TY - GEN
T1 - On the Robustness of Intrusion Detection Systems for Vehicles Against Adversarial Attacks
AU - Choi, Jeongseok
AU - Kim, Hyoungshick
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Because connected cars typically have several communication capabilities (through 5G, WiFi, and Bluetooth), and third-party applications can be installed on the cars, it would be essential to deploy intrusion detection systems (IDS) to prevent attacks from external attackers or malicious applications. Therefore, many IDS proposals have been presented to protect the controller area network (CAN) in a vehicle. Some studies showed that deep neural network models could be effectively used to detect various attacks on the CAN bus. However, it is still questionable whether such an IDS is sufficiently robust against adversarial attacks that are crafted aiming to target the IDS. In this paper, we present a genetic algorithm to generate adversarial CAN attack messages for Denial-of-Service (DoS), fuzzy, and spoofing attacks to target the state-of-the-art deep learning-based IDS for CAN. The experimental results demonstrate that the state-of-the-art IDS is not effective in detecting the generated adversarial CAN attack messages. The detection rates of the IDS were significantly decreased from 99.27%, 96.40%, and 99.63% to 2.24%, 11.59%, and 0.01% for DoS, fuzzy, and spoofing attacks, respectively.
AB - Because connected cars typically have several communication capabilities (through 5G, WiFi, and Bluetooth), and third-party applications can be installed on the cars, it would be essential to deploy intrusion detection systems (IDS) to prevent attacks from external attackers or malicious applications. Therefore, many IDS proposals have been presented to protect the controller area network (CAN) in a vehicle. Some studies showed that deep neural network models could be effectively used to detect various attacks on the CAN bus. However, it is still questionable whether such an IDS is sufficiently robust against adversarial attacks that are crafted aiming to target the IDS. In this paper, we present a genetic algorithm to generate adversarial CAN attack messages for Denial-of-Service (DoS), fuzzy, and spoofing attacks to target the state-of-the-art deep learning-based IDS for CAN. The experimental results demonstrate that the state-of-the-art IDS is not effective in detecting the generated adversarial CAN attack messages. The detection rates of the IDS were significantly decreased from 99.27%, 96.40%, and 99.63% to 2.24%, 11.59%, and 0.01% for DoS, fuzzy, and spoofing attacks, respectively.
KW - Adversarial attack
KW - Controller area network (CAN)
KW - Intrusion detection system
UR - https://www.scopus.com/pages/publications/85119013604
U2 - 10.1007/978-3-030-89432-0_4
DO - 10.1007/978-3-030-89432-0_4
M3 - Conference contribution
AN - SCOPUS:85119013604
SN - 9783030894313
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 39
EP - 50
BT - Information Security Applications - 22nd International Conference, WISA 2021, Revised Selected Papers
A2 - Kim, Hyoungshick
PB - Springer Science and Business Media Deutschland GmbH
T2 - 22nd World Conference on Information Security Application, WISA 2021
Y2 - 11 August 2021 through 13 August 2021
ER -