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@article{195870,
        author = {Amol Atmaram Dhumal and Dr. Pravin Haribhau Ghosekar},
        title = {IoT Privacy Protection Through Machine Learning-Based Behavioral Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {1708-1713},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195870},
        abstract = {With the proliferation of Internet of Things (IoT) devices, privacy protection has become a critical concern. Users’ personal information and sensitive data are at risk of being exposed due to their behaviors and interactions with IoT devices. This research focuses on the analysis of user behaviors for privacy protection in the IoT environment using machine learning techniques. Privacy-sensitive behaviors are identified by analyzing data collected from IoT devices; patterns that may compromise user privacy are detected via data pre-processing, anomaly detection, and machine learning- based classifiers. Privacy-preserving mechanisms specific to the IoT environment—such as data encryption, anonymization, and differential privacy—are discussed and integrated with rule- based policy enforcement. The proposed system architecture con- siders scalability and fault tolerance, while security enhancements including access control and intrusion detection strengthen its resilience. Experimental results demonstrate that the proposed behavioral learning framework outperforms conventional static approaches in detection accuracy and adaptive response to evolving threat patterns.},
        keywords = {Internet of Things (IoT), Privacy Protection, Machine Learning, Behavioral Analysis, Privacy-Preserving Mechanisms, Anomaly Detection, Federated Learning},
        month = {April},
        }