IoT-based Cloud Service for Secured Android Markets using PDG-based Deep Learning Classification

Abstract

Software piracy is an act of illegal stealing and distributing commercial software either for revenue or identify theft. Pirated applications on Android app stores are harming developers and their users by clone scammers. The scammers usually generate pirated versions of the same applications and publish them in different open-source app stores. There is no centralized system between these app stores to prevent scammers from publishing pirated applications. As most of the app stores are hosted on cloud storage, therefore a cloud-based interaction system can prevent scammers from publishing pirated applications. In this paper, we proposed IoT-based cloud architecture for clone detection using program dependency analysis. First, the newly submitted APK and possible original files are selected from app stores. The APK Extractor and JDEX decompiler extract APK and DEX files for Java source code analysis. The dependency graphs of Java files are generated to extract a set of weighted features. The Stacked-Long Short-Term Memory (S-LSTM) deep learning model is designed to predict possible clones. Experimental results have shown that the proposed approach can achieve an average accuracy of 95.48% among clones from different application stores.