  Our computer systems for decades have been threatened by various types of
hardware and software attacks of which Malwares have been one of them. This
malware has the ability to steal, destroy, contaminate, gain unintended access,
or even disrupt the entire system. There have been techniques to detect malware
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 malware
but, in this work we propose a novel technique to detect malware. 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.
