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Intelligent intrusion detection in external communication systems for autonomous vehicles

Ali Alheeti, KM and McDonald-Maier, K (2018) 'Intelligent intrusion detection in external communication systems for autonomous vehicles.' Systems Science & Control Engineering, 6 (1). 48 - 56. ISSN 2164-2583

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Abstract

Self-driving vehicles are known to be vulnerable to different types of attacks due to the type of communication systems which are utilized in these vehicles. These vehicles are becoming more reliant on external communication through vehicular ad hoc networks. However, these networks contribute new threats to self-driving vehicles which lead to potentially significant problems in autonomous systems. These communication systems potentially open self-driving vehicles to malicious attacks like the common Sybil attacks, black hole, Denial of Service, wormhole attacks and grey hole attacks. In this paper, an intelligent protection mechanism is proposed, which was created to secure external communications for self-driving and semi-autonomous cars. The protection mechanism is based on the Proportional Overlapping Scores method, which allows to decrease the number of features found in the Kyoto benchmark dataset. This hybrid detection system uses Back Propagation neural networks to detect Denial of Service (DoS), a common type of attack in vehicular ad hoc networks. The results from our experiment revealed that the proposed intrusion detection has the ability to identify malicious vehicles in self-driving and even in semi-autonomous vehicles.

Item Type: Article
Uncontrolled Keywords: Artificial neural networks, intrusion detection system, security, vehicular ad hoc networks, driverless vehicles, semi-autonomous vehicles
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TL Motor vehicles. Aeronautics. Astronautics
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 15 May 2018 14:20
Last Modified: 15 May 2018 14:20
URI: http://repository.essex.ac.uk/id/eprint/22020

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