# Feature importance in mobile malware detection

arXiv: https://arxiv.org/abs/2008.05299 · HTML: https://mlsanity.com/paper/2008.05299/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2020.

## Built on by (most cited first, 2 of 2)

- [Using Deep Learning to Detect Digitally Encoded DNA Trigger for Trojan Malware in Bio-Cyber Attacks](https://mlsanity.com/paper/2202.11824/) (2022)
- [Synthetic Data: AI's New Weapon Against Android Malware](https://mlsanity.com/paper/2511.19649/) (2025)

## Abstract (arXiv)

The topic of mobile malware detection on the Android platform has attracted significant attention over the last several years. However, while much research has been conducted toward mobile malware detection techniques, little attention has been devoted to feature selection and feature importance. That is, which app feature matters more when it comes to machine learning classification. After succinctly surveying all major, dated from 2012 to 2020, datasets used by state-of-the-art malware detection works in the literature, we analyse a critical mass of apps from the most contemporary and prevailing datasets, namely Drebin, VirusShare, and AndroZoo. Next, we rank the importance of app classification features pertaining to permissions and intents using the Information Gain algorithm for all the three above-mentioned datasets.