As network applications are increasingly used with the popularity of broadband networks and Internet of Things (IoT), network security is a very important issue. The smartphones and computers use various applications for banking and online purchases, and the user needs to use it securely. To perform network attacks like spoofing, flooding, eavesdropping, etc. is so easy with some research. An intrusion detection system looks for different kind of network attacks in the incoming packets. Can we use machine learning to differentiate the attack packet flows, classify attacks and eventually stop them? Can we allow the learning algorithm to understand new kind of attacks and grow in intelligence? Can we use multi-agent approach? There is a need for an intelligent intrusion detection system that could detect different and dynamic attack patterns, and this research will develop such a system.
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]) in computer science or computer networks or related degrees; or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
* Appropriate IELTS score, if required
This project is well suited to motivated and hard-working candidates with a keen interest in network security and machine learning. The applicant should have excellent communication skills including proven ability to write in English.
Applicants should have and some background knowledge in the following areas: Network Security, Machine Learning and Artificial Intelligence algorithms. The following skills are highly desirable for this project:
• Ability to read and understand state of the art research in network security and machine learning
• Experience in Matlab
• Computer programming using object-oriented language (Preferably Java or C++)
• Analytical modelling
• Application development experience
For further details of how to apply, entry requirements and the application form, see https://www.northumbria.ac.uk/research/postgraduate-research-degrees/how-to-apply/
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. SF18/CIS/ISSAC) will not be considered.
Start Date: 1 March 2019 or 1 June 2019 or 1 October 2019
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.
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. (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, 17 pages.
Sharma, R. K., Kalita, H. R., Das, S. & Issac, B. (2016, December). Learning is never secure: Poison learning by Intrusion Detection System based on Self-Organizing Map, Proceedings of the 5th IEEE-EDS International Conference on Computing, Communication and Sensor Network (CCSN 2016), Kolkata, India.
Sharma, R. K., Kalita, H. R. & Issac, B. (2016). PIRIDS: A Model on Intrusion Response System Based on Biologically Inspired Response Mechanism in Plants, in "Advances in Intelligent and Soft Computing" Eds. Snášel V., Abraham, A., Krömer, P., Pant, M. & Muda, A. K., Springer, ISBN 978-3-319-28031-8, pp.105-116.
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.
Albayati, M., & Issac, B. (2015). Analysis of Intelligent Classifiers and Enhancing the Detection Accuracy for Intrusion Detection System, International Journal of Computational Intelligence Systems (IJCIS), Atlantis Press, Taylor and Francis, ISSN 1875-6891, 8(5), 841-853.