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Using discriminant analysis to detect intrusions in external communication for self-driving vehicles

Alheeti, KMA and Gruebler, A and McDonald-Maier, K (2017) 'Using discriminant analysis to detect intrusions in external communication for self-driving vehicles.' Digital Communications and Networks, 3 (3). 180 - 187. ISSN 2468-5925

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© 2017 Chongqing University of Posts and Telecommuniocations Security systems are a necessity for the deployment of smart vehicles in our society. Security in vehicular ad hoc networks is crucial to the reliable exchange of information and control data. In this paper, we propose an intelligent Intrusion Detection System (IDS) to protect the external communication of self-driving and semi self-driving vehicles. This technology has the ability to detect Denial of Service (DoS) and black hole attacks on vehicular ad hoc networks (VANETs). The advantage of the proposed IDS over existing security systems is that it detects attacks before they causes significant damage. The intrusion prediction technique is based on Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) which are used to predict attacks based on observed vehicle behavior. We perform simulations using Network Simulator 2 to demonstrate that the IDS achieves a low rate of false alarms and high accuracy in detection.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > T Technology (General)
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 10 Mar 2017 14:46
Last Modified: 04 Feb 2019 12:15

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