@conference {263,
	title = {Automating ECU Identification for Vehicle Security},
	booktitle = {2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)},
	year = {2016},
	month = {Dec},
	abstract = {<p>The field of vehicular cybersecurity has received considerable media and research attention in the past few years. Given the increasingly connected aspect of consumer automobiles, along with the inherent danger of these machines, there has been a call for experienced security researchers to contribute towards the vehicle security domain. The proprietary nature of Controller Area Network (CAN) bus messages, however, creates a barrier of entry for those unfamiliar, due to the need to identify what the messages on a given vehicle{\textquoteright}s bus are broadcasting. This work aims to automate the process of correlating CAN bus messages with specific Electronic Control Unit (ECU) functions in a new vehicle, by creating a machine learning classifier that has been trained on a dataset of multiple vehicles from different manufacturers. The results show that accurate classification is possible, and that some ECUs that broadcast similar vehicle dynamics broadcast similar CAN messages.</p>
},
	keywords = {Acceleration, automobiles, automotive electronics, automotive security, Brakes, broadcasting, CAN bus, CAN bus messages, classification, consumer automobiles, control engineering computing, controller area network, controller area networks, ECU identification automation, electronic control unit, electronic engineering computing, embedded systems, learning (artificial intelligence), machine learning, machine learning classifier, pattern classification, Protocols, Security, security of data, Training, vehicle bus, vehicular ad hoc networks, vehicular cybersecurity, Wheels},
	doi = {10.1109/ICMLA.2016.0111},
	author = {Jaynes, Michael and Ram Dantu and Varriale, Roland and Evans, Nathaniel}
}
@conference {92,
	title = {Tire-road friction estimation utilizing smartphones},
	booktitle = {Information Reuse and Integration (IRI), 2014 IEEE 15th International Conference on},
	year = {2014},
	month = {08/2014},
	abstract = {<p>Tire-road friction is an important parameter for a number of different safety features present in modern-day vehicles, and the knowledge of this friction may also prove useful to the driver of a vehicle while it is in motion. In particular, this information may help inform a driver of dangerous low-traction situations that he or she may need to be aware of. Furthermore, since a growing number of drivers have access to Bluetooth-enabled smartphones, it is worth exploring how these devices may be leveraged in concert with vehicular CAN-bus networks to provide valuable safety information.</p>
},
	keywords = {Acceleration, Bluetooth, bluetooth-enabled smart phones, CAN-bus, controller area networks, dangerous low-traction situations, driver information systems, Estimation, friction, graphical user interfaces, mobile computing, road safety, road vehicles, Roads, Safety, safety information, smart phones, tire-road friction estimation, traction, tyres, vehicle safety features, Vehicles, vehicular CAN-bus networks, vehicular safety, Wheels},
	doi = {10.1109/IRI.2014.7051980},
	author = {Jaynes, Michael and Ram Dantu}
}
