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MaSS-Droid: Android Malware Detection Framework Using Multi-Layer Feature Screening and Stacking Integration.

Zihao Zhang^1,2, Qiang Han^1,2, Zhichao Shi^1,2

¹School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China.

Entropy (Basel, Switzerland)

|December 24, 2025

Summary

This summary is machine-generated.

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This study introduces MaSS-Droid, a novel Android malware detection framework. It enhances security by reducing feature redundancy and improving model stability and accuracy for complex threats.

Area of Science:

Computer Science
Cybersecurity
Machine Learning

Background:

Android malware is a growing threat, challenging user security.
Malware detection faces issues with feature redundancy and unstable single model performance.

Purpose of the Study:

To propose MaSS-Droid, a framework for robust Android malware detection.
To address feature redundancy and improve ensemble model generalization and stability.

Main Methods:

Extracted permission, API call, and opcode sequence features from APK files.
Implemented a three-layer feature screening mechanism to reduce redundancy and complexity.
Utilized an adaptive Stacking integration method (Adaptive-Stacking) to dynamically adjust base classifier weights.

Keywords:

Android malware detection Stacking integration feature selection static analysis

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Main Results:

MaSS-Droid effectively mitigates overfitting and enhances model generalization.
The framework significantly reduces feature redundancy, improving detection accuracy.
Demonstrated enhanced overall stability and accuracy in detecting diverse Android malware samples.

Conclusions:

MaSS-Droid offers a stable and accurate solution for Android malware detection.
The proposed framework effectively tackles challenges posed by feature redundancy and model instability.
Adaptive Stacking integration proves crucial for superior ensemble performance in malware analysis.