Protectors of the Android Domain: Research into Mobile Malware Detection and Defense

Abstract

Due to the ever-increasing amount of malware that can be found on mobile devices, Android malware detection has become an essential topic of study. It starts with an overview of android malware, including its many subtypes such as Trojan horses, adware, spyware, and ransomware, as well as the procedures that must be taken to avoid having malware installed on your device. In order to detect malicious programs, it performs an analysis on a number of attributes that have been collected from the application package files and system call traces of the device. The suggested solution makes use of a neural network model that was educated using a dataset consisting of both safe and harmful apps. This problem is solved with the assistance of android malware detection with the use of machine learning. In order to determine whether or not the Android application file that was uploaded includes malware or can be used safely, the system that has been presented makes use of a method for supervised machine learning that is known as a Neural Network. It gives an overview of the numerous methods that may be used to identify android malware, such as detection based on signatures, detection based on behaviors, and detection based on machine learning. Mobile devices, especially smartphones powered by Android, have emerged as indispensable aids in our day-to-day activities. On the other hand, a growth in the usage of mobile devices has resulted in an increase in the number of cyber-attacks, with malware being a serious concern. Malware designed for Android devices presents a serious threat to users of mobile devices since it has the potential to steal personal data, disrupt device functionality, and jeopardize the security of the device. As a result, it is more important than ever to identify and eliminate malware on Android devices.