A Study on the Effect of Feature Selection on Malware Analysis using Machine Learning

Babaagba, Kehinde Oluwatoyin; Adesanya, Samuel Olumide

Abstract

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 and clustering algorithms. The algorithms are compared for effectiveness and efficiency using their predictive accuracy, among others, as performance metric. From the studies, we observe that the best detection rate was attained for supervised learning with feature selection. The supervised learning algorithm used was Multilayer Perceptron (MLP) algorithm. The analysis also reveals that our system can detect viruses from varying sources.

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: ICEIT 2019: 2019 8th International Conference on Educational and Information Technology
• Start Date: Mar 2, 2019
• End Date: Mar 4, 2019
• Acceptance Date: Feb 7, 2019
• Online Publication Date: Mar 2, 2019
• Publication Date: Mar 2, 2019
• Deposit Date: Aug 17, 2021
• Publicly Available Date: Aug 18, 2021
• Publisher: Association for Computing Machinery (ACM)
• Pages: 51–55
• Book Title: ICEIT 2019: Proceedings of the 2019 8th International Conference on Educational and Information Technology
• ISBN: 978-1-4503-6267-2
• DOI: https://doi.org/10.1145/3318396.3318448
• Public URL: http://researchrepository.napier.ac.uk/Output/2793726
• Publisher URL: https://dl.acm.org/doi/10.1145/3318396.3318448