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Computers 2016, 5(3), 16; doi:10.3390/computers5030016

Intelligent Intrusion Detection of Grey Hole and Rushing Attacks in Self-Driving Vehicular Networks

1
Embedded and Intelligent Systems Research Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK
2
College of Computer Science and Information Technology, University of Anbar, Anbar 31001, Iraq
*
Author to whom correspondence should be addressed.
Academic Editor: Thomas Strang
Received: 27 May 2016 / Revised: 13 July 2016 / Accepted: 14 July 2016 / Published: 22 July 2016
View Full-Text   |   Download PDF [2998 KB, uploaded 25 July 2016]   |  

Abstract

Vehicular ad hoc networks (VANETs) play a vital role in the success of self-driving and semi self-driving vehicles, where they improve safety and comfort. Such vehicles depend heavily on external communication with the surrounding environment via data control and Cooperative Awareness Messages (CAMs) exchanges. VANETs are potentially exposed to a number of attacks, such as grey hole, black hole, wormhole and rushing attacks. This work presents an intelligent Intrusion Detection System (IDS) that relies on anomaly detection to protect the external communication system from grey hole and rushing attacks. These attacks aim to disrupt the transmission between vehicles and roadside units. The IDS uses features obtained from a trace file generated in a network simulator and consists of a feed-forward neural network and a support vector machine. Additionally, the paper studies the use of a novel systematic response, employed to protect the vehicle when it encounters malicious behaviour. Our simulations of the proposed detection system show that the proposed schemes possess outstanding detection rates with a reduction in false alarms. This safe mode response system has been evaluated using four performance metrics, namely, received packets, packet delivery ratio, dropped packets and the average end to end delay, under both normal and abnormal conditions. View Full-Text
Keywords: security; vehicular ad hoc networks; intrusion detection system; self-driving car; semi self-driving car security; vehicular ad hoc networks; intrusion detection system; self-driving car; semi self-driving car
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Ali Alheeti, K.M.; Gruebler, A.; McDonald-Maier, K. Intelligent Intrusion Detection of Grey Hole and Rushing Attacks in Self-Driving Vehicular Networks. Computers 2016, 5, 16.

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