Malware Analysis for Android-based Reverse Engineering Techniques

Mobile devices are one of the most popular and widespread technological implements in the modern world. Their immense popularity represents a proportionate immense vulnerability to mobile device-based malware attacks, especially on Android phones, which have cornered the majority of the market. Hackers are constantly devising new ways of attacking smartphones using creative and clever techniques like surveillance, credential theft, and malicious advertising. Therefore, there arises a need to safeguard against such attacks and secure our devices. One promising avenue is the use of machine learning (ML)-based methods to develop specific classifiers based on a set of training examples, which can be relied on by malware detectors instead of using definitions for each malware signature. In this paper, we review the ML techniques for malware analysis of Android apps. We devise our own ML method based on suitability and offer a justification. We also delve into reverse engineering of Android applications, and determine the overall security and vulnerability of a few chosen datasets. In doing so, we hope to demonstrate any issues in this field, which can be the subject of further research.