Intelligent Intrusion Detection of Grey Hole and Rushing Attacks in Self-Driving Vehicular Networks

Khattab Ali Alheeti,Anna Gruebler,Klaus Mcdonald-Maier

doi:10.3390/computers5030016

Khattab Ali Alheeti, Anna Gruebler + Show 1 more

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https://doi.org/10.3390/computers5030016

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Abstract

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.

Highlights

Vehicular ad hoc networks (VANETs) play a vital role in the growth and the use of self-driving and semi self-driving vehicles [1]
We carried out calculation of four kinds of alarms: false positive (FP), true positive (TP), false negative (FN) and true negative (TN)
We implemented the methodology of this Intrusion Detection System (IDS) in eight phases, which lead to generating the mobility and traffic model, the trace file, the ns-2, fuzzification, data collection and pre-processing, training and testing for the Support Vector Machines (SVM), and training and testing for the Feed Forward Neural Networks (FFNN), and compared the results we generated from the two types of IDS

Summary

Introduction

Vehicular ad hoc networks (VANETs) play a vital role in the growth and the use of self-driving and semi self-driving vehicles [1]. Internal and external communication systems are considered important components in autonomous and semi-autonomous cars. In autonomous and semi-autonomous vehicles, the communication utilises Cooperative Awareness. VANETs [4] are mobile nodes that facilitate communication in a particular zone as well as with RSUs in the absence of a fixed security infrastructure, which is used in conventional networks like wired networks [5]. Many researches consider VANETs a subclass or subtype of Mobile Ad hoc Networks (MANETs) [3]. They directly affect the ITS through the provision of comfort

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