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Machine Learning for Multi-Stage Attacks in Computer Networks (WSS03)

  • Full or part time
  • Application Deadline
    Tuesday, August 28, 2018
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

Project Description

Application details:

Reference number: WSS03
PhD start date: 1 January 2019
Closing date: 28 August 2018


Primary supervisor: Dr Konstantinos Kyriakopoulos
Secondary supervisor: Professor Lambotharan Sangarapillai

Loughborough University

Loughborough University is a top-ten rated university in England for research intensity (REF2014) and an outstanding 66% of the work of Loughborough’s academic staff who were eligible to be submitted to the REF was judged as ‘world-leading’ or ‘internationally excellent’, compared to a national average figure of 43%.

In choosing Loughborough for your research, you’ll work alongside academics who are leaders in their field. You will benefit from comprehensive support and guidance from our Graduate School, including tailored careers advice, to help you succeed in your research and future career.

Find out more: http://www.lboro.ac.uk/study/postgraduate/supporting-you/research/

Project Detail:

Cybersecurity attacks are an international priority in the agenda of many nations, particularly in the UK. A common trait of recurring top cyber threats, such as Botnets, DDoS, phishing, is that they are dependent on multiple stages in the cyber Kill Chain, and exploit communication protocols vulnerabilities and/or employ social engineering. These attacks are referred to as Multi-stage attacks.

The successful applicant will perform high-quality research, at Wolfson School’s Signal Processing and Networks research group, in topics related to multi-stage attacks and malware hunting in computer networks. Therefore, a strong background in computer network communications is essential (e.g. the OSI stack).

The project aims to perform fundamental research in computer networks, focusing on creating real-time statistical learning and machine learning algorithms for identifying individual stages of the kill chain sequence in the network traffic. The developed methodologies will be applied on network traffic captured both from aggregated flows and per packet statistics satisfying different scaling requirements.

The project will be supervised between Dr Kostas Kyriakopoulos and Professor Lambotharan Sangarapillai, combining their expertise in network security and signal processing techniques.

In addition, the project will follow a practical/demonstrable approach, rather than restrict just in theoretical research. This combinational approach will provide the candidate with significant expertise transferable to the private sector.

Entry requirements:

Applicants should have, or expect to achieve, at least a 2:1 Honours degree (or equivalent) in Telecommunications, Computer Networks or a related subject. A relevant Master’s degree and/or experience in one or more of the following will be an advantage: Computer Networks, Network Security, Data Science, Signal Processing.

Contact details:

Name: Dr Konstantinos Kyriakopoulos
Email address:
Telephone number: +44(0)1509227542

How to apply:

All applications should be made online at http://www.lboro.ac.uk/study/postgraduate/apply/. Under programme name, select Mechanical and Manufacturing Engineering.

Please quote reference number: WSS03

Funding Notes

Please note that studentships will be awarded on a competitive basis to applicants who have applied to this project and other advertised projects starting with advert reference ‘WSS’ for the School of Mechanical, Electrical and Manufacturing Engineering.
If awarded, each 3 year studentship will provide a tax-free stipend of £14,777 p/a, plus tuition fees at the UK/EU rate (currently £4,260 p/a). While we welcome applications from non EU nationals, please be advised that it will only be possible to fund the tuition fees at the international rate and no stipend will be available. Successful candidates will be notified by 26 November 2018.

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