Unmasking the common traits: an ensemble approach for effective malware detection

Abstract

Malware detection has become a critical aspect of ensuring the security and integrity of computer systems. With the ever-evolving landscape of malicious software, developing effective detection methods is of utmost importance. This study focuses on the identification of important features for malware detection methods, aiming to enhance the accuracy and efficiency of such systems. In this work, we propose an ensemble approach called FRAMC to identify the key features that contribute significantly to the detection of malware. The effectiveness of FRAMC is assessed using different types of classifiers on a number of real-world malware datasets. The outcomes of our analysis demonstrate that the proposed approach excels in terms of performance when compared to other methods.