Machine Learning-Driven Optimization for Intrusion Detection in Smart Vehicular Networks
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Machine Learning‑Driven Optimization for Intrusion Detection in Smart Vehicular Networks Ayoub Alsarhan1 · Abdel‑Rahman Al‑Ghuwairi2 · Islam T. Almalkawi3 · Mohammad Alauthman4 · Ahmed Al‑Dubai5
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract An essential element in the smart city vision is providing safe and secure journeys via intelligent vehicles and smart roads. Vehicular ad hoc networks (VANETs) have played a significant role in enhancing road safety where vehicles can share road information conditions. However, VANETs share the same security concerns of legacy ad hoc networks. Unlike exiting works, we consider, in this paper, detection a common attack where nodes modify safety message or drop them. Unfortunately, detecting such a type of intrusion is a challenging problem since some packets may be lost or dropped in normal VANET due to congestion without malicious action. To mitigate these concerns, this paper presents a novel scheme for minimizing the invalidity ratio of VANET packets transmissions. In order to detect unusual traffic, the proposed scheme combines evidences from current as well as past behaviour to evaluate the trustworthiness of both data and nodes. A new intrusion detection scheme is accomplished through a four phases, namely, rule-based security filter, Dempster–Shafer adder, node’s history database, and Bayesian learner. The suspicion level of each incoming data is determined based on the extent of its deviation from data reported from trustworthy nodes. Dempster–Shafer’s theory is used to combine multiple evidences and Bayesian learner is adopted to classify each event in VANET into well-behaved or misbehaving event. The proposed solution is validated through extensive simulations. The results confirm that the fusion of different evidences has a significant positive impact on the performance of the security scheme compared to other counterparts. Keywords Intrusion detection · Smart city · Malicious nodes · Security · Misbehavior detection
1 Introduction Recently, VANETs are adopted to significantly reduce traffic accidents, enhance road safety and traffic congestion, and to improve the driving experience. In order to increase drivers’ awareness, smart vehicles cooperate to relay safety messages and road condition to other * Ayoub Alsarhan [email protected] Extended author information available on the last page of the article
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vehicles and roadside units (RSU) [2, 5, 6, 11, 16, 19]. However, VANETs have security concerns since information is transmitted via open space environment without any central support. In this environment, malicious node can join a network at any time and inject false messages wirelessly [11]. Unsecured nodes can expect to maliciously manipulating the stream of packets. For enhancing safety, malicious nodes should be prevented from changing safety messages. In VANET, source and destination nodes which are not in the same range relay on intermediate nodes for forwarding messages to the f
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