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  Cryptography Artifact and Behavioural Analysis for Malware Detection


   School of Computing

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  Assoc Prof Rich McFarlane, Dr G Russell, Dr Owen Lo  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

The Blockpass Identity Lab at Edinburgh Napier University focuses on core areas of trust, identity and cryptography, and has success with recent spin-out companies. This studentship aims to extend current research work around the detection of cryptographic threats, such as those related to ransomware and data loss protection. An important focus is likely to be around the detection of cryptographic assets and behaviours, including the analysis of running memory and side channels, in order to detect malware activity or for malicious data sharing purposes.

 

Academic qualifications

A first degree (at least a 2.1) or MSc ideally in Computer Science-related area with a good fundamental knowledge of  computer science and computer security.

 

English language requirement

IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online.

 

Essential attributes:

·        Strong focus on applying computer security concepts, such as for the classification of threat information, trusted digital signing, data protection, and data privacy.

·        Good written and oral communication skills.

·        Strong motivation, with evidence of independent research skills relevant to the project.

·        Good organisation and time management skills.

 

Desirable attributes:

·        Excellent in programming and software testing.

·        Good knowledge and understanding of symmetric and public key encryption methods.

·        Knowledge of cryptography fundamentals and their application.


Funding Notes

This is a fully funded positions, with an associated stipend.

References

Lee, K., Lee, S. Y., & Yim, K. (2019). Machine learning based file entropy analysis for ransomware detection in backup systems. IEEE Access, 7, 110205-110215.
McLaren, P., Russell, G., Buchanan, W. J., & Tan, Z. (2019). Decrypting live SSH traffic in virtual environments. Digital Investigation, 29, 109-117.
Sihwail, R., Omar, K., & Ariffin, K. A. Z. (2018). A survey on malware analysis techniques: Static, dynamic, hybrid and memory analysis. International Journal on Advanced Science, Engineering and Information Technology, 8(4-2), 1662.
Lo, O., Buchanan, W. J., & Carson, D. (2017). Power analysis attacks on the AES-128 S-box using differential power analysis (DPA) and correlation power analysis (CPA). Journal of Cyber Security Technology, 1(2), 88-107.