Knowledge Transfer Associate – Data Exfiltration Detection Specialist

University of Bristol

Salary: In the region of £28K pa, plus £6K for personal development

Based at TRL Technology Ltd in Tewkesbury, the post is funded through the Knowledge Transfer Partnership (KTP) between the University of Bristol and TRL.

Protecting sensitive data is a critical part of any organisation’s security measures, and as network infrastructures become increasingly complex and inter-connected, the classical model of perimeter defences is no longer sufficient to prevent loss of data assets.

One of the most potent and hard to detect threats is that of exfiltration by (or assisted by) a trusted insider, and new methods are needed to detect, prevent or disrupt this form of attack. Detecting such insider attacks, and minimising the number of false positives, is one of the most challenging problems in cyber security.

As a KTP associate you will be at the core of the collaboration between L-3 TRL Technology Ltd and the Systems Engineering Centre at the University of Bristol. The collaboration will develop innovative ways to protect against exfiltration of data, by applying novel academic techniques. Your role will be to develop and refine suitable methods and models that will enable TRL to develop a product/service system solution.

TRL aims to provide the best technological products and solutions to protect people and infrastructure. This focuses on three capability areas of Electronic Warfare, Spectrum Surveillance and National Security, and the company is a leading supplier of high technology network security solutions to government organisations. Currently, TRL is focusing on the data exfiltration arena; market demand for data exfiltration solutions is significant, due to increased threats: organisations’ external security and protection are seen as strong, hence gaining data externally is difficult; organisations are increasingly connected; and the consequences of organisations egressing data are significant. You will have the opportunity to contribute towards this exciting area of development.

The Systems Centre at the University of Bristol leads in applying Systems Thinking to complex engineering problems and deals with a number of themes of multidisciplinary fundamental and applied research in Systems. A key tenet of the Systems research undertaken at Bristol is to recognise explicitly that engineered ‘hard’ systems are embedded in the ‘soft’ human systems, including those of research, design, manufacture and end use, and therefore, that all technical ‘hard’ systems risks need to be understood within a socio-economic context. One of its key research themes, which will address this project, is to develop advances in studies of security, systems resilience, engineering risk, safety critical systems and uncertainty management.

You will have a first or 2:1 degree in Computer Science, Maths, Engineering or another related discipline plus a Doctorate-level qualification in the fields of information and communication security, computer networks or computer forensics or related discipline. You will have an advanced understanding of computer network and security principles, excellent theory and practice of computer networking, and an understanding of analytical modelling techniques. Experience of modelling and simulation tools, steganography and steganalysis, intrusion detection and prevention systems and machine learning techniques are desirable.

This post is offered on a full time, fixed term contract for 3 years. Candidates must be able to take up the post by 14th July 2015 at the latest.

David Griffiths: david.eh.griffiths@l-3com.com; 01684 852452 
or 
Dr Theo Tryfonas: theo.tryfonas@bristol.ac.uk; 0117 33 15740

To apply, please click here to be redirected to our website.

The closing date for applications is 15 March 2015.



Status:Research Scientist
Location:Bristol, UNITED KINGDOM
Advert Published:16 Feb 2015
Expiry date:15 Mar 2015
University of Bristol Ref. No.:
ACAD101268
Academic Jobs EU Ref. No.:
Please quote this number if applying for this job offline.
J17732