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

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