Title: Dynamic event classification for intrusion and false alarm detection in vehicular ad hoc networks

Authors: Mohammed G.H. Al Zamil; Samer Samarah

Addresses: Department of Computer Information Systems, Yarmouk University, Irbed, Jordan ' Department of Computer Information Systems, Yarmouk University, Irbed, Jordan

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.

Keywords: 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.

DOI: 10.1504/IJICT.2016.074840

International Journal of Information and Communication Technology, 2016 Vol.8 No.2/3, pp.140 - 164

Received: 27 Nov 2013
Accepted: 05 May 2014
Published online: 21 Feb 2016 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article