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.
Highlights
Vehicular ad hoc networks (VANETs) are adopted to significantly reduce traffic accidents, enhance road safety and traffic congestion, and to improve the driving experience
Most of the cyber-security systems show good results in detecting attacks, they are struggling to avoid the modification of safety messages by malicious nodes in VANET
P24lease give a shorter version with: \authorrunning and \titlerunning prior to \maketitle minimize the ratio of modified packets but, at the same time, does not wish the nodes to feel too much restricted in communication
Summary
VANETs are adopted to significantly reduce traffic accidents, enhance road safety and traffic congestion, and to improve the driving experience. 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[12]. – The eavesdropper vehicle may modify, or reroute some packets Motivated by these observations, this paper proposes a new Intrusion Detection System (IDS) using Dempster–Shafer theory and Bayesian classifier to prevent possible future security attacks in VANET. – The intrusion detection scheme is a hybrid-based monitoring solution that uses Dempster-Shafer’s framework for combining the evidences from multiple sources of information and Bayesian classifier for events classification.Yet, researchers neglected exploring sufficiently well how to combine this information into one comprehensive security system for modeling intrusion detection in the domain of VANETs. The main concern of IDS is handling the imprecise, fuzzy, ambiguous, inconsistent, and even incomplete information about nodes.
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