Malware Detection using the concept of Random Forest Algorithm

Malicious software is abundant in a world of innumerable computer users, who are constantly faced with these threats fromvarious sources like the internet, local networks and portable drives. Malware is potentially low to high risk and can causesystems to function incorrectly, steal data and even crash. Malware may be executable or system library files in the form ofviruses, worms, Trojans, all aimed at breaching the security of the system and compromising user privacy. In this study, theproposed machine learning algorithm is RF algorithm which use Gini index CART algorithm to create multiple decision treewith majority of the outputs from each decision trees. Here, total 1,38,047 data is collected which contain 96,724 malware and41,323 legit. RF algorithm achieved 99.54% accuracy during malware detection followed by 99.13% precision, 99.35% recalland 99.24% f1 score respectively during testing.