SURVEY ON LITERATURES FOR THE DETECTION OF ANDROID MALWARE USING MACHINE LEARNING

Abstract

Android Operating System is an open source operating system with high efficiency and flexibility, which has led to enormous acceptance globally. Its populous prompts the advent of Android malware with the aim of invading users’ information without their knowledge and posing a threat to the android community at large. For that reason, a great number of signaturebased tools to detect Android malware are available on the market, but they can’t detect unknown Android malware. Thus, many researchers have conducted studies using machine learning techniques to detect Android malware, and the results have proved promising for detecting both known and unknown Android malware. This paper gives a study of machine learning-based methods for Android malware detection. In this regard, it succinctly provides a little background on Android applications and the Android Dataset. Besides, a critical evaluation of existing works on machine learning for detecting Android malware, the analyzes and summarizes a number of research papers based on sample collection, feature choice, model strength, and model problem for the benefit of the research community and identifies the areas that require additional study in spite of the dynamics of both Android technology and the related advancements in malware penetration