Enhancing Malware Detection through Self-Union Feature Selection Using Firefly Algorithm with Random Forest Classification

Abstract

The proliferation of malware gravely threatens the security of computer systems and sensitive data. This work aims to improve malware detection by using advanced feature selection techniques. The study utilizes the Firefly Algorithm (FA) for feature selection in binary and multiclass classifications to enhance the discrimination capabilities of selected features. The selected features from the binary and multiclass classifications are combined to generate a comprehensive feature set. The Obfuscated-MalMem2022 dataset is employed in the experimental evaluation. The Random Forest (RF) method completes the classification problem. Remarkably, the results demonstrate that RF performs better with the combined feature set than with features chosen separately from the binary and multiclass classifications by the FA method. RF attains a remarkable 99.983% accuracy in binary classification, demonstrating the potency of the selected features in differentiating between malicious and benign data. Moreover, RF demonstrates an impressive accuracy of 87.304% in multiclass classification, highlighting the strength of the proposed methodology.