Research Repository

An Intrusion Detection System against Black Hole Attacks on the Communication Network of Self-Driving Cars

Alheeti, KMA and Gruebler, A and McDonald-Maier, KD (2016) An Intrusion Detection System against Black Hole Attacks on the Communication Network of Self-Driving Cars. In: UNSPECIFIED, ? - ?.

Full text not available from this repository.

Abstract

© 2015 IEEE. The emergence of self-driving and semi self-driving vehicles which form vehicular ad hoc networks (VANETs) has attracted much interest in recent years. However, VANETs have some characteristics that make them more vulnerable to potential attacks when compared to other networks such as wired networks. The characteristics of VANETs are: an open medium, no traditional security infrastructure, high mobility and dynamic topology. In this paper, we build an intelligent intrusion detection system (IDS) for VANETs that uses a Proportional Overlapping Scores (POS) method to reduce the number of features that are extracted from the trace file of VANET behavior and used for classification. These are relevant features that describe the normal or abnormal behavior of vehicles. The IDS uses Artificial Neural Networks (ANNs) and fuzzified data to detect black hole attacks. The IDSs use the features extracted from the trace file as auditable data to detect the attack. In this paper, we propose hybrid detection (misuse and anomaly) to detect black holes.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: Proceedings - 2015 6th International Conference on Emerging Security Technologies, EST 2015
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 13 Dec 2016 21:01
Last Modified: 17 Aug 2017 17:20
URI: http://repository.essex.ac.uk/id/eprint/18509

Actions (login required)

View Item View Item