Deep Learning Based Malapps Detection in Android Powered Mobile Cyber-Physical System

The Mobile Cyber-Physical System (MCPS) integrates the mobility of various smart devices to exchange information between physical and cyber systems. Among those intelligent devices, Android-powered smart-phone usage increased significantly due to its low cost and simplicity. But this global prominence of Android operating system also makes it more appealing for cyber-attacks to obtain users' physical private information. Since attackers mostly prefer malicious applications to spread different viruses and take control of the user's device, it is crucial to classify and categorize the malignant application for secure MCPS. Modern machine learning algorithms have shown promising performance in identifying dangerous applications compared to traditional signature-based methods. But most existing works identify only the malicious application where category identification is essential for proper precaution. Also, the static analysis is insufficient for polymorphic malware, which includes regenerating code and changing its properties frequently to evade the detection process. In this study, we compare several state-of-the-art deep learning methods for malapps classification and categorization. Moreover, we propose an ensemble Dynamic Weighted Voting model to identify and label a wide variety of malicious applications using the CCCS-CIC-AndMal-2020 [1] dataset, which contains an extensive collection of Android malware samples. Our proposed ensemble model outperforms the baseline ensemble method Majority Voting by 1% and the classical LSTM model by 2%.