AndroClass: An Effective Method to Classify Android Applications by Applying Deep Neural Networks to Comprehensive Features
Masoud Reyhani Hamedani, Dongjin Shin, Myeonggeon Lee, Seong-je Cho, Changha Hwang · Wireless Communications and Mobile Computing · 2018
Android application (app) stores contain ahugenumber of apps, which aremanuallyclassified based on the apps’ descriptions into various categories. However, the predefined categories or apps descriptions are usuallynotvery accurate to reflect the real functionalities of apps, thereby leading tomisclassifythe apps, which may cause serioussecurity issuesandunreliabilityproblem in the app store. Therefore, the automatic app classification is animportantdemand to construct asecure,reliable,integrated, andeasy to navigateapp store. In this paper, we propose an effective method calledAndroClasstoautomaticallyclassify apps based on theirrealfunctionalities by usingrichandcomprehensivefeatures representing theactualfunctionalities of the apps. AndroClass performsthreesteps offeature extraction,feature refinement, andclassification. In the feature extraction step, we extract 14 various features for each app by utilizing aunified tool suite. In the feature refinement step, we applyRandom Forestalgorithm to refine the features. In the classification step, we combine refined features into asingleone and AndroClass is equipped with K‐Nearest Neighbor, Naive Bayes, Support Vector Machine, and Deep Neural Network to classify apps. On the contrary to the existing methods, all the utilized features in AndroClass arestableandclearlyrepresent the actual functionalities of the app, AndroClass doesnotpose any issues to theuser privacy, and our method can be applied to classifyunreleasedornewly releasedapps. The results ofextensiveexperiments with tworeal-worlddatasets and a dataset constructed byhuman expertsdemonstrate the effectiveness of AndroClass where the classification accuracy of AndroClass with the latter dataset is 83.5%.