Machine Learning-Based Jamming Detection for Safety Applications in Vehicular Networks: Individual Detection?

Abstract

Intelligent transportation system (ITS) refers to advanced applications to make transportation safer and more intelligent. The dynamic and diverse natures of the system have been creating many challenges in ITS deployment and security. The progression in recent years of machine learning provides potentially strong methods to exploit data sources from transportation networks. Machine learning-based approaches promise to deal with various challenges in networks thanks to their ability to adapt to the changing network topology and network scale. This paper investigates the application of machine-learning models in jamming detection by analyzing how the approach works on individual observations of vehicles in different scenarios. We propose a machine learning-based approach that explores hidden rules of how the observation changes under a reactive jamming attack. Our proposed machine learning-based approach improves detection accuracy compared to existing methods. The performance of the proposed method depends closely on dataset selection. Though we evaluate our approach with synthetic data, the data are generated with justifiable simulations calibrated to match a real-referenced dataset. Our analysis provides a connection between the machine-learning domain and vehicular networks by utilizing specific domain techniques for jamming detection.

A Torpor-based Enhanced Security Model for CSMA/CA Protocol in Wireless Networks

“…There are several studies [6 -11] in the literature seeking how to effectively tackle jamming attacks on wireless networks. In recent times, a jamming detection attack [12] was designed based on machine learning algorithms for vehicular networks [13]. Study [12] investigated the use of hidden rules and how it affects the observation changes under a reactive jamming attack while the latter classified the different jamming attacks based on the techniques employed by the attackers [14].…”