Abstract
Vehicles are becoming more and more connected, this opens up a larger attack surface which not only affects the passengers inside vehicles, but also people around them. These vulnerabilities exist because modern systems are built on the comparatively less secure and old CAN bus framework which lacks even basic authentication. Since a new protocol can only help future vehicles and not older vehicles, our approach tries to solve the issue as a data analytics problem and use machine learning techniques to secure cars. We develop a Hidden Markov Model to detect anomalous states from real data collected from vehicles. Using this model, while a vehicle is in operation, we are able to detect and issue alerts. Our model could be integrated as a plug-n-play device in all new and old cars.
Abstract (translated by Google)
车辆变得越来越紧密，这开辟了更大的攻击面，不仅影响车内乘客，而且影响周围的人。存在这些漏洞是因为现代系统建立在相对不太安全且旧的CAN总线框架上，甚至缺乏基本认证。由于新协议只能帮助未来的车辆而不是旧车辆，我们的方法试图将问题解决为数据分析问题，并使用机器学习技术来保护汽车。我们开发了一种隐马尔可夫模型，用于从车辆收集的实际数据中检测异常状态。使用此模型，在车辆运行时，我们能够检测并发出警报。我们的模型可以作为即插即用设备集成到所有新旧车中。
URL
http://arxiv.org/abs/1512.08048