Using Data Analytics to Detect Anomalous States in Vehicles

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https://ebiquity.umbc.edu/paper/html/id/723/Using-Data-Analytics-to-Detect-Anomalous-States-in-Vehicles

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http://hdl.handle.net/11603/11808

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UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection
UMBC Student Collection

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Date

2015-12-25

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technical reports
Text

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This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.

Subjects

Data Analytics
Anomalous
Vehicles
machine learning techniques
Hidden Markov Model
UMBC Ebiquity Research Group

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.