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

Author:

Nguyen-Minh Huong12ORCID,Tran Hoang Tung2,Pham Thanh Giang3

Affiliation:

1. Graduate University of Science and Technology (GUST), Vietnam Academy of Science and Technology, Hanoi, Vietnam

2. University of Science and Technology of Hanoi (USTH), Vietnam Academy of Science and Technology, Hanoi, Vietnam

3. Institute of Information Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam

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.

Funder

Graduate University of Science and Technology, Vietnam Academy of Science and Technology

Publisher

Hindawi Limited

Subject

Computer Networks and Communications,Information Systems

Reference37 articles.

1. Software-defined heterogeneous vehicular networking: the architectural design and open challenges;A. Mahmood;Future Internet,2019

2. Evolutionary v2x technologies toward the internet of vehicles: challenges and opportunities;H. Zhou;Proceedings of the IEEE,2020

3. Dsrc implementation guide;S. International;A Guide of Users of Sae J2735 Message Sets over Dsrc,2010

4. Architecture, Protocols, and Security in IoV: Taxonomy, Analysis, Challenges, and Solutions

5. When trust meets the internet of vehicles: opportunities, challenges, and future prospects;A. Mahmood

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