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Uncertainty-aware defence mechanisms against persistent aerial threats


   School of Electronics, Electrical Engineering and Computer Science

  Dr Vishal Sharma,  Monday, February 28, 2022  Funded PhD Project (UK Students Only)

Belfast United Kingdom Electrical Engineering Electronic Engineering Networks Computer Science

About the Project

UK’s drone industry would impact £42bn on the economy with an expected GDP growth of 2% by 2030. It is a vast market to explore, and this PhD project aims to fill the potential gap of security and threats associated with aerial vehicles.

Uncertainty is one of the primary role-players in dealing with rogue aerial vehicles. Rogue UAVs are adversaries in the system that aim to break the defence mechanism leading to the entire breakdown of the aerial networks. In an environment prone to persistent threats, such as aerial networks, the state of the systems can be ambiguous and yield high confusion in identifying the potential hazards. Uncertainty-awareness enables a model to intelligently decide on the system’s state resulting from a corresponding correction to any previous error. A model needs to optimise a decision when identifying rogue UAVs by balancing situations and factors of uncertainty. Having a reactive defence mechanism to analyse the status of the aerial network may provide a relatively clear idea of the workflow rather than going with a proactive system as an alteration in the UAVs’ operation can be a response to uncertain parameters, such as reconfiguration, topology change, battery-drainage, or addition of new UAVs. However, this is a speculative argument and needs a more thorough investigation.

Project Description:

In this PhD project, we aim to define the principles of uncertainty amongst UAVs using logical reasoning and understand the impact on the detection of rogue UAVs. We plan to form defence mechanisms against threats that evolve continuously and also understand the evolution of potential hazards in the aerial networks and how the defence mechanisms perceive these. We aim to identify the points of threat exploration subject to proactive and reactive defence methods and list the best possible strategy with their impact study and conditions of operations for aerial networks. This research will help attain expertise in aerial networks and prepare the scholar for the astronomically vast drone market with the advantage of learning, exploring and building solutions with AI and cybersecurity.

Project Key Words: Aerial networks, Security, Rogue-UAVs, Uncertainty, defence mechanisms, situational-awareness

Start Date: 01/10/22

Application Closing date: 28/02/22

For further information about eligibility criteria please refer to the DfE Postgraduate Studentship Terms and Conditions 2021-22 at https://go.qub.ac.uk/dfeterms

Applicants should apply electronically through the Queen’s online application portal at: https://dap.qub.ac.uk/portal/

Academic Requirements:

A minimum 2.1 honours degree or equivalent in Computer Science or Electrical and Electronic Engineering or relevant degree is required.


Funding Notes

This three year studentship, for full-time PhD study, is potentially funded by the Department for the Economy (DfE) and commences on 1 October 2022. For UK domiciled students the value of an award includes the cost of approved tuition fees as well as maintenance support (Fees £4,500 pa and Stipend rate £15,609 pa - 2022-23 rates to be confirmed). To be considered eligible for a full DfE studentship award you must have been ordinarily resident in the United Kingdom for the full three year period before the first day of the first academic year of the course.

References

[1] PWC (2018). The impact of drones on the UK economy, Skies without limits: Drones – taking the UK’s economy to new heights, https://www.pwc.co.uk/issues/intelligent-digital/the-impact-of-drones-on-the-uk-economy.html, [last accessed on December 9, 2021].
[2] Wan, Z., Cho, J. H., Zhu, M., Anwar, A. H., Kamhoua, C., & Singh, M. P. (2021). Foureye: Defensive Deception based on Hypergame Theory Against Advanced Persistent Threats. arXiv preprint arXiv:2101.02863.
[3] Liu, D., Jaramillo, M., & Vincenzi, D. (2015). The effects of system reliability and task uncertainty on autonomous unmanned aerial vehicle operator performance under high time pressure. Human Factors and Ergonomics in Manufacturing & Service Industries, 25(5), 515-522.
[4] Sharma, V., Choudhary, G., Ko, Y., & You, I. (2018). Behavior and vulnerability assessment of drones-enabled industrial internet of things (IIoT). IEEE Access, 6, 43368-43383.
[5] Mallmann-Trenn, F., Cavorsi, M., & Gil, S. (2021). Crowd Vetting: Rejecting Adversaries via Collaboration with Application to Multirobot Flocking. IEEE Transactions on Robotics, doi: 10.1109/TRO.2021.3089033 (Early Access).

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