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@article{195088,
        author = {V.Ankitha Venkat and Matru Dhanavath and B.Sowmya and N.Naga Sahithi and A.Kavya Reddy},
        title = {ENHANCING TRUST IN ONLINE BROWSING ENVIRONMENTS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {6978-6987},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195088},
        abstract = {With the rapid growth of web-based applications, browser security has become a critical concern due to increasing threats such as session hijacking, clickjacking, and malicious redirections. These attacks compromise user privacy and integrity by exploiting vulnerabilities in browsers and web applications. This paper presents a real-time browser hijacking detection system aimed at enhancing trust in online browsing environments through behavioral analysis and machine learning techniques.
The proposed system monitors user browsing activities using a lightweight browser extension that captures behavioral features such as click intervals, scroll patterns, and redirect counts. These features are transmitted to a backend server where they undergo preprocessing and feature extraction. A Random Forest classifier is employed to distinguish between normal and hijacked browsing sessions based on trained behavioral patterns.
The system incorporates a real-time monitoring dashboard that visualizes user activity, detection results, and trends over time. In addition, an alert mechanism is implemented to notify users instantly when suspicious behavior is detected, thereby minimizing potential security risks. The system is designed to operate with low latency and high accuracy while ensuring user privacy by avoiding sensitive data storage.
Experimental evaluation demonstrates that the proposed approach effectively detects anomalous browsing behavior with high confidence, providing a scalable and efficient solution for browser security. By integrating behavioral analytics, machine learning, and real-time alerting, the system significantly enhances user trust and safety in online environments.},
        keywords = {},
        month = {March},
        }