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Performance evaluation of cyber-physical intrusion detection on a robotic vehicle

Performance evaluation of cyber-physical intrusion detection on a robotic vehicle

Vuong, Tuan Phan, Loukas, George and Gan, Diane ORCID: 0000-0002-0920-7572 (2015) Performance evaluation of cyber-physical intrusion detection on a robotic vehicle. In: 13th International Conference on Pervasive Intelligence and Computing (IEEE-PICOM 2015). IEEE, Piscataway, NJ, US, pp. 2106-2113. ISBN 9781509001538 (doi:https://doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.313)

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Abstract

Intrusion detection systems designed for con- ventional computer systems and networks are not necessarily suitable for mobile cyber-physical systems, such as robots, drones and automobiles. They tend to be geared towards attacks of different nature and do not take into account mobility, energy consumption and other physical aspects that are vital to a mobile cyber-physical system. We have developed a decision tree-based method for detecting cyber attacks on a small-scale robotic vehicle using both cyber and physical features that can be measured by its on-board systems and processes. We evaluate it experimentally against a variety of scenarios involving denial of service, command injection and two types of malware attacks. We observe that the addition of physical features noticeably improves the detection accuracy for two of the four attack types and reduces the detection latency for all four.

Item Type: Conference Proceedings
Title of Proceedings: 13th International Conference on Pervasive Intelligence and Computing (IEEE-PICOM 2015)
Additional Information: CIT/IUCC/DASC/PICOM 2015, 26-28 October 2015, Liverpool, UK.
Uncontrolled Keywords: Cyber security, Cyber-physical security, Robotics, Vehicle security, Intrusion detection
Faculty / Department / Research Group: Faculty of Architecture, Computing & Humanities
Faculty of Architecture, Computing & Humanities > Department of Computing & Information Systems
Last Modified: 19 May 2019 17:06
Selected for GREAT 2016: None
Selected for GREAT 2017: GREAT b
Selected for GREAT 2018: None
Selected for GREAT 2019: GREAT 1
URI: http://gala.gre.ac.uk/id/eprint/15014

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