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http://dx.doi.org/10.9717/kmms.2019.22.2.203

An Optimal Feature Selection Method to Detect Malwares in Real Time Using Machine Learning  

Joo, Jin-Gul (Dept. of Civil Eng., School of Engineering, Dongshin University)
Jeong, In-Seon (School of Electronics and Computer Eng., Chonnam National University)
Kang, Seung-Ho (Dept. of Information Security, College of Energy Convergence, Dongshin University)
Publication Information
Journal of Korea Multimedia Society / v.22, no.2, 2019 , pp. 203-209 More about this Journal
Abstract
The performance of an intelligent classifier for detecting malwares added to multimedia contents based on machine learning is highly dependent on the properties of feature set. Especially, in order to determine the malicious code in real time the size of feature set should be as short as possible without reducing the accuracy. In this paper, we introduce an optimal feature selection method to satisfy both high detection rate and the minimum length of feature set against the feature set provided by PEFeatureExtractor well known as a feature extraction tool. For the evaluation of the proposed method, we perform the experiments using Windows Portable Executables 32bits.
Keywords
Malware; Machine Learning; PEFeatureExtractor; Feature Selection; Real Time Detection; Intelligent Classifier;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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