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Machine learning models and dimensionality reduction for improving the Android malware detection.

Pablo Morán¹, Antonio Robles-Gómez¹, Andres Duque²

¹Departamento de Sistemas de Comunicación y Control, Universidad Nacional de Educación a Distancia, Madrid, Spain.

Peerj. Computer Science

|February 3, 2025

Summary

This summary is machine-generated.

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This study enhances Android malware detection using machine learning. Random Forest models achieved 91.72% malware detection with a 0.13% false positive rate, significantly reducing features.

Area of Science:

Computer Science
Cybersecurity
Machine Learning

Background:

Android's widespread use presents numerous cybersecurity vulnerabilities.
Existing Android malware analysis often relies on limited machine learning approaches.
The DREBIN project provides a foundational dataset for Android malware research.

Purpose of the Study:

To develop an efficient dimensionality reduction technique for Android malware features.
To evaluate various supervised machine learning algorithms for malware prediction.
To improve the accuracy and efficiency of Android malware detection systems.

Main Methods:

Utilized the DREBIN dataset for Android malware analysis.
Implemented an efficient feature dimensionality reduction method.

Keywords:

Feature filtering techniques Machine Learning algorithms Predictive goodness metrics Random Forest Supervised feature selection techniques

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Applied and compared multiple supervised machine learning algorithms, including Random Forest.

Main Results:

Random Forest models demonstrated superior performance in malware detection.
Achieved an average malware detection rate of 91.72% with a 0.13% false positive rate.
Reduced feature set to 5,000 (9% of DREBIN features) while maintaining high accuracy (99.52%) and F1-score (96.99%).

Conclusions:

Efficient dimensionality reduction combined with Random Forest offers a highly effective approach to Android malware detection.
The proposed method significantly improves detection rates and reduces false positives compared to previous methods.
This research contributes to more robust and efficient mobile security solutions.