Stochastic log file analysis for network based intrusion detection

Eligibility: UK/EU/International

Award Details: No award (self-funding)

Duration: Full-Time 3 years Fixed Term (Sept 2017)

Application deadline: Ongoing

Informal enquiries are essential before application; contact Dr. Sandy Taramonli to discuss this opportunity.

Congratulations on taking your first steps toward a Research Degree with Coventry’s Faculty of Engineering and Computing. As an ambitious and innovative University, we’re investing an initial £100m into our new research strategy, ‘Excellence with Impact’. Through original approaches from world-leading experts, we’re aiming for our research to make a tangible difference to the way we live. As a research student you are an integral part of Coventry’s lively and diverse research community and contribute to our reputation for excellence. With our exceptional facilities and superb support mechanisms you are afforded every opportunity for academic success.

The Project

In a network-based intrusion detection system, log file analysis is crucial, as a thorough examination of log files might reveal hidden malicious activities and attacks. Combining information from different sources could provide more accurate results; however, different types of log files exist, each following their own arbitrary formats and sizes, making it hard to utilise digital evidence efficiently and reliably. In addition, the increasing number of network attacks, especially in the case of large and complex networks, make the examination and analysis of log files a rather challenging task. In this work, it is suggested that stochastic approaches could be applied to log file analysis in order to optimise network intrusion detection. The aim of this project is to provide a reliable decision making framework for log file analysis that can be used to improve the efficiency and accuracy of real time event classification and intrusion detection of malicious activities, as well as to reduce false positives and false negatives, using stochastic and statistical methods.

About The Centre/Department

The Cybersecurity researchers within the school of Computing, Electronics and Maths work together and with members of the wider faculty on a number of projects and have regular opportunities for new PhD students. Current work covers a range topics such as security for low-powered embedded systems, software defined networking, enhancing security through machine learning, protocol analysis, detecting and analysing steganography, vehicle security and security in BYOD environments.
Successful Applicants

Successful applicants will have:

- A minimum of a 2:1 first degree in a relevant discipline/subject area with a minimum 60% mark in the Project
- A Masters Degree in a relevant subject area will be considered as an equivalent. The Masters must have been attained with overall marks at a minimum merit level (60%). In addition, the dissertation or equivalent element in the Masters must also have been attained with a minimum mark at merit level (60%).
- The potential to engage in innovative research and to complete the PhD within a prescribed period of study
- Language proficiency (IELTS overall minimum score of 7.0 with a minimum of 6.5 in each component).

Additionally:

- Background in Computer Science or relevant subject
- Strong programming skills
- Willingness to learn stochastic modelling
- Willingness to research the area, perform experiments, analyse the results and publish papers
- Knowledge in anomaly detection ad machine learning would be beneficial

Eligibility & Application Procedure

Application Procedure:

Application form and covering letter only.

Application information can be found in our how to apply section:
http://www.coventry.ac.uk/research/research-students/how-to-apply/
Before completing the application please contact Dr. Sandy Taramonli (cc’ing [Email Address Removed]) for an initial informal discussion about the opportunity.

Eligibility:

All UK/EU and International students with the required entry requirements are eligible to apply that meet the academic requirements, the eligibility criteria can be found here:
http://www.coventry.ac.uk/research/research-students/research-entry-criteria/?id=88841