# A New Android Malware Detection Approach Using Bayesian Classification

arXiv: https://arxiv.org/abs/1608.00848 · HTML: https://mlsanity.com/paper/1608.00848/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2016.

## Built on by (most cited first, 6 of 6)

- [Robust Android Malware Detection System against Adversarial Attacks using Q-Learning](https://mlsanity.com/paper/2101.12031/) (2021)
- [Graph Neural Network-based Android Malware Classification with Jumping Knowledge](https://mlsanity.com/paper/2201.07537/) (2022)
- [A Comparison of Graph Neural Networks for Malware Classification](https://mlsanity.com/paper/2303.12812/) (2023)
- [On building machine learning pipelines for Android malware detection: a procedural survey of practices, challenges and opportunities](https://mlsanity.com/paper/2306.07118/) (2023)
- [Light up that Droid! On the Effectiveness of Static Analysis Features against App Obfuscation for Android Malware Detection](https://mlsanity.com/paper/2310.15645/) (2023)
- [Malceiver: Perceiver with Hierarchical and Multi-modal Features for Android Malware Detection](https://mlsanity.com/paper/2204.05994/) (2022)

## Abstract (arXiv)

Mobile malware has been growing in scale and complexity as smartphone usage continues to rise. Android has surpassed other mobile platforms as the most popular whilst also witnessing a dramatic increase in malware targeting the platform. A worrying trend that is emerging is the increasing sophistication of Android malware to evade detection by traditional signature-based scanners. As such, Android app marketplaces remain at risk of hosting malicious apps that could evade detection before being downloaded by unsuspecting users. Hence, in this paper we present an effective approach to alleviate this problem based on Bayesian classification models obtained from static code analysis. The models are built from a collection of code and app characteristics that provide indicators of potential malicious activities. The models are evaluated with real malware samples in the wild and results of experiments are presented to demonstrate the effectiveness of the proposed approach.