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@article{170472,
        author = {Ragu V and N. Pazhaniraja and Ezhil Kumaran B and Sathish Kumar K},
        title = {Prediction of Phishing websites through Recurrent Neural Network with Autoencoders},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {1239-1249},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170472},
        abstract = {Phishing websites are a growing threat to online security, exploiting users’ trust by imitating legitimate platforms to steal sensitive data such as login credentials and financial information. The prevalence of these malicious sites presents a continuous challenge, as attackers frequently adapt their methods to bypass detection. Traditional defenses, like blacklisting, struggle to keep pace with the rapid evolution of phishing techniques, leaving users more vulnerable to these attacks. This issue is further intensified by the demand for real-time detection and the difficulty of differentiating between authentic and malicious URLs. As phishing tactics become increasingly advanced, it is essential to develop sophisticated, adaptive approaches that can reliably and efficiently identify and counter these threats to enhance online security.},
        keywords = {Phishing website Prediction, Deep Learning, Recurrent Neural Networks, Autoencoders, URL pattern recognition, Cyber Security, Feature Extraction, Data analysis.},
        month = {December},
        }