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Cloud-based cyber-physical intrusion detection for vehicles using Deep Learning

Cloud-based cyber-physical intrusion detection for vehicles using Deep Learning

Loukas, George, Vuong, Tuan, Heartfield, Ryan, Sakellari, Georgia ORCID: 0000-0001-7238-8700, Yoon, Yongpil and Gan, Diane ORCID: 0000-0002-0920-7572 (2017) Cloud-based cyber-physical intrusion detection for vehicles using Deep Learning. IEEE Access, 6. pp. 3491-3508. ISSN 2169-3536 (Online) (doi:https://doi.org/10.1109/ACCESS.2017.2782159)

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Abstract

Detection of cyber attacks against vehicles is of growing interest. As vehicles typically afford limited processing resources, proposed solutions are rule-based or lightweight machine learning techniques. We argue that this limitation can be lifted with computational offloading commonly used for resource-constrained mobile devices. The increased processing resources available in this manner allow access to more advanced techniques. Using as case study a small four-wheel robotic land vehicle, we demonstrate the practicality and benefits of offloading the continuous task of intrusion detection that is based on deep learning. This approach achieves high accuracy much more consistently than with standard machine learning techniques and is not limited to a single type of attack or the in-vehicle CAN bus as previous work. As input, it uses data captured in real-time that relate to both cyber and physical processes, which it feeds as time series data to a neural network architecture. We use both a deep multilayer perceptron and a recurrent neural network architecture, with the latter benefitting from a long-short term memory hidden layer, which proves very useful for learning the temporal context of different attacks. We employ denial of service, command injection and malware as examples of cyber attacks that are meaningful for a robotic vehicle. The practicality of the latter depends on the resources afforded onboard and remotely, as well as the reliability of the communication means between them. Using detection latency as the criterion, we have developed a mathematical model to determine when computation offloading is beneficial given parameters related to the operation of the network and the processing demands of the deep learning model. The more reliable the network and the greater the processing demands, the greater the reduction in detection latency achieved through offloading.

Item Type: Article
Uncontrolled Keywords: Cyber-physical security, Vehicles, Intrusion detection, Cyber security, Cloud computing, Computational offloading
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Faculty of Architecture, Computing & Humanities
Faculty of Architecture, Computing & Humanities > Department of Computing & Information Systems
Faculty of Architecture, Computing & Humanities > Internet of Things and Security (ISEC)
Last Modified: 10 Jun 2019 10:13
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: GREAT d
Selected for GREAT 2019: GREAT 1
URI: http://gala.gre.ac.uk/id/eprint/18213

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