Deep Bayesian Learning for Car Hacking Detection
This work addresses a life-threatening security issue for smart cars, offering an incremental improvement over current machine learning methods by reducing overconfidence and enhancing explainability.
The paper tackled the problem of car hacking detection in self-driving and connected vehicles by investigating Deep Bayesian Learning models, which showed advantages over existing deep learning methods by capturing data uncertainty and providing more informative predictions.
With the rise of self-drive cars and connected vehicles, cars are equipped with various devices to assistant the drivers or support self-drive systems. Undoubtedly, cars have become more intelligent as we can deploy more and more devices and software on the cars. Accordingly, the security of assistant and self-drive systems in the cars becomes a life-threatening issue as smart cars can be invaded by malicious attacks that cause traffic accidents. Currently, canonical machine learning and deep learning methods are extensively employed in car hacking detection. However, machine learning and deep learning methods can easily be overconfident and defeated by carefully designed adversarial examples. Moreover, those methods cannot provide explanations for security engineers for further analysis. In this work, we investigated Deep Bayesian Learning models to detect and analyze car hacking behaviors. The Bayesian learning methods can capture the uncertainty of the data and avoid overconfident issues. Moreover, the Bayesian models can provide more information to support the prediction results that can help security engineers further identify the attacks. We have compared our model with deep learning models and the results show the advantages of our proposed model. The code of this work is publicly available