Enhancing Security and Privacy in FANETs Using Federated Learning

Summary

Flying Ad Hoc Networks (FANETs), a subset of Mobile Ad Hoc Networks (MANETs), consist of Unmanned Aerial Vehicles (UAVs) that work together in a decentralised and cooperative manner to perform a wide range of tasks, including environmental monitoring, search and rescue operations, disaster management, and military surveillance. FANETs are characterised by their high mobility, dynamic topology, and frequent changes in network structure, making them more flexible and scalable than traditional networks. However, these very features also introduce significant challenges in terms of communication, resource management, and, crucially, security and privacy.

The distributed nature of FANETs, combined with the sensitive data often collected by UAVs, creates a fertile ground for various security vulnerabilities, such as eavesdropping, data interception, spoofing, and malicious node attacks. Traditional centralised security mechanisms are inadequate for FANETs due to the high mobility of UAVs and the lack of a fixed infrastructure. Therefore, novel approaches are required to ensure both the privacy of data and the security of the network itself.

Federated learning (FL), a decentralised machine learning paradigm, presents a promising solution to these challenges. In contrast to conventional machine learning, where data is aggregated on a central server, FL allows each UAV in the network to locally train models on its own data. The learning process is coordinated, but the data itself is never shared, thereby enhancing privacy. This research proposes to explore and develop a federated learning approach specifically tailored for FANETs, with the objective of providing a robust, scalable, and secure framework that protects both the network and its sensitive data from potential threats while maintaining efficient communication and collaboration among UAVs.

The School of Computing at Ulster University holds Athena Swan Bronze Award since 2016 and is committed to promote and advance gender equality in Higher Education. We particularly welcome female applicants, as they are under-represented within the School.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

• Sound understanding of subject area as evidenced by a comprehensive research proposal
• A demonstrable interest in the research area associated with the studentship

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

• First Class Honours (1st) Degree
• Masters at 70%
• For VCRS Awards, Masters at 75%
• Experience using research methods or other approaches relevant to the subject domain
• Work experience relevant to the proposed project
• Publications - peer-reviewed
• Experience of presentation of research findings

Equal Opportunities

The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities.

Appointment will be made on merit.

Funding and eligibility

This project is funded by:

• Department for the Economy (DfE)
• Vice Chancellor's Research Scholarship (VCRS)

Our fully funded PhD scholarships will cover tuition fees and provide a maintenance allowance of £19,237 (tbc) per annum for three years (subject to satisfactory academic performance).  A Research Training Support Grant (RTSG) of £900 per annum is also available.

These scholarships, funded via the Department for the Economy (DfE) and the Vice Chancellor’s Research Scholarships (VCRS), are open to applicants worldwide, regardless of residency or domicile.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies.