Abstract
As cars evolve to be smarter than ever, they also become susceptible to attack. Malicious entities can attempt to override automated functions by sending a series of attack signals to the smart vehicle. It is thus imperative that we create systems to detect these attacks on the fly, so that they may be discarded. Machine learning approaches are a natural choice for detecting such attacks based on the payload information. However, machine learning models typically require a large dataset for training, in order to attain good performance. With manufacturers independently gathering this data based on their own cars, it is unlikely that all this data will be available in one place. To address this issue, we explore federated solutions that learn in a distributed manner for increased smart vehicle security. We explore challenging scenarios in which we do not assume an independent and identically distributed (IID) setting for the data, which is typical in many federated learning environments. We investigate various degrees of such heterogeneity in the attack data distribution between different manufacturers, and study the effectiveness of detection systems under them. Furthermore, with a combination of techniques including triplet-mixup based augmentation and a data exchange scheme involving synthetically generated samples, we show that we can attain strong performance in the most challenging label distribution scenarios. We perform our experiments on a publicly available dataset and on a proprietary attack dataset developed for this project.
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Acknowledgement
For the authors in Japan, the research was supported by the ICSCoE Core Human Resources Development Program and JSPS KAKENHI Grant Number 22H03572, Japan.
For the authors in the US, the research was supported in part by the National Center for Transportation Cybersecurity and Resiliency (TraCR) (a U.S. Department of Transportation National University Transportation Center) headquartered at Clemson University, Clemson, South Carolina, USA. Any opinions, findings, conclusions, and recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of TraCR, and the U.S. Government assumes no liability for the contents or use thereof.
Other support was provided in part by the following: NIST Award # 60NANB23D007, NSF awards DMS-1737978, DGE-2039542, OAC-1828467, OAC-1931541, and DGE-1906630, ONR awards N00014-17-1-2995 and N00014-20-1-2738.
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Halim, S.M. et al. (2024). Securing Smart Vehicles Through Federated Learning. In: Mosbah, M., Sèdes, F., Tawbi, N., Ahmed, T., Boulahia-Cuppens, N., Garcia-Alfaro, J. (eds) Foundations and Practice of Security. FPS 2023. Lecture Notes in Computer Science, vol 14551. Springer, Cham. https://doi.org/10.1007/978-3-031-57537-2_2
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