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
翻译:随着自驾驶汽车和相联车辆的兴起,汽车配备了各种辅助驾驶员或支持自驾驶系统的各种装置。毫无疑问,汽车已经变得更加智能,因为我们可以在汽车上部署越来越多的装置和软件。因此,汽车的助驾驶和自驾驶系统的安全成为一个威胁生命的问题,因为智能汽车可能会被恶意袭击侵入,从而造成交通事故。目前,汽车黑客探测中广泛使用机能学习和深层学习方法。然而,机器学习和深层学习方法很容易过于自信,容易被精心设计的对抗性实例击败。此外,这些方法无法为安全工程师提供进一步分析的解释。在这项工作中,我们调查了深贝叶斯人学习模式,以检测和分析汽车黑客行为。贝叶人的学习方法可以捕捉数据的不确定性,避免过度自信问题。此外,贝叶斯模式可以提供更多信息,支持预测结果,帮助安全工程师进一步识别攻击事件。我们把模型与深层次学习模型进行比较,结果显示我们提议的模型的优点。