Reverse Engineering Controller Area Network Messages Using Unsupervised Machine Learning
Reverse Engineering Controller Area Network Messages Using Unsupervised Machine Learning
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DOI：
10.1109/mce.2020.3023538
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发表时间：
2022-01-01
影响因子：
4.5
通讯作者：
Bloom, Gedare
中科院分区：
文献类型：
--
作者：
Ezeobi, Uchenna;Olufowobi, Habeeb;Bloom, Gedare
智能城市的前景充满了移动性和经济优化的机会，但也提出了许多安全问题，横跨智能生态系统中的一系列组件和系统。这种生态系统的一个关键推动因素是智能交通和运输，其基础是建立在互联车辆的基础上。确保车辆安全虽然是保障乘客和行人安全所必需的，但由于现代汽车系统的广泛攻击面，确保车辆安全本身是具有挑战性的。一辆汽车包含数十到数百个称为电子控制单元(ECU)的小型嵌入式计算设备，它们执行数亿行代码；这种紧密集成的网络物理系统(CPS)固有的复杂性是阻碍有效安全的关键问题之一。我们描述了一种方法，通过利用无监督机器学习来学习在ECU之间传递的消息簇，这些消息与车辆在世界各地移动时CPS状态的变化相关，从而帮助降低安全分析的复杂性。我们的方法可以帮助提高智能城市中车辆的安全性，并可以利用智能城市基础设施来进一步丰富和细化机器学习输出的质量。
The smart city landscape is rife with opportunities for mobility and economic optimization, but also presents many security concerns spanning the range of components and systems in the smart ecosystem. One key enabler for this ecosystem is smart transportation and transit, which is foundationally built upon connected vehicles. Ensuring vehicular security, while necessary to guarantee passenger and pedestrian safety, is itself challenging due to the broad attack surfaces of modern automotive systems. A single car contains dozens to hundreds of small embedded computing devices known as electronic control units (ECUs) executing hundreds of millions of lines of code; the inherent complexity of this tightly integrated cyber-physical system (CPS) is one of the key problems that frustrate effective security. We describe an approach to help reduce the complexity of security analyses by leveraging unsupervised machine learning to learn clusters of messages passed between ECUs that correlate with changes in the CPS state of a vehicle as it moves throughout the world. Our approach can help to improve the security of vehicles in a smart city, and can leverage smart city infrastructure to further enrich and refine the quality of the machine learning output.