All Outputs (3)

Multi-Objective Evolutionary Algorithm for Automatic Generation of Adversarial Metamorphic Malware (2024)
Presentation / Conference Contribution
Babaagba, K., 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. Sophos’ recent threat report reveals that 94% of malware targeting organiza... Read More about Multi-Objective Evolutionary Algorithm for Automatic Generation of Adversarial Metamorphic Malware.

An Evolutionary based Generative Adversarial Network Inspired Approach to Defeating Metamorphic Malware (2023)
Presentation / Conference Contribution
Babaagba, K. O., & Wylie, J. (2023, July). An Evolutionary based Generative Adversarial Network Inspired Approach to Defeating Metamorphic Malware. Presented at The Genetic and Evolutionary Computation Conference (GECCO) 2023, Lisbon

Defeating dangerous families of malware like polymorphic and metamorphic malware have become well studied due to their increased attacks on computer systems and network. Traditional Machine Learning (ML) models have been used in detecting this malwar... Read More about An Evolutionary based Generative Adversarial Network Inspired Approach to Defeating Metamorphic Malware.

Evaluation of Ensemble Learning for Android Malware Family Identification (2020)
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 Android malware families can assist in addressing the damage caused by malware.... Read More about Evaluation of Ensemble Learning for Android Malware Family Identification.