About the Project
This project is supervised by Dr Biju Issac.
Eligibility and How to Apply:
Please note eligibility requirement:
- Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
- Appropriate IELTS score, if required.
For further details of how to apply, entry requirements and the application form, see
Please note: Applications that do not include a research proposal of approximately 1,000 words (not a copy of the advert), or that do not include the advert reference (e.g. SF19/EE/CIS/ISSAC) will not be considered.
Start Date: 1 March 2020 or 1 October 2020.
Northumbria University takes pride in, and values, the quality and diversity of our staff. We welcome applications from all members of the community. The University holds an Athena SWAN Bronze award in recognition of our commitment to improving employment practices for the advancement of gender equality and is a member of the Euraxess network, which delivers information and support to professional researchers.
Imam, N., Issac, B. & and Jacob, S.M., (2019). A Semi-Supervised Learning Approach for Tackling Twitter Spam Drift, International Journal of Computational Intelligence and Applications (IJCIA), Imperial College Press (World Scientific Europe), 18(2), ISSN 1469-0268, 17 pages.
Sharma, R. K., Kalita, H. R. & Issac, B. (2018). Are Machine Learning Based Intrusion Detection System Always Secure? An Insight Into Tampered Learning, Journal of Intelligent and Fuzzy Systems, IOS Press, ISSN 1064-1246, 35(3), pp. 3635-3651.
Shah, S. A. R., Issac, B. & Jacob, S. M. (2018). Intelligent Intrusion Detection System through Combined and Optimized Machine Learning, International Journal of Computational Intelligence and Applications (IJCIA), Imperial College Press (World Scientific Europe), ISSN 1469-0268, 17(2), 17 pages.
Shah, S. A. R., & Issac, B. (2018). Performance Comparison of Intrusion Detection Systems and Application of Machine Learning to Snort System, Future Generation Computer Systems, Elsevier, ISSN 0167-739X, Vol. 80, 157-170.
Sharma, R. K., Kalita, H. R. & Issac, B. (2016). Plant-based Biologically Inspired Intrusion Response Mechanism: An insight into the proposed model PIRIDS, Journal of Information Assurance and Security (JIAS), ISSN 1554-1010, 11(6), 340-347.
J Li, Y Qu, F Chao, HPH Shum, ESL Ho, L Yang (2019). Machine Learning Algorithms for Network Intrusion Detection, AI in Cybersecurity, 2019
N Naik, P Jenkins, R Cooke, L Yang (2018) Honeypots that bite back: A fuzzy technique for identifying and inhibiting fingerprinting attacks on low interaction honeypots, Proceedings of 2018 IEEE International Conference on Fuzzy Systems.
L Yang, J Li, G Fehringer, P Barraclough, G Sexton (2017). Intrusion detection system by fuzzy interpolation, Proceedings of 2017 IEEE international conference on fuzzy systems.
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