Dynamic protection framework against advanced persistent threats in 5G networks

Advanced Persistent Threats (APTs) are considered and often defined as the threats that are the most challenging to detect and defend against. Crucially, the advent of 5G not only accentuates existing advanced threats but also introduces new ones, for which existing security mechanisms are not always directly applicable.

Start date
1 October 2019
Duration
Applications for this studentship are now closed.
Application deadline
Funding information

This research project has funding attached. Funding for this project is available to UK citizens. A stipend of £22k tax free and fees are covered per year.

 

About

This PhD project aims to improve 5G security against APTs by:

  • Designing and developing a decentralised threat detection method based on statistical and rule-based machine learning with the goal to assess the behaviour of the network and certain cells and their deviations from “standard” behaviour, in a manner that is dynamic and able to reconfigure automatically in time.
  • Designing and developing a game-theoretic framework to support decision making of the defender (e.g. 5G infrastructure owner or 5G network operator) against a rational APT-style attacker. The proposed framework will advance the current state of the art by enabling a precise analysis of the interactions between Defender and the Attacker (i.e. APT) providing “robust” decision support for the Defender.

This research is supported by the UK Government and it is in partnership with BT.

You will work at the University of Surrey, at the Surrey Centre for Cyber Security (SCCS) and at the 5G Innovation Centre (5GIC).

Eligibility criteria

Skills

Essential

  • Bachelors degree in computer science (UK equivalent first classification)
  • Interest in any of the following: cyber security, privacy, machine learning, game theory, mathematical optimisation cyber risk assessment
  • Programming experience
  • Analytical skills 
  • Knowledge of foundations of computer science 
  • Ability to think independently 
  • Strong verbal and written communication skills, both in plain English and scientific language for publication in relevant journals and presentation at conferences. 

Desirable

  • Masters degree (UK equivalent of Merit classification or above) 
  • Knowledge of cyber security and computer networks
  • Experience in machine learning
  • Experience in game theory or mathematical optimisation 
  • Experience of implementation and/or experimentation with verification tools.

This studentship is only available to UK students.

How to apply

Applications for this studentship are now closed.

Contact details

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