Securing Smart Vehicles Through Federated Learning

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.