MalwD&C: A Quick and Accurate Machine Learning-Based Approach for Malware Detection and Categorization
Attaullah Buriro, Abdul Baseer Buriro, Tahir Ahmad, Saifullah Buriro, Subhan Ullah
Abstract
Malware, short for malicious software, is any software program designed to cause harm to a computer or computer network. Malware can take many forms, such as viruses, worms, Trojan horses, and ransomware. Because malware can cause significant damage to a computer or network, it is important to avoid its installation to prevent any potential harm. This paper proposes a machine learning-based malware detection method called MalwD&C to allow the secure installation of Programmable Executable (PE) files. The proposed method uses machine learning classifiers to analyze the PE files and classify them as benign or malware. The proposed MalwD&C scheme was evaluated on a publicly available dataset by applying several machine learning classifiers in two settings: two-class classification (malware detection) and multi-class classification (malware categorization). The results showed that the Random Forest (RF) classifier outperformed all other chosen classifiers, achieving as high as 99.56% and 97.69% accuracies in the two-class and multi-class settings, respectively. We believe that MalwD&C will be widely accepted in academia and industry due to its speed in decision making and higher accuracy.