This project will focus on the security issue facing Industrial Unmanned Aerial Vehicles (UAVs)-based systems.
Aims and objectives
Industrial Unmanned Aerial Vehicles (UAVs) are widely considered as the next breakaway in the ongoing Industry 4.0 revolution, thanks to their high mobility, on-demand deployment, and versatility in various industrial sectors such as manufacturing, transportation, energy, etc. However, unlike traditional industrial networks equipped with wired communications, in industrial UAV-based systems all communication links are wireless and hence UAVs wirelessly communicate to an industrial control system for data and control signal exchanges. Due to the broadcast nature of the wireless channel, such systems are vulnerable to control signal spoofing attacks and information can be easily intercepted by unauthorized receivers, giving rise to a new security challenge. More specifically, unauthorized receivers can forge the control signal and take over the UAV illegitimately, which poses a serious threat to the safety of the UAV systems. Therefore, security is a critical design issue in the implementation and operation of industrial UAVs. Although it is true that traditional encryption techniques can be used to partially address the security issues of industrial UAV networks, the security offered by such techniques can be very limited in scenarios where the eavesdropper has powerful computational capabilities. In this respect, physical layer security (PLS) can safeguard wireless data transmissions without requiring secret keys and complex algorithms, thereby making PLS a more desirable candidate to address the security issues in industrial UAV networks. In light of this, this project aims to investigate the security issue in industrial UAVs-based networks and tackle this challenge by developing and applying advanced PLS and machine learning techniques. Simulation-based modelling, backed up by mathematical analysis, will be undertaken for several practical scenarios. The project will involve theoretical analyses as well as numerical simulation results; therefore, having excellent mathematical and programming skills is necessary.
Specific requirements of the project
Essential: - Strong mathematical, analytical and programming skills, e.g., MATLAB. - Strong background in communication theory and digital signal processing. - IELTS 6.5 (or equivalent) with no element below 6.0.
Desirable: - Basic knowledge of machine learning tools is a plus.
This opportunity is open to UK, EU, and International (overseas) applicants. The project is offered on a self-funded basis.