Android Malware Detection Using Permission-Based Static Analysis and Machine Learning

• Author(s): G. Prajith; A. Rakesh Kanna; T. Tarun Kumar; M. Janu
• Paper ID: 1716219
• Page: 1278-1285
• Published Date: 14-04-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 10 April-2026

Abstract

The Rapid Growth Of Android Applications Has Significantly Increased The Risk Of Malicious Software Targeting Mobile Users. Traditional Signature-Based Detection Methods Fail To Identify Newly Emerging And Obfuscated Malware. This Paper Presents A Permission-Based Static Analysis Approach For An- Droid Malware Detection Using Machine Learning Techniques. The System Extracts Declared Permissions From Apk Files Using The Androguard Framework And Converts Them Into Binary Feature Vectors. A Random Forest Classifier Is Trained Using An 80–20 Train-Test Split To Classify Applications As Benign Or Malicious. Performance Metrics Including Accuracy, Precision, Recall, F1- Score, And Confusion Matrix Are Evaluated. The System Also Includes A Heuristic Fallback Mechanism Based On Dangerous Permission Thresholds. Experimental Results Demonstrate That Permission-Based Static Analysis Combined With Machine Learning Provides An Efficient And Scalable Approach For Preliminary Android Malware Detection.

Keywords

Android Malware, Static Analysis, Machine Learning, Random Forest, Androguard, Cybersecurity

Citations

IRE Journals:
G. Prajith, A. Rakesh Kanna, T. Tarun Kumar, M. Janu "Android Malware Detection Using Permission-Based Static Analysis and Machine Learning" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 1278-1285 https://doi.org/10.64388/IREV9I10-1716219

IEEE:
G. Prajith, A. Rakesh Kanna, T. Tarun Kumar, M. Janu "Android Malware Detection Using Permission-Based Static Analysis and Machine Learning" Iconic Research And Engineering Journals, 9(10) https://doi.org/10.64388/IREV9I10-1716219