Adverse cyber events, either distributed/spreading attacks or targeted one-off attacks, are happening all the time in the cyber space, leading to a tremendous loss economically and socially. Intensive research efforts have been dedicated to fighting such attacks, but most of the existing approaches are only able to detect known threats, commonly using the signature of known attacks. In order to detect unknown attacks, the anomaly detection approaches were designed which identify the behaviour of the network traffic that does not conform to any expected pattern. However, these approaches are only able to detect abnormal events, but cannot determine their types in order to prevent their occurrences.
This proposed project aims to address this by developing and evaluating an unknown cyber threat detection and interpretation system. A model based on the core technology adaptive sparse deep fuzzy inference will be proposed to address this challenge. Compared to other AI approaches, this approach has the following unique advantages: 1) the rule base used in a fuzzy inference system can be readily transferred to linguistic rules, which are transparent, readily comprehensible and interpretable for human experts for intelligence acquisition; 2) with the support of the recent development in adaptive sparse fuzzy rule base generation and deep learning, concise, neat but expressively powerful rules can be generated to highly summarise big streaming data in real time; and 3) fuzzy inference systems usually require less computational resources, making it possible to be deployed in embedded systems or IoT devices, in addition to high-end servers. The method will be evaluated extensively by publicly available data set and experiments in our recently built modern computer network and cybersecurity labs to ensure its high-grade performance.
This project is supervised by Dr Longzhi Yang.
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 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. SF19/EE/CIS/YANG) 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.
L. Yang, F. Chao, and Q. Shen, “Generalized adaptive fuzzy rule interpolation,” IEEE Transactions on Fuzzy Systems, vol. 25, no. 4, pp. 839–853, Aug 2017.
L. Yang and Q. Shen, “Adaptive fuzzy interpolation,” IEEE Transactions on Fuzzy Systems, vol. 19, no. 6, pp. 1107–1126, Dec 2011.
J. Li, Y. Qu, F. Chao, H. P. H. Shum, E. S. L. Ho, and L. Yang, Machine Learning Algorithms for Network Intrusion Detection. Cham: Springer International Publishing, 2019, pp. 151–179.
N. Naik, P. Jenkins, R. Cooke, and L. Yang, “Honeypots that bite back: A fuzzy technique for identifying and inhibiting fingerprinting attacks on low interaction honeypots,” in 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), July 2018, pp. 1–8.
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.
Sharma, R., Issac, B. & K., Kalita, H. R. (2019). Intrusion Detection and Response System Inspired by the Defense Mechanism of Plants, IEEE Access, IEEE, ISSN 2169-3536, vol. 7, 52427-52439.
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.