# Understanding Concept Drift with Deprecated Permissions in Android Malware Detection

arXiv: https://arxiv.org/abs/2507.22231 · HTML: https://mlsanity.com/paper/2507.22231/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 90 references that are in the map.
- First posted on arXiv 29 Jul 2025.

## Builds on (most cited first, 14 of 14)

- [Characterizing Concept Drift](https://mlsanity.com/paper/1511.03816/) (2015)
- [From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors](https://mlsanity.com/paper/2203.11070/) (2022)
- [Transcending Transcend: Revisiting Malware Classification in the Presence of Concept Drift](https://mlsanity.com/paper/2010.03856/) (2020)
- [Continuous Learning for Android Malware Detection](https://mlsanity.com/paper/2302.04332/) (2023)
- [Revisiting Concept Drift in Windows Malware Detection: Adaptation to Real Drifted Malware with Minimal Samples](https://mlsanity.com/paper/2407.13918/) (2024)
- [TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (Extended Version)](https://mlsanity.com/paper/2402.01359/) (2024)
- [MORPH: Towards Automated Concept Drift Adaptation for Malware Detection](https://mlsanity.com/paper/2401.12790/) (2024)
- [Combating Concept Drift with Explanatory Detection and Adaptation for Android Malware Classification](https://mlsanity.com/paper/2405.04095/) (2024)
- [Efficient Concept Drift Handling for Batch Android Malware Detection Models](https://mlsanity.com/paper/2309.09807/) (2023)
- [Aper: Evolution-Aware Runtime Permission Misuse Detection for Android Apps](https://mlsanity.com/paper/2201.12542/) (2022)
- [Deep Learning-Driven Malware Classification with API Call Sequence Analysis and Concept Drift Handling](https://mlsanity.com/paper/2502.08679/) (2025)
- [Cluster Analysis and Concept Drift Detection in Malware](https://mlsanity.com/paper/2502.14135/) (2025)
- [Multi-label Classification for Android Malware Based on Active Learning](https://mlsanity.com/paper/2410.06444/) (2024)
- [Malware families discovery via Open-Set Recognition on Android manifest permissions](https://mlsanity.com/paper/2505.12750/) (2025)

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

- [FreeMOCA: Memory-Free Continual Learning for Malicious Code Analysis](https://mlsanity.com/paper/2605.09664/) (2026)
- [Adversarial Vulnerability Under Temporal Concept Drift: A Longitudinal Study of Android Malware Detection](https://mlsanity.com/paper/2605.23623/) (2026)

## Abstract (arXiv)

Permission analysis is a widely used method for Android malware detection. It involves examining the permissions requested by an application to access sensitive data or perform potentially malicious actions. In recent years, various machine learning (ML) algorithms have been applied to Android malware detection using permission-based features and feature selection techniques, often achieving high accuracy. However, these studies have largely overlooked important factors such as protection levels and the deprecation or restriction of permissions due to updates in the Android OS -- factors that can contribute to concept drift. In this study, we investigate the impact of deprecated and restricted permissions on the performance of machine learning models. A large dataset containing 166 permissions was used, encompassing more than 70,000 malware and benign applications. Various machine learning and deep learning algorithms were employed as classifiers, along with different concept drift detection strategies. The results suggest that Android permissions are highly effective features for malware detection, with the exclusion of deprecated and restricted permissions having only a marginal impact on model performance. In some cases, such as with CNN, accuracy improved. Excluding these permissions also enhanced the detection of concept drift using a year-to-year analysis strategy. Dataset balancing further improved model performance, reduced low-accuracy instances, and enhanced concept drift detection via the Kolmogorov-Smirnov test.