# Android Malware Category and Family Detection and Identification using Machine Learning

arXiv: https://arxiv.org/abs/2107.01927 · HTML: https://mlsanity.com/paper/2107.01927/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 38 references that are in the map.
- First posted on arXiv 5 Jul 2021.

## Builds on (most cited first, 1 of 1)

- [MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models](https://mlsanity.com/paper/1612.04433/) (2016)

## Built on by (most cited first, 1 of 1)

- [Analysis and Detection against Network Attacks in the Overlapping Phenomenon of Behavior Attribute](https://mlsanity.com/paper/2310.10660/) (2023)

## Abstract (arXiv)

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.