Revisiting the Approaches, Datasets and Evaluation Parameters to Detect Android Malware: A Comparative Study from State-of-Art

Abstract

Alongside the recognition of the android operating system (OS), android malware is on the increase. Cybercriminals are using different techniques to develop malware for android devices. In addition, malware authors are trying to make malicious android applications that severely undermine the potential of traditional malware detectors. The key purpose of the chapter is to analyze and have a different appearance at various techniques of Android malware detection in a variety of research articles. However, this chapter presents an analysis of varied android malware detection approaches and comparing them to supported various parameters like detection technique, analysis method, features extracted and so on. The experiments are based on substantial malware datasets, evaluation parameters and this study employ a wide variety of machine learning techniques, including decision trees and random forests, support vector machines, logistic model trees, and artificial neural networks, also Deep learning techniques. It is a comparative analysis that should be useful in this field for researchers. The analysis shows, based on simple criteria, the similarities and differences in essential published research in addition to the accuracy. Thus, this chapter aims to study various android malware detection techniques and to identify plausible research directions. The findings showed that machine learning, with greater detection accuracy, is a more promising method. In order to achieve improved accuracy, future researchers can pursue a deep learning approach with the use of a large dataset.