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@article{171901,
        author = {Mr.Sandeep Seetaram Naik and Mohammed Younus and Naveen Tevari and Vipul Durga P and Nr Akash},
        title = {Vulnerability detection in websites},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {1290-1294},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171901},
        abstract = {Vulnerability detection in websites is crucial for safeguarding sensitive data and ensuring system integrity in an era of increasing cyber threats. Traditional detection techniques rely on handcrafted features and rule-based approaches, which often struggle to adapt to the complexity and evolving nature of modern web vulnerabilities. To address these challenges, we propose a novel deep learning-based framework leveraging a customized Convolutional Neural Networks (CNN) architecture. Our approach automatically extracts robust spatial and semantic patterns from website source code and network traffic data without requiring predefined rules or manual feature engineering. The model integrates multiple input channels, including HTML structure, JavaScript behavior, and HTTP request-response patterns, to comprehensively analyze potential vulnerabilities. Experimental validation on a diverse dataset demonstrates the proposed method’s superiority over conventional techniques in terms of accuracy, scalability, and adaptability, paving the way for more efficient and proactive website vulnerability management.},
        keywords = {},
        month = {January},
        }