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@article{175310,
        author = {Nitin Sigilipelli and K J S Upendra and Smily Tappeta and Karuna Kumar Varjiparthi and Sai Kiran Miriyala and Venkata Manindra Guptha Pragallapati},
        title = {ANALYSIS AND DETECTION OF MALWARE IN ANDROID APPLICATION USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {2841-2850},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175310},
        abstract = {Android smart phones are now frequently the subject of sophisticated malware assaults due to their rapid popularity, which presents serious security risks. The use of machine learning (ML) techniques for malware analysis and detection in Android applications is examined in this study. For feature extraction, a large dataset of both malicious and benign apps is used, with an emphasis on permissions, API calls, and behavioral patterns. To get precise predictions, sophisticated classifiers like Support Vector Machines (SVM), Random Forest, and Neural Networks are used. Principal Component Analysis (PCA) and Recursive Feature Elimination (RFE) are two feature selection approaches used to improve detection performance. To increase resilience and scalability, cutting-edge techniques like federated learning, ensemble learning, and graph neural networks (GNNs) are being investigated. The efficiency of the suggested strategy is demonstrated by the experimental findings, which show a high classification accuracy of over 95%. The potential of incorporating ML models into Android security frameworks for real-time malware detection is highlighted by this study. The results open the door for scalable and flexible cyber security solutions that take into account the always changing mobile threat landscape.},
        keywords = {Android malware detection, Machine learning, Feature extraction, Neural networks, Federated learning, Ensemble learning, Cyber security.},
        month = {April},
        }