Machine Learning Approaches for Malware Detection: An Analytical Overview

Abstract

The constantly changing character of malware makes "difficult to resolve using conventional signature-based detection methods. This paper discusses possible applications of Machine Learning (ML) algorithms to enhance malware detection precision. A comparative analysis is conducted on five prominent ML algorithms: Support Vector Machines (SVM), Decision Trees (DT), Neural Networks (NN), Random Forests (RF), and Logistic Regression (LR). Performance metrics, e.g., F1 score, TPR, FPR, and a Confusion Matrix analysis are used for picking up the most suitable algorithm for a given dataset.
This research, however, highlights the need for achieving an equilibrium between accurate malware detection and lowering the rate of false positives. Through the evaluation of the plusses and minuses of each ML algorithm, steps are taken towards the improvement of detection performance, especially that of the variants of elusive and polymorphic malwares. These results will contribute to the development of ML-based cybersecurity strategies that may be effective against constantly emerging malware threats in the digital space.