You can also check out our work on CAN-ADF.
If you find our work useful for your research, please consider citing the following papers :)
@inproceedings{10.1145/3341105.3373868,
author = {Tariq, Shahroz and Lee, Sangyup and Woo, Simon S.},
title = {CANTransfer: Transfer Learning Based Intrusion Detection on a Controller Area Network Using Convolutional LSTM Network},
year = {2020},
isbn = {9781450368667},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3341105.3373868},
doi = {10.1145/3341105.3373868},
abstract = {In-vehicle communications, due to simplicity and reliability, a Controller Area Network (CAN) bus is widely used as the de facto standard to provide serial communications between Electronic Control Units (ECUs). However, prior research exhibits several network-level attacks can be easily performed and exploited in the CAN bus. Additionally, new types of intrusion attacks are discovered very frequently. However, unless we have a large amount of data about an intrusion, developing an efficient deep neural network-based detection mechanism is not easy. To address this challenge, we propose CANTransfer, an intrusion detection method using Transfer Learning for CAN bus, where a Convolutional LSTM based model is trained using known intrusion to detect new attacks. By applying one-shot learning, the model can be adaptable to detect new intrusions with a limited amount of new datasets. We performed extensive experimentation and achieved a performance gain of 26.60% over the best baseline model for detecting new intrusions.},
booktitle = {Proceedings of the 35th Annual ACM Symposium on Applied Computing},
pages = {1048–1055},
numpages = {8},
keywords = {controller area network, convolutional LSTM, transfer learning, in-vehicle network, intrusion detection},
location = {Brno, Czech Republic},
series = {SAC '20}
}