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A Data Driven Framework for Attibution and Correlation in Intrusion Detection

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  • Full or part time
    Dr Yoo
    Prof Katos
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

Project Description

The overall aim of this project is to develop data driven intelligent and adaptive systems capable of analysing and correlating security events in intrusion detection.

As attribution is a non-trivial problem in cyberspace, the systematic research and progress of the state of the art is critical to the wellbeing of citizens who need to be protected by identification of potential attackers and perpetrators sent to justice.

Obscure security events can be correlated from multiple logs, and in doing so provide the higher level of vision necessary for accurate and expeditious intrusion analysis. However, security device logging can be extensive and difficult to interpret.

In this project, we will use novel approaches that combine the state-of-the-art methodologies from cyber security and data science, and develop an intelligent system that performs event correlation from the large-scale logs and alerts of multiple security technologies.

In experiments, we will setup an entry point and monitor all the communications in and out of the darknet and collect streams of security device logs, which have not been investigated.

We will then use methods from data science to investigate which changes of the multiple security device logs can be used to correlate the elements of the attack. We will design and develop two stages of clustering/classification algorithms. The first stage is essentially an anomaly detection exercise for modelling benign behaviour in order to highlight attack outliers. Once the offending events are identified, a feature-based attribution algorithm will run in order to establish the types of attacks but also to group them per specific attack activity. The latter essentially correlates the elements of the attack set to allow the potential identification of the attacker.

Finally, we will build an automated software framework capable of analysing and correlating security events in intrusion detection while efficiently interpreting large-scale security device logs.

What does the funded studentship include?
Funded candidates will receive a maintenance grant of £14,000 per annum (unless otherwise specified), to cover their living expenses and have their fees waived for 36 months. In addition, research costs, including field work and conference attendance, will be met.

Funded Studentships are open to both UK/EU and International students unless otherwise specified.

How to apply: Applications are made via our website using the Apply Online button below. If you have an enquiry about this project please contact us via the Email NOW button below, however your application will only be processed once you have submitted an application form as opposed to emailing your CV to us.

Candidates for funded PhD studentship must demonstrate outstanding qualities and be motivated to complete a PhD in 3 years.

All candidates must satisfy the University’s minimum doctoral entry criteria for studentships of an honours degree at Upper Second Class (2.1) and/or an appropriate Master’s degree. An IELTS (Academic) score of 6.5 minimum (or equivalent) is essential for candidates for whom English is not their first language.

In addition to satisfying basic entry criteria, BU will look closely at the qualities, skills and background of each candidate and what they can bring to their chosen research project in order to ensure successful and timely completion.

Funding Notes

Funded candidates will receive a maintenance grant of £14,000 (unless otherwise specified) per annum, to cover their living expenses and have their fees waived for 36 months. In addition, research costs, including field work and conference attendance, will be met.
Funded Studentships are open to both UK/EU and International students unless otherwise specified.

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