Research article

BEHAVIOR BASED ANDROID MALWARE DETECTION USING MACHINE LEARNING CLASSIFIERS

Juan José Flores Fiallos, Diego Fernando Mayorga Pérez, Edwin Ángel Jácome Domínguez, María Verónica Albuja Landi, Carlos Luis Gusqui Guananga, Henry Sebastián Mayorga Pérez

Online First: May 10, 2023


The android mobile phone platform widely presents smart phone operating system in the current market. As it works on open source platform it is developing continuously and rapidly and is advantageous to the android developers. Current mobile devices offers large number of services applications functions new technologies as compared to personal computers. Due the popularity it gains all the attentions from the developers. Next, it undergoes several twists and turns while going through development process. There are various ways through which malware can insert into an application. This paper revels about how the malware attacks an application and identify the malware in it. We have used machine learning classifiers to identify the malwares in an application. This paper also summarizes the analysis and comparison of machine learning algorithms on an application.

Keywords

Behavior analysis; Machine Learning; Malware attacks; anomaly detection;