Comparative Analysis of Malware Detection Techniques and Machine-Learning Algorithms Used for Security Testing of Android Applications

Android application security testing comprises testing a mobile app through attacks that a malicious user would try. Android application security testing is a new field that is currently developing. The purpose of conducting this review is to briefly summaries the role of malware detection techniques and machine learning in security testing used for Android applications. The objective of this article is to gain some level of understanding of malware detection techniques and to analyze the machine learning algorithms used for security testing of Android applications. The findings of this study are intended to provide direction to security practitioners, researchers, and application developers in selecting appropriate methods and algorithms for protecting Android applications. This research supports ongoing initiatives to strengthen the vast Android app ecosystem and protect user data from nefarious actors by emphasizing the advantages and drawbacks of different techniques.