Community Based Feature Selection Method for Detection of Android Malware

Abstract

The amount of malware has been rising drastically as the Android operating system enabled smartphones and tablets are gaining popularity around the world in last couple of years. One of the popular methods of static detection techniques is permission/feature-based detection of malware through the AndroidManifest.xml file using machine learning classifiers. Ignoring important features or keeping irrelevant features may specifically cause mystification to classification algorithms. Therefore, to reduce classification time and improve accuracy, different feature reduction tools have been used in past literature. Community detection is one of the major tools in social network analysis but its implementation in the context of malware detection is quite rare. In this article, the authors introduce a community-based feature reduction technique for Android malware detection. The proposed method is evaluated on two datasets consisting of 3004 benign components and 1363 malware components. The proposed community-based feature reduction technique produces a classification accuracy of 98.20% and ROC value up to 0.989.