Clone detection in 5G-enabled social IoT system using graph semantics and deep learning model

Abstract

The protection and privacy of the 5G-IoT framework is a major challenge due to the vast number of mobile devices. Specialized applications running these 5G-IoT systems may be vulnerable to clone attacks. Cloning applications can be achieved by stealing or distributing commercial Android apps to harm the advanced services of the 5G-IoT framework. Meanwhile, most Android app stores run and manage Android apps that developers have submitted separately without any central verification systems. Android scammers sell pirated versions of commercial software to other app stores under different names. Android applications are typically stored on cloud servers, while API access services may be used to detect and prevent cloned applications from being released. In this paper, we proposed a hybrid approach to the Control Flow Graph (CFG) and a deep learning model to secure the smart services of the 5G-IoT framework. First, the newly submitted APK file is extracted and the JDEX decompiler is used to retrieve Java source files from possibly original and cloned applications. Second, the source files are broken down into various android-based components. After generating Control-Flow Graphs (CFGs), the weighted features are stripped from each component. Finally, the Recurrent Neural Network (RNN) is designed to predict potential cloned applications by training features from different components of android applications. Experimental results have shown that the proposed approach can achieve an average accuracy of 96.24% for cloned applications selected from different android application stores.