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@article{178179,
        author = {Mr. Kalluri Akhileshwar reddy and Ms. C Vishnupriya},
        title = {Deep Learning for Phishing Detection: A User-Friendly LSTM Approach to Big Email Data},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {3346-3351},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178179},
        abstract = {The exponential surge in phishing attacks via email continues to jeopardize the digital security landscape. Traditional tactics frequently fail due to the dynamic nature of phishing techniques and the wide variety of email structures. This paper presents a comprehensive strategy to detecting phishing emails that uses Bidirectional Long Short-Term Memory (Bi-LSTM) networks. To increase classification accuracy on large-scale, unlabeled datasets, we introduced a hybrid data labeling and expansion strategy that combines K-Nearest Neighbors (KNN) with K-Means clustering. The system also provides a front-end web interface that allows users to interact with the model in a user-friendly manner. The model achieved up to 95% accuracy throughout evaluation, demonstrating its efficiency in real-world applications.},
        keywords = {Phishing detection, Email security, Bi-LSTM, Deep learning, KNN, K-Means, RNN},
        month = {May},
        }