An Efficient Malware Detection System using Hybrid Feature Selection Methods
S. Abijah Roseline1, S. Geetha2
1S. Abijah Roseline, Department of Computing Science and Engineering, Vellore Insitute of Technology, Chennai (Tamil Nadu), India.
2S. Geetha, Department of Computing Science and Engineering, Vellore Insitute of Technology, Chennai (Tamil Nadu), India.
Manuscript received on 16 December 2019 | Revised Manuscript received on 23 December 2019 | Manuscript Published on 31 December 2019 | PP: 224-228 | Volume-9 Issue-1S3 December 2019 | Retrieval Number: A10431291S319/19©BEIESP | DOI: 10.35940/ijeat.A1043.1291S319
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© 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 a serious threat to individuals and users. The security researchers present various solutions, striving to achieve efficient malware detection. Malware attackers devise detection avoidance techniques to escape from detection systems. The key challenge is that growth of malware increases every hour, leading to large damages to users’ privacy. The training process takes much longer time, mining the unnecessary features. Feature Selection is effective in achieving unique feature set in detecting malware. In this paper, we propose a malware detection system using hybrid feature selection approach to detect malware efficiently with a reduced feature set. Machine learning based classification is performed on eight classifiers with two malware datasets. The experiments were done without and with feature selection. The empirical results show that the classification using selected feature set and XGB classifier identifies malware efficiently with an accuracy of 98.9% and 99.26% for the two datasets.
Keywords: Malware, Malware Features, Malware Detection, Malware Feature Selection, PE Files.
Scope of the Article: Probabilistic Models and Methods