Revisiting the Approaches, Datasets and Evaluation Parameters to Detect Android Malware: A Comparative Study from State-of-Art Abu Bakkar Siddikk, Md. Fahim Muntasir, Rifat Jahan Lia, Sheikh Shah Mohammad Motiur Rahman, Takia Islam, and Mamoun Alazab 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 mali- cious android applications that severely undermine the potential of traditional mal- ware detectors. The key purpose of the chapter is to analyze and have a differ- ent appearance at various techniques of Android malware detection in a variety of research articles. However, this chapter presents an analysis of varied android mal- ware detection approaches and comparing them to supported various parameters like detection technique, analysis method, features extracted and so on. The experi- ments 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 A. B. Siddikk (B ) · Md. F. Muntasir · R. J. Lia · S. S. M. M. Rahman (B ) · T. Islam Department of Software Engineering, Daffodil International University, Dhaka, Bangladesh e-mail: abu35-1994@diu.edu.bd S. S. M. M. Rahman e-mail: motiur.swe@diu.edu.bd Md. F. Muntasir e-mail: fahim35-1900@diu.edu.bd R. J. Lia e-mail: rifat35-1845@diu.edu.bd T. Islam e-mail: takia35-1014@diu.edu.bd M. Alazab College of Engineering, IT and Environment, Charles Darwin University, Darwin, Australia e-mail: alazab.m@ieee.org A. B. Siddikk · Md. F. Muntasir · R. J. Lia · S. S. M. M. Rahman · T. Islam nFuture Research Lab, Dhaka, Bangladesh © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Y. Maleh et al. (eds.), Artificial Intelligence and Blockchain for Future Cybersecurity Applications, Studies in Big Data 90, https://doi.org/10.1007/978-3-030-74575-2_7 125