Android Malware Detection through Machine Learning Techniques: A Review


  • Abikoye Oluwakemi Christiana
  • Benjamin Aruwa Gyunka Department of Computer Science University of Ilorin Ilorin, Nigeria
  • Akande Noah Computer Science Department, Landmark University, kwara State



Machine Learning, Ensemble Learning, Android Malware, Android Malware Detection, Base Classifier, Static Analysis, Dynamic Analysis


The open source nature of Android Operating System has attracted wider adoption of the system by multiple types of developers. This phenomenon has further fostered an exponential proliferation of devices running the Android OS into different sectors of the economy. Although this development has brought about great technological advancements and ease of doing businesses (e-commerce) and social interactions, they have however become strong mediums for the uncontrolled rising cyberattacks and espionage against business infrastructures and the individual users of these mobile devices. Different cyberattacks techniques exist but attacks through malicious applications have taken the lead aside other attack methods like social engineering. Android malware have evolved in sophistications and intelligence that they have become highly resistant to existing detection systems especially those that are signature-based. Machine learning techniques have risen to become a more competent choice for combating the kind of sophistications and novelty deployed by emerging Android malwares. The models created via machine learning methods work by first learning the existing patterns of malware behaviour and then use this knowledge to separate or identify any such similar behaviour from unknown attacks. This paper provided a comprehensive review of machine learning techniques and their applications in Android malware detection as found in contemporary literature.




How to Cite

Christiana, A. O., Gyunka, B. A., & Noah, A. (2020). Android Malware Detection through Machine Learning Techniques: A Review. International Journal of Online and Biomedical Engineering (iJOE), 16(02), pp. 14–30.