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

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@article{1716219,
      author = {G. Prajith, A. Rakesh Kanna, T. Tarun Kumar, M. Janu},
      title = {Android Malware Detection Using Permission-Based Static Analysis and Machine Learning},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {10},
      pages = {1278-1285},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1716219.pdf},
      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},
      month = {April},
      doi = {https://doi.org/10.64388/IREV9I10-1716219}
  }