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Improving SIEM for Critical SCADA Water Infrastructures Using Machine Learning Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Comparative Study of Machine Learning Methods for In-Vehicle Intrusion Detection

Authors : Ivo Berger, Roland Rieke, Maxim Kolomeets, Andrey Chechulin, Igor Kotenko

Published in: Computer Security

Publisher: Springer International Publishing

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Abstract

An increasing amount of cyber-physical systems within modern cars, such as sensors, actuators, and their electronic control units are connected by in-vehicle networks and these in turn are connected to the evolving Internet of vehicles in order to provide “smart” features such as automatic driving assistance. The controller area network bus is commonly used to exchange data between different components of the vehicle, including safety critical systems as well as infotainment. As every connected controller broadcasts its data on this bus it is very susceptible to intrusion attacks which are enabled by the high interconnectivity and can be executed remotely using the Internet connection. This paper aims to evaluate relatively simple machine learning methods as well as deep learning methods and develop adaptations to the automotive domain in order to determine the validity of the observed data stream and identify potential security threats.

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Metadata
Title
Comparative Study of Machine Learning Methods for In-Vehicle Intrusion Detection
Authors
Ivo Berger
Roland Rieke
Maxim Kolomeets
Andrey Chechulin
Igor Kotenko
Copyright Year
2019
Book
Computer Security

Print ISBN: 978-3-030-12785-5

Electronic ISBN: 978-3-030-12786-2

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-12786-2_6

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