Android Malware Category and Family Detection and Identification using Machine Learning

TL;DR

This paper introduces machine learning methods for dynamic analysis to accurately detect and classify Android malware into categories and families, significantly improving detection accuracy and analysis speed.

Contribution

It presents two novel machine learning approaches for dynamic analysis that achieve over 96% accuracy in category detection and over 99% in family detection of Android malware.

Findings

Category detection accuracy exceeds 96%

Family detection accuracy exceeds 99%

Reduces malware analysis time significantly

Abstract

Android malware is one of the most dangerous threats on the internet, and it's been on the rise for several years. Despite significant efforts in detecting and classifying android malware from innocuous android applications, there is still a long way to go. As a result, there is a need to provide a basic understanding of the behavior displayed by the most common Android malware categories and families. Each Android malware family and category has a distinct objective. As a result, it has impacted every corporate area, including healthcare, banking, transportation, government, and e-commerce. In this paper, we presented two machine-learning approaches for Dynamic Analysis of Android Malware: one for detecting and identifying Android Malware Categories and the other for detecting and identifying Android Malware Families, which was accomplished by analyzing a massive malware dataset with 14 prominent malware categories and 180 prominent malware families of CCCS-CIC-AndMal2020 dataset on Dynamic Layers. Our approach achieves in Android Malware Category detection more than 96 % accurate and achieves in Android Malware Family detection more than 99% accurate. Our approach provides a method for high-accuracy Dynamic Analysis of Android Malware while also shortening the time required to analyze smartphone malware.