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A static analysis approach for Android permission-based malware detection systems.

Juliza Mohamad Arif¹, Mohd Faizal Ab Razak¹, Suryanti Awang¹

¹Faculty of Computing, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia.

|September 30, 2021

Summary

This summary is machine-generated.

This study enhances Android malware detection using static analysis and machine learning. The Random Forest algorithm achieved 91.6% accuracy, improving mobile device security against evolving threats.

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Area of Science:

Computer Science
Cybersecurity
Machine Learning

Background:

Mobile malware evolution necessitates robust detection methods.
Current security evaluations for Android malware, including static and dynamic analysis, show room for improvement.

Purpose of the Study:

To evaluate the effectiveness of static analysis for Android malware detection using permission-based features.
To compare the performance of various machine learning classifiers for identifying Android malware.

Main Methods:

Utilized a dataset of 5,000 Drebin malware and 5,000 Androzoo benign samples.
Employed machine learning with feature selection to identify distinguishing malware characteristics.
Compared the performance of different classifier sets for Android malware detection.

Main Results:

The Random Forest algorithm demonstrated the highest accuracy in malware detection.
Achieved a True Positive Rate (TPR) of 91.6% using the Random Forest algorithm.
Identified key permission-based features crucial for distinguishing malware from benign applications.

Conclusions:

Static analysis, enhanced by machine learning and feature selection, is effective for Android malware detection.
The Random Forest algorithm offers a highly accurate solution for identifying Android malware.
Permission-based features are valuable indicators for improving mobile security evaluations.