Cyber security analytics in telecommunications systems: Managing security and service in complex real-time 5G networks


An opportunity to apply for a full-time PhD in the Faculty of the Environment and Technology. The studentship will be funded/part-funded by the Faculty of Environment and Technology: Ref 2022-JAN-FET04.

The expected start date of this studentship is January 2022.

The closing date for applications is 14 October 2021.

About the studentship

Our digital society is dependent on complex dynamic telecommunications systems that support our activities and interactions, with increasingly more devices being connected and communicating each day. Managing these systems is a challenging task with time critical needs to provide real-time functionality to services – including healthcare, transport, finance, socialising, and other forms of interaction. Analysts need to ensure that networks are functional, from physical layers through to networking and application layers. At the same time, analysts need to identify and mitigate against threats which can materialise as denial-of-service attacks, targeted sabotage of users, or leaks of confidentiality.

In recent years, Machine Learning techniques have been applied to manage service level provisions, and yet security threats continue to challenge this domain. A major challenge in this domain is to establish cyber situational awareness, in terms of the current landscape and the anticipated future events, and how to effectively integrate human-machine collaboration to best utilise machine learning approaches whilst enabling analysts to best home in on contextual aspects of security threats, all the while doing so in a real-time manner that causes little or no disruption to the end-user service.

This PhD research will explore the current trends of machine learning and communication networks, recognising the role of Machine Learning as an enabler of cyber security but also as introducing another potential attack vector. In this manner, analysts need to determine how best to collaborate with the system, to identify what should be automated, what should be human-assisted, and what should be human-led investigation. Fundamentally, the research question that this project will seek to resolve is how best to inform real-time decision making in complex communication networks when potential attack vectors are observed such that service and security are both appropriately maintained.

The successful candidate will work closely with our industry partner, Ribbon Communications Ltd. The research will address real-world challenges as recognised in practice by our partners. In turn, the successful candidate will link between academia and industry to deliver real-world impact.

For an informal discussion about the studentship, please email Associate Professor Phil Legg (


The studentship is available from January 2022 for a period of three years, subject to satisfactory progress and includes a tax exempt stipend, which is currently £15,609 per annum.

In addition, full-time tuition fees will be covered for up to three years.


Applicants must have a good first degree or, ideally, a Masters. The successful candidate will be expected to demonstrate excellent programming expertise and will have a good practical understanding of machine learning concept, data analytics, and cyber security for networking diagnostics. Students from under-represented groups are particularly encouraged to apply. The studentship is available both for UK and overseas applicants.

A recognised English language qualification is required.

How to apply

Please submit your application online. When prompted, use the reference number 2022-JAN-FET04.

Applicants have the opportunity to discuss their studentship with Directors of Studies via a webinar to take place on Wednesday 8 September 2021 from 10:00 to 12:00 BST. Please use the registration form to reserve your place at this webinar.

Supporting documentation: You will need to upload your research proposal, all your degree certificates and transcripts and your proof of English language proficiency as attachments to your application, so please have these available when you complete the application form. 

References: You will need to provide details of two referees as part of your application. At least one referee must be an academic referee from the institution that conferred your highest degree. Your referee will be asked for a reference at the time you submit your application, so please ensure that your nominated referees are willing and able to provide references within 14 days of your application being submitted.

Closing date

The closing date for applications is 14 October 2021.

Further information

It is expected that interviews will take place on the weeks commencing 1 and 8 November 2021. If you have not heard from us by 28 October 2021, we thank you for your application but, on this occasion, you have not been successful.

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