Malware Detection using the concept of Random Forest Algorithm
DOI:
https://doi.org/10.46243/jst.2025.v10.i05.pp38-49Keywords:
Decision Tree,, Malware Detection,, Machine learning,, Random Forest,Abstract
Malicious software is abundant in a world of innumerable computer users, who are constantly faced with these threats from
various sources like the internet, local networks and portable drives. Malware is potentially low to high risk and can cause
systems to function incorrectly, steal data and even crash. Malware may be executable or system library files in the form of
viruses, worms, Trojans, all aimed at breaching the security of the system and compromising user privacy. In this study, the
proposed machine learning algorithm is RF algorithm which use Gini index CART algorithm to create multiple decision tree
with majority of the outputs from each decision trees. Here, total 1,38,047 data is collected which contain 96,724 malware and
41,323 legit. RF algorithm achieved 99.54% accuracy during malware detection followed by 99.13% precision, 99.35% recall
and 99.24% f1 score respectively during testing.
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