
@article {EzeOlu20A,
	title = {Reverse Engineering Controller Area Network Messages using Unsupervised Machine Learning},
	journal = {IEEE Consumer Electronics Magazine},
	volume = {11},
	year = {2022},
	month = {January},
	abstract = {<p>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 100s of millions of lines of code; the inherent complexity of this tightly-integrated cyber-physical system (CPS) is one of the key problems that frustrates 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 through 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.</p>
},
	author = {Uchenna Ezeobi and Habeeb Olufowobi and Clinton Young and Joseph Zambreno and Gedare Bloom}
}
@conference {OluEze19A,
	title = {Anomaly Detection Approach Using Adaptive Cumulative Sum Algorithm for Controller Area Network},
	booktitle = {Proceedings of the ACM Workshop on Automotive Cybersecurity (AutoSec)},
	year = {2019},
	month = {March},
	abstract = {The modern vehicle has transformed from a purely mechanical system to a system that embeds several electronic devices. These devices communicate through the in-vehicle network for enhanced safety and comfort but are vulnerable to cyber-physical risks and attacks. A well-known technique of detecting these attacks and unusual events is by using intrusion detection systems. Anomalies in the network occur at unknown points and produce abrupt changes in the statistical features of the message stream. In this paper, we propose an anomaly-based intrusion detection approach using the cumulative sum (CUSUM) change-point detection algorithm to detect data injection attacks on the controller area network (CAN) bus. We leverage the parameters required for the change-point algorithm to reduce false alarm rate and detection delay. Using real dataset generated from a car in normal operation, we evaluate our detection approach on three different kinds of attack scenarios.},
	author = {Habeeb Olufowobi and Uchenna Ezeobi and Eric Muhati and Gaylon Robinson and Clinton Young and Joseph Zambreno and Gedare Bloom}
}
