Elham Mohammadzadeh Mianji

Vehicular networks have a high potential in the creation of smarter cities, but also smarter roads. This potential relies on the on the wheels connectivity provided by vehicular networks that can also meet the always connected need of drivers and passengers as they are spending much of their daily time in their vehicles. Moreover, the vehicular network is considered to have a crucial role in the context of self-driving vehicles. Vehicular networks are based on “smart” vehicles that are able to communicate to each other and to the infrastructure via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, known under the generic term of V2X communications, but also via other wireless communications technologies.

Security is an important concern in vehicular networks as malicious attacks can lead not only to material losses, or reputation loss, etc., but can actually lead to the loss of human lives. Vehicular networks have specific characteristics that pose challenges to security, such as high mobility, rapidly changing topology, large scale, heterogeneous environment, etc. The implementation of security mechanisms in vehicular networks usually introduce an overhead in communication. In particular, the proactive mechanisms lead to high signalling and communication cost. Hence, the state of the art is represented by reactive detection in order to balance the quality of service and security. Machine learning (ML) algorithms have been used successfully in network security in general, and vehicular networks in particular. In the latter case, they demonstrated their potential of dealing with the challenges imposed by the aforementioned unique characteristics of vehicular networks.

This project’s main research objectives are listed below, but the ultimate goal of the project is to propose proactive security methods for vehicular networks.

RO1 – comprehensive literature review in the area of ML-based security mechanisms for vehicular networks.

RO2 – propose proactive security methods for vehicular networks that are based on ML. Blockchain will be also investigated as it is showing promises in this area.