st International Workshop, IOSec 2018, CIPSEC Project, Revised Selected Papers
A2 - Marín Tordera, Eva
A2 - Fournaris, Apostolos P.
A2 - Lampropoulos, Konstantinos
PB - Springer Verlag
T2 - 1st International Workshop on Information and Operational Technology Security Systems, IOSec 2018
Y2 - 13 September 2018 through 13 September 2018
ER -