Intelligent Intrusion Detection of Grey Hole and Rushing Attacks in Self-Driving Vehicular Networks
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...
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doaj-2d7e688b5f2a411f9acbac351e32b4272020-11-25T01:07:46ZengMDPI AGComputers2073-431X2016-07-01531610.3390/computers5030016computers5030016Intelligent Intrusion Detection of Grey Hole and Rushing Attacks in Self-Driving Vehicular NetworksKhattab M. Ali Alheeti0Anna Gruebler1Klaus McDonald-Maier2Embedded and Intelligent Systems Research Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UKEmbedded and Intelligent Systems Research Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UKEmbedded and Intelligent Systems Research Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UKVehicular 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.http://www.mdpi.com/2073-431X/5/3/16securityvehicular ad hoc networksintrusion detection systemself-driving carsemi self-driving car |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Khattab M. Ali Alheeti Anna Gruebler Klaus McDonald-Maier |
spellingShingle |
Khattab M. Ali Alheeti Anna Gruebler Klaus McDonald-Maier Intelligent Intrusion Detection of Grey Hole and Rushing Attacks in Self-Driving Vehicular Networks Computers security vehicular ad hoc networks intrusion detection system self-driving car semi self-driving car |
author_facet |
Khattab M. Ali Alheeti Anna Gruebler Klaus McDonald-Maier |
author_sort |
Khattab M. Ali Alheeti |
title |
Intelligent Intrusion Detection of Grey Hole and Rushing Attacks in Self-Driving Vehicular Networks |
title_short |
Intelligent Intrusion Detection of Grey Hole and Rushing Attacks in Self-Driving Vehicular Networks |
title_full |
Intelligent Intrusion Detection of Grey Hole and Rushing Attacks in Self-Driving Vehicular Networks |
title_fullStr |
Intelligent Intrusion Detection of Grey Hole and Rushing Attacks in Self-Driving Vehicular Networks |
title_full_unstemmed |
Intelligent Intrusion Detection of Grey Hole and Rushing Attacks in Self-Driving Vehicular Networks |
title_sort |
intelligent intrusion detection of grey hole and rushing attacks in self-driving vehicular networks |
publisher |
MDPI AG |
series |
Computers |
issn |
2073-431X |
publishDate |
2016-07-01 |
description |
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. |
topic |
security vehicular ad hoc networks intrusion detection system self-driving car semi self-driving car |
url |
http://www.mdpi.com/2073-431X/5/3/16 |
work_keys_str_mv |
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