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Ransomware has emerged as one of the major global threats in recent days. Thealarming increasing rate of ransomware attacks and new ransomware variantsintrigue the researchers in this domain to constantly examine thedistinguishing traits of ransomware and refine their detection orclassification strategies. Among the broad range of different behavioralcharacteristics, the trait of Application Programming Interface (API) calls andnetwork behaviors have been widely utilized as differentiating factors forransomware detection, or classification. Although many of the prior approacheshave shown promising results in detecting and classifying ransomware familiesutilizing these features without applying any feature selection techniques,feature selection, however, is one of the potential steps toward an efficientdetection or classification Machine Learning model because it reduces theprobability of overfitting by removing redundant data, improves the model'saccuracy by eliminating irrelevant features, and therefore reduces trainingtime. There have been a good number of feature selection techniques to datethat are being used in different security scenarios to optimize the performanceof the Machine Learning models. Hence, the aim of this study is to present thecomparative performance analysis of widely utilized Supervised Machine Learningmodels with and without RFECV feature selection technique towards ransomwareclassification utilizing the API call and network traffic features. Thereby,this study provides insight into the efficiency of the RFECV feature selectiontechnique in the case of ransomware classification which can be used by peersas a reference for future work in choosing the feature selection technique inthis domain.

A Comparative Performance Analysis of Explainable Machine Learning  Models With And Without RFECV Feature Selection Technique Towards Ransomware  Classification