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@article{184700,
        author = {Rushikesh Jagtap and Raviraj Jagtap and Anurag Tapkir and Tejas Gholap and Siddhi Waghchoude and Jai Girawale},
        title = {Enhancing Endpoint Security in Android Devices Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {3161-3165},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184700},
        abstract = {The Android operating system commands the largest share of the global mobile device market, making it a prime target for an ever-growing variety of malware and cyber security threats. Traditional endpoint protection methods predominantly rely on signature-based detection, which are limited in identifying zero-day attacks, polymorphic malware, and advanced persistent threats. This research proposes a hybrid machine learning (ML) based approach for enhancing endpoint security on Android devices by combining static and dynamic analysis of application behavior. The framework is designed to be lightweight, enabling efficient on-device execution while preserving user privacy. Our approach demonstrates the feasibility and benefits of integrating ML techniques in mobile endpoint protection, offering improved detection rates and adaptability to evolving threat landscapes.},
        keywords = {Android Security, Endpoint Protection, Hybrid Machine Learning, Malware Detection, Privacy Preservation},
        month = {September},
        }