# Android Malware Detection with Unbiased Confidence Guarantees

arXiv: https://arxiv.org/abs/2312.11559 · HTML: https://mlsanity.com/paper/2312.11559/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 23 references that are in the map.
- First posted on arXiv 17 Dec 2023.

## Builds on (most cited first, 2 of 2)

- [Criteria of efficiency for conformal prediction](https://mlsanity.com/paper/1603.04416/) (2016)
- [A machine learning approach to anomaly-based detection on Android platforms](https://mlsanity.com/paper/1512.04122/) (2015)

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

- [Enhancing Decision-Making in Windows PE Malware Classification During Dataset Shifts with Uncertainty Estimation](https://mlsanity.com/paper/2512.18495/) (2025)
- [Reliable Hierarchical Operating System Fingerprinting via Conformal Prediction](https://mlsanity.com/paper/2602.12825/) (2026)

## Abstract (arXiv)

The impressive growth of smartphone devices in combination with the rising ubiquity of using mobile platforms for sensitive applications such as Internet banking, have triggered a rapid increase in mobile malware. In recent literature, many studies examine Machine Learning techniques, as the most promising approach for mobile malware detection, without however quantifying the uncertainty involved in their detections. In this paper, we address this problem by proposing a machine learning dynamic analysis approach that provides provably valid confidence guarantees in each malware detection. Moreover the particular guarantees hold for both the malicious and benign classes independently and are unaffected by any bias in the data. The proposed approach is based on a novel machine learning framework, called Conformal Prediction, combined with a random forests classifier. We examine its performance on a large-scale dataset collected by installing 1866 malicious and 4816 benign applications on a real android device. We make this collection of dynamic analysis data available to the research community. The obtained experimental results demonstrate the empirical validity, usefulness and unbiased nature of the outputs produced by the proposed approach.