Hybrid Feature Selection Model for Detection of Android Malware and Family Classification

Abstract

Android OS based applications offer services in various aspects of our daily lives such as banking, personal, professional, social, etc. Increased usage of Android applications makes them extremely vulnerable to various malware threats. A resilient and attack resistant machine learning based Android malware detector is desired to achieve a safe working environment. This work employs feature selection on static and dynamic features and proposes a hybrid feature selection method that can identify most informative features while eliminating the irrelevant ones. Information gain from filter and recursive feature elimination from wrapper feature selection methods outperform other evaluated feature selection techniques. Thereafter, different classification algorithms are trained on the features selected through hybrid feature selection technique and experimental results showed that XGBoost obtained maximum accuracy i.e., 98% and 89% for binary and multiclass classification respectively using only 50 features.