An Ensemble Voted Feature Selection Technique for Predictive Modeling of Malwares of Android

An Ensemble Voted Feature Selection Technique for Predictive Modeling of Malwares of Android

Abhishek Bhattacharya (Birla Institute of Technology, Mesra, India), Radha Tamal Goswami (Director, Techno India College of Technology, Kolkata, India), Kuntal Mukherjee (Birla Institute of Technology, Mesra, India) and Nhu Gia Nguyen (Duy Tan University, Da Nang, Vietnam)
Copyright: © 2019 |Pages: 24
DOI: 10.4018/IJISMD.2019040103


Each Android application requires accumulations of permissions in installation time and they are considered as the features which can be utilized in permission-based identification of Android malwares. Recently, ensemble feature selection techniques have received increasing attention over conventional techniques in different applications. In this work, a cluster based voted ensemble voted feature selection technique combining five base wrapper approaches of R libraries is projected for identifying most prominent set of features in the predictive modeling of Android malwares. The proposed method preserves both the desirable features of an ensemble feature selector, accuracy and diversity. Moreover, in this work, five different data partitioning ratios are considered and the impact of those ratios on predictive model are measured using coefficient of determination (r-square) and root mean square error. The proposed strategy has created significant better outcome in term of the number of selected features and classification accuracy.
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