ShielDroid: a hybrid ML and DL approach for real-time malware detection system in Android

Abstract

Due to the rapid development of the advanced world of technology, there is a high increase in devices such as smartphones and tablets, which increase the number of applications used. Though an application has to pass the malware detection test before appearing in the play store, many applications successfully get trusted and accepted even though they contain malicious software variants that are challenging to detect. The application requires physical execution to see these malicious contents, which get undetected during the rst screening test. Due to the physical implementation of the application, it may be too late to undo the malware's damage. In this work, the usage of real-time Android malware detection analyzing Android applications to detect and swiftly distinguish complex malware has been discussed. This work focuses on the use of dynamic algorithms implemented by hybrid detection techniques of Android malware. After ltrating the collected dataset, the process of separation between harmful and benign apps is discussed. Then summarization and evaluation of the various techniques and classi cation algorithms employed have been discussed, identifying the best-suited method that gives the most accurate result in a minimum amount of time. The best way to reach the target is a hybrid Random Forest, and Multilayer perceptron network, where the overall accuracy achieved was 97.5% with an execution time of 22.945 seconds. An Android application, namely,\Shield: Malware Scanner", was developed using Java in determining if malware is present in an application. If there is any malware, it detects the type of malware and advises the user on securing their data and privacy and recovering from it.