Performance of Malware Detection Classifier Using Genetic Programming in Feature Selection

Abstract

The term "malicious software," which is commonly referred to as malware, describes malicious software that affects or harms computers, servers, or networks. While the numbers and complexity of malware have rapidly increased, developing a malware detection system is required to detect malware in the world of cybersecurity and test the behavior of its new features. While traditional techniques provide less efficiency in detecting new malware, machine learning techniques are used to achieve rapid malware detection in an intelligent way to improve detection performance, as malware and its application in the industry are constantly increasing. In this study, we developed a malware detection model by detecting malware using machine learning classifiers, after passing a new feature selection technique using genetic programming. We also compared the performance of all classifiers using the most recent feature selection techniques. Results show that Random Forest, Random Forest (4), and Random Tree give the best value in all experiments, while Hoeffding Tree and Decision Stump give lower values for F1-score and accuracy in all experiments. The feature selection method that proposed GPMP gives a better value than Filter-based with little differences. The accuracy and F1-score have the values of 0.881066 and 0.867546 for GPMP, and the values of 0.877624 and 0.862894 for Filter-based, respectively. The experimental results reveal that GPMP used fewer features than Filter-based, and this affected the computation and complexity of the model.