Improving Malware Detection Classification Accuracy with Feature Selection Methods and Ensemble-based Machine Learning Methods
P HarshaLatha1, R Mohanasundaram2
1AP. Harsha Latha*, Research Scholar, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
2Dr. R. Mohanasundaram, Associate Professor, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Manuscript received on November 12, 2019. | Revised Manuscript received on 22 November, 2019. | Manuscript published on December 10, 2019. | PP: 2055-2059 | Volume-9 Issue-2, December 2019. | Retrieval Number: B8009129219/2019©BEIESP | DOI: 10.35940/ijitee.B8009.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Malware is evolving serious threats to internet security. The classification of malware is extremely crucial in recent days. The traditional models are failed to achieve to get effective accuracy rate and the machine learning models are the basic models that accomplish the task of classification in a certain way, but in recent decades malware attacks are very drastic and difficult to achieve zero-day attacks. To compete with new malware, ensemble methods are highly effective and give better results of accuracy. In this paper, we propose a framework that combines the exploit of both feature selection methods and ensemble learning classifiers and gives better results of classification. In the experimental results, we prove that this combination of methods gives better classification with high accuracy of 100% with the Random Forest ensemble classifier.
Keywords: Machine Learning, Feature Selection methods, Classification, Malware Detection, Ensemble Learning
Scope of the Article: Classification