Ransomware Tweet Detection and Attack Detection using Deep Learning (Application Ref: SF19/EE/CIS/ISSAC)

Project Description

Ransomware is a type of malicious software designed to deny access to a computer system or data until a ransom is paid. This project will try to tackle two aspects of ransomware. First, we will investigate and implement a ransomware tweet detection scheme. In this research, we will initially analyse multiple families of ransomware over a period. A deep learning architecture will be used to categorize ransomware tweets to their corresponding family. The social media data can be monitored by the method proposed which will be able to alert about ransomware spreads. This will help the incident management to better plan the resources to mitigate the attack. Second, we will investigate and implement an intelligent ransomware attack detection scheme, for the attacks identified in the first stage, as well as for the new variants. The number of ransomware variants has increased rapidly, and the way ransomware works needs to be differentiated from malware so as to protect against ransomware-based attacks. Though ransomware is like malware in some respects, but they are clearly different. Ransomware generally focuses on many file-related operations in a short burst of time to encrypt files and lock the victim’s computer. The signature-based malware detection methods will not be able to detect zero-day and unknown ransomware. Thus a novel protection mechanism for ransomware detection is needed and it should focus on ransomware-specific operations to differentiate ransomware from other malware and benign files. This project will use a ransomware detection method using an optimized version of deep learning through bio-inspired metaheuristics algorithms to achieve that purpose. Optimized versions of deep learning architectures like convolutional neural networks (CNNs) or other variants, can detect malware or ransomware efficiently simply by looking at the raw bytes of Windows Portable Executable files.

This project is supervised by Dr Biju Issac.
Start Date: 1 March 2020 or 1 October 2020.

Funding Information

This is an unfunded research project.

Eligibility Requirements

• 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.

Application Process

For further details of how to apply, entry requirements and the application form, see link: 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/ISSAC) will not be considered.

Supplementary Information

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.

References

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.

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.