Abstract
Intelligent transportation system (ITS) is a promising technology to enhance
driving safety and efficiency within smart cities. It involves public transportation
management, infrastructure control and road safety. Its main purpose is to avoid risks
and accidents, reduce traffic congestion and ensure safety for road users. Vehicular ad
hoc networks (VANET) are core components of ITS where wireless communications
between vehicles, as well as between vehicles and infrastructure, are possible to allow
exchanging road, traffic or infotainment information. VANET is vulnerable to several
security attacks that may compromise the driver’s safety.
Using misbehavior detection approaches and information analysis demonstrated
promising results in securing VANET. In this context, Machine Learning techniques
proved their efficiency in detecting attacks and misbehavior, especially zero-day
attacks.
The goal of this chapter is twofold. First, we intend to analyze the security issue in
VANET by reviewing the most important vulnerabilities and proposed
countermeasures. In the second part, we introduce a comprehensive Machine Learning
framework to design a VANET IDS. We used the framework to evaluate the
performances of several Machine Learning techniques to detect position attacks using
the VeReMi security dataset. Experimental results prove that KNN, Decision Tree and
Random Forest outperform Logistic Regression, SVM and Gaussian Naïve Bayes in
terms of Accuracy, F-measure, Precision and Recall.