A Novel Malware Detection Mechanism based on Features Extracted from Converted Malware Binary Images

Abstract

Our computer systems since decades have been threatened by various types of harware and software attacks of which Malwares have been one of them. These malwares have the ability to steal, destroy, contaminate, gain unintended access or even disrupt the entire system. There have been techniques to detect malwares by performing static and dynamic analysis of malware files, but, stealthy malware has circumvented the static analysis method and for dynamic analysis there have been previous works that propose different methods to detect malwares but, in this work we propose a novel technique to detect malwares. We use malware binary images and then extract different features from the same and then employ different ML-classifiers on the dataset thus obtained. We show that this technique is successful in differentiating classes of malware based on the features extracted.