A multi-tiered feature selection model for android malware detection based on Feature discrimination and Information Gain

Abstract

With the rise in popularity and its open system architecture, Android has become vulnerable to malicious attacks. There are several malware detection approaches available to fortify the Android operating system from such attacks. These malware detectors classify target applications based on the patterns found in the features present in the Android applications. As the analytics data continues to grow, it negatively impacts the Android defense mechanism. A large number of irrelevant features has become the performance bottleneck of the detection mechanism. This paper presents a multi-tiered feature selection model, which can discover relevant and significant features for improving the accuracy of malware detection approaches. The proposed method applies five machine learning classification techniques to the selected feature set. This work presents the Optimal Static Feature Set (OSFS) and, Most Important Features (MIFs) discovered with each machine learning approach. Rigorous testing and analysis show that Random Forest classification achieves the highest Accuracy rate of 96.28%.