Hyperparameter Tunning and Feature Selection Methods for Malware Detection

Abstract

Smartphones have started to take an essential place in every aspect of our lives with the developing technology. All kinds of transactions, from daily routine work to business meetings, payments, and personal transactions, started to be done via smartphones. Therefore, there is a significant amount of very important user information stored in these devices which makes them a target for malware developers. For these reasons, machine learning (ML) methods have been used to detect malicious software on android devices quickly and reliably. In this study, a machine learning-based Android malware detection system has been developed, optimized, and tested. To this end, firstly, the data in the dataset has been balanced with 3 different methods namely SMOTE, SMOTETomek and ClusterCentroids. Afterward, the obtained results have been tried to be optimized by using different feature selection approaches including mRMR, Mutual Information, Select From Model, and Select k Best. Finally, the most two successful methods from the five tested ML algorithms (i.e. RF, SVM, LR, XGBoost, and ETC) have been tuned using GridSearch, Random Search, and Bayesian Optimization algorithms in order to investigate the effects of hyperparameter tuning on the performance of ML algorithms.