
@conference {YouSvo20A,
	title = {Towards Reverse Engineering Controller Area Network Messages Using Machine Learning},
	booktitle = {Proceedings of the IEEE World Forum on Internet of Things (WF-IoT)},
	year = {2020},
	month = {April},
	abstract = {The automotive Controller Area Network (CAN) allows Electronic Control Units (ECUs) to communicate with each other and control various vehicular functions such as engine and braking control. Consequently CAN and ECUs are high priority targets for hackers. As CAN implementation details are held as proprietary information by vehicle manufacturers, it can be challenging to decode and correlate CAN messages to specific vehicle operations. To understand the precise meanings of CAN messages, reverse engineering techniques that are time-consuming, manually intensive, and require a physical vehicle are typically used. This work aims to address the process of reverse engineering CAN messages for their functionality by creating a machine learning classifier that analyzes messages and determines their relationship to other messages and vehicular functions. Our work examines CAN traffic of different vehicles and standards to show that it can be applied to a wide arrangement of vehicles. The results show that the function of CAN messages can be determined without the need to manually reverse engineer a physical vehicle.  },
	author = {Clinton Young and Jordan Svoboda and Joseph Zambreno}
}
@conference {YouZam16A,
	title = {Towards a Fail-Operational Intrusion Detection System for In-Vehicle Networks},
	booktitle = {Proceedings of the Workshop on Security and Dependability of Critical Embedded Real-Time Systems (CERTS)},
	year = {2016},
	month = {November},
	abstract = {The landscape of automotive in-vehicle networks is changing driven by the vast options for infotainment features and progress toward fully-autonomous vehicles. However, the security of automotive networks is lagging behind feature-driven technologies, and new vulnerabilities are constantly being discovered. In this paper, we introduce a road map towards a security solution for in-vehicle networks that can detect anomalous and failed states of the network and adaptively respond in real-time to maintain a fail-operational system.},
	author = {Clinton Young and Joseph Zambreno and Gedare Bloom}
}
