Jordan Wylie
jordan wylie

Jordan Wylie

Student Experience

  J.Wylie@napier.ac.uk

   

Biography

A PhD student at Edinburgh Napier University funded by the Carnegie Trust for the Universities of Scotland. The focus of this research is on the verification of federated unlearning. My research interests include the privacy and security of machine learning as well as the use of machine learning for cybersecurity applications. I have previously completed BEng (Hons) Cybersecurity and Forensics and MSc Advanced Security and Digital Forensics degrees at Edinburgh Napier University. Both of my dissertations for these degrees focused on the application of machine learning for cybersecurity. In addition, I have also worked on three research projects as a research assistant investigating the generation of metamorphic Android malware to improve detection systems.

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Multi-Objective Evolutionary Algorithm for Automatic Generation of Adversarial Metamorphic Malware

Presentation / Conference Contribution
Babaagba, K. O., Wylie, J., Ayodele, M., & Tan, Z. (2024, September)
Multi-Objective Evolutionary Algorithm for Automatic Generation of Adversarial Metamorphic Malware. Presented at 29th European Symposium on Research in Computer Security - SECAI, Bydgoszcz, Poland
The rise of metamorphic malware, a dangerous type of malware, has sparked growing research interest due to its increasing attacks on information assets and computer networks. ...

An Evolutionary based Generative Adversarial Network Inspired Approach to Defeating Metamorphic Malware

Conference Proceeding
Babaagba, K. O., & Wylie, J. (2023)
An Evolutionary based Generative Adversarial Network Inspired Approach to Defeating Metamorphic Malware. In GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation (1753-1759). https://doi.org/10.1145/3583133.3596362
Defeating dangerous families of malware like polymorphic and metamorphic malware have become well studied due to their increased attacks on computer systems and network. Tradi...

Evaluation of Ensemble Learning for Android Malware Family Identification

Journal Article
Wylie, J., Tan, Z., Al-Dubai, A., & Wang, J. (2020)
Evaluation of Ensemble Learning for Android Malware Family Identification. Journal of Guangzhou University (Natural Science Edition), 19(4), 28-41
Every Android malware sample generally belongs to a specific family that performs a similar set of actions and characteristics. Having the ability to effectively identify Andr...

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