Methods for Detecting Android Malware: Employing Mobile Devices to Improve Procedures for Inquiry-Based Learning

Abstract

The operating system (OS) of a computer controls both its hardware and software. It handles necessary functions including input and output processing, file and memory management, and peripheral device management, including disc drives and printers. Programs created for particular purposes are referred to as application software. These programs, which are frequently open source and freely accessible, contribute to the growing number of downloads. This paper discusses the basics of Android malware, its evolution, and malware analysis tools and techniques. Providing the research gaps and giving a review of the literature on Android malware detection using machine learning and deep learning are its main objectives. It offers the knowledge gathered from the literature as well as potential avenues for future research, which may aid in the development of reliable and precise methods for classifying Android malware. This paper conducts a systematic and comprehensive assessment of the methods and tools utilized for the analysis, classification, and detection of malicious Android apps. Several research gaps are indicated based on the thorough literature evaluation. Additionally, the report offers insights on future research paths that may aid academics in developing novel and reliable methods for identifying and categorizing Android malware.