TY - GEN
T1 - SOME/IP Intrusion Detection using Deep Learning-based Sequential Models in Automotive Ethernet Networks
AU - Alkhatib, Natasha
AU - Ghauch, Hadi
AU - Danger, Jean Luc
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - Intrusion Detection Systems are widely used to detect cyberattacks, especially on protocols vulnerable to hacking attacks such as SOME/IP. In this paper, we present a deep learning-based sequential model for offline intrusion detection on SOME/IP application layer protocol. To assess our intrusion detection system, we have generated and labeled a dataset1 with several classes representing realistic intrusions, and a normal class-a significant contribution due to the absence of such publicly available datasets. Furthermore, we also propose a recurrent neural network (RNN), as an instance of deep learning-based sequential model, that we apply to our generated dataset. The numerical results show that RNN excel at predicting in-vehicle intrusions, with F1 Scores and AUC values greater than 0.8 depending on each intrusion type.
AB - Intrusion Detection Systems are widely used to detect cyberattacks, especially on protocols vulnerable to hacking attacks such as SOME/IP. In this paper, we present a deep learning-based sequential model for offline intrusion detection on SOME/IP application layer protocol. To assess our intrusion detection system, we have generated and labeled a dataset1 with several classes representing realistic intrusions, and a normal class-a significant contribution due to the absence of such publicly available datasets. Furthermore, we also propose a recurrent neural network (RNN), as an instance of deep learning-based sequential model, that we apply to our generated dataset. The numerical results show that RNN excel at predicting in-vehicle intrusions, with F1 Scores and AUC values greater than 0.8 depending on each intrusion type.
KW - Automotive Ethernet
KW - Deep Learning
KW - In-vehicle security
KW - Intrusion detection
KW - Recurrent Neural Network
KW - SOME/IP
KW - Sequential Models
KW - Service-oriented communication
UR - https://www.scopus.com/pages/publications/85123572004
U2 - 10.1109/IEMCON53756.2021.9623129
DO - 10.1109/IEMCON53756.2021.9623129
M3 - Conference contribution
AN - SCOPUS:85123572004
T3 - 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2021
SP - 954
EP - 962
BT - 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2021
A2 - Chakrabarti, Satyajit
A2 - Paul, Rajashree
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2021
Y2 - 27 October 2021 through 30 October 2021
ER -