Dynamic event classification for intrusion and false alarm detection in vehicular ad hoc networks. (2016)

• Record Type: Journal Article
• Authors: Zamil, Mohammed G.H. Al; Samarah, Samer
• Is Part Of: International journal of information and communication technology. Volume 8:Number 2/3(2016)
• Page Start: 140
• Page End: 164
• Publication Date: 2016
• Subjects: ad hoc data classification -- automatic vehicle location -- data mining -- sensor networks -- intrusion detection -- false alarm detection -- dynamic event classification -- vehicular ad hoc networks -- VANETs -- false alarms; Information technology -- Periodicals; Computer science -- Periodicals; Telecommunication -- Periodicals; 004.05
• Languages: English
• ISSNs: 1466-6642

Abstract

Several classification techniques have been proposed as a basis to build intrusion detection systems for vehicular ad-hoc networks. In this paper, we proposed a dynamic event classification technique to categorise communication messages for the purpose of detecting intrusions and false alarms. The contributions of this research are to: 1) propose an efficient binary classification technique to evaluate the plausibility of communication messages in VANETs based on a set of semantic patterns of actions; 2) apply a mechanism to construct association rules that handle the representation of ad-hoc conditions. The proposed technique relies on defining the classification task as an optimisation problem that maximises true-positives and minimises false-positives. A set of experiments have been performed in order to evaluate the proposed technique using two different datasets. The results indicated that our proposed technique outperformed state-of-the-art classification techniques and efficiently detect intrusions and false alarms.