Adaptive Android Malware Detection using Machine Learning and Semantic Analysis

 Adaptive Android Malware Detection using Machine Learning and Semantic Analysis

Vishnu K1, Dr. T. Vijaya Kumar2

1Student, Department of MCA, Bangalore Institute of Technology, Karnataka, India

2Professor, Department of MCA, Bangalore Institute of Technology, Karnataka, India

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ABSTRACT:  Android malware poses a significant threat to the security and privacy of users. Traditional signature-based detection methods often fail to recognize new and sophisticated malware, necessitating advanced detection techniques. This paper presents an innovative approach to Android malware detection using machine learning and semantic analysis. By analyzing application permissions and user comments, the system effectively identifies malicious applications. Implemented on a local server, the system ensures high accuracy and efficiency. Extensive testing confirms the system's robustness and user-friendliness. Future enhancements will focus on real-time data integration, advanced machine learning models, and expanded feature sets.

Key Words: Android Malware, Machine Learning, Semantic Analysis, Permission Analysis, Malware Detection, Natural Language Processing, Local Server