# High Accuracy Android Malware Detection Using Ensemble Learning

arXiv: https://arxiv.org/abs/1608.00835 · HTML: https://mlsanity.com/paper/1608.00835/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2016.

## Built on by (most cited first, 5 of 5)

- [Improving Accuracy and Explainability of Online Handwriting Recognition](https://mlsanity.com/paper/2209.09102/) (2022)
- [Machine and Deep Learning for IoT Security and Privacy: Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2210.13547/) (2022)
- [On building machine learning pipelines for Android malware detection: a procedural survey of practices, challenges and opportunities](https://mlsanity.com/paper/2306.07118/) (2023)
- [On Ensemble Learning](https://mlsanity.com/paper/2103.12521/) (2021)
- [Multifamily Malware Models](https://mlsanity.com/paper/2207.00620/) (2022)

## Abstract (arXiv)

With over 50 billion downloads and more than 1.3 million apps in the Google official market, Android has continued to gain popularity amongst smartphone users worldwide. At the same time there has been a rise in malware targeting the platform, with more recent strains employing highly sophisticated detection avoidance techniques. As traditional signature based methods become less potent in detecting unknown malware, alternatives are needed for timely zero-day discovery. Thus this paper proposes an approach that utilizes ensemble learning for Android malware detection. It combines advantages of static analysis with the efficiency and performance of ensemble machine learning to improve Android malware detection accuracy. The machine learning models are built using a large repository of malware samples and benign apps from a leading antivirus vendor. Experimental results and analysis presented shows that the proposed method which uses a large feature space to leverage the power of ensemble learning is capable of 97.3 to 99 percent detection accuracy with very low false positive rates.