All Outputs (2)

Nowhere Metamorphic Malware Can Hide - A Biological Evolution Inspired Detection Scheme (2019)
Presentation / Conference Contribution
Babaagba, K. O., Tan, Z., & Hart, E. (2019, November). Nowhere Metamorphic Malware Can Hide - A Biological Evolution Inspired Detection Scheme. Presented at The 5th International Conference on Dependability in Sensor, Cloud, and Big Data Systems and Applications (DependSys 2019), Guangzhou, China

The ability to detect metamorphic malware has generated significant research interest over recent years, particularly given its proliferation on mobile devices. Such malware is particularly hard to detect via signature-based intrusion detection syste... Read More about Nowhere Metamorphic Malware Can Hide - A Biological Evolution Inspired Detection Scheme.

A Study on the Effect of Feature Selection on Malware Analysis using Machine Learning (2019)
Presentation / Conference Contribution
Babaagba, K. O., & Adesanya, S. O. (2019, March). A Study on the Effect of Feature Selection on Malware Analysis using Machine Learning. Presented at ICEIT 2019: 2019 8th International Conference on Educational and Information Technology, Cambridge, UK

In this paper, the effect of feature selection in malware detection using machine learning techniques is studied. We employ supervised and unsupervised machine learning algorithms with and without feature selection. These include both classification... Read More about A Study on the Effect of Feature Selection on Malware Analysis using Machine Learning.