Development of an Android-Based Malware Detection Model

Abstract

The study to identify malicious threats in Android mobile phones is presented in this study. The sample Dataset was from DroidFusion-2018—Jupyter Notebook, together with Python for implementation. The techniques considered include the classifiers mentioned in the study. An ensemble of techniques was developed for the study. The Ensemble model achieved 97% accuracy, compared to 71%, 77%, and 79% attained by SVM, KNN, and RF. The study designed a model for detecting malicious Android applications that are integrated into existing malware detection platforms to improve their usage and acceptance.

Keywords

• Android
• malicious threats
• machine learning
• manifest analysis

References

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