Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kiran, Talari Uday; Vigneshwaran, T.; Natarajan, K. Title A name given to the resource A Deep Learning Approach to Phishing Detection Using BiLSTM with an Attention Mechanism Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1612 LNNS;pp.445-457 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-95-2872-1_32" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-95-2872-1_32</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028264891?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028264891?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kiran T.U., Department of AI, ML and Data Science, Christ University, Karnataka, Bengaluru, India; Vigneshwaran T., Department of AI, ML and Data Science, Christ University, Karnataka, Bengaluru, India; Natarajan K., Department of AI, ML and Data Science, Christ University, Karnataka, Bengaluru, India Description An account of the resource Phishing sites are a serious cyber threat as they trick users into revealing sensitive personal data. Conventional detection techniques, including rule-based systems and blocklists, cannot cope with changing phishing tactics. In this paper, a new approach to phishing detection is introduced using a Bidirectional Long Short-Term Memory (BiLSTM) network with an attention mechanism. The suggested model learns and examines URL-based features and identifies forward and backward relationships in data, enhancing classification accuracy. A 30,000 URL-tagged dataset is utilized to train the model, which is then optimized with the help of methods like sequence tokenization, embedding layers, dropout regularization, and class weight balancing to counter data imbalance issues. The BiLSTM layer processes sequential information about URLs in a bidirectional manner, whereas the attention mechanism applies weights to important features differently to ensure the model pays attention to the most critical elements of phishing URLs. The model was tested based on standard performance metrics and has attained an astounding accuracy of 99.22%, precision of 99.1%, recall of 99.3%, and an F1-score of 99.2%, surpassing the traditional approach like Logistic Regression. The model indicates good generalization ability and is possible to be applied in real-time in web security systems. In the future, the use of dynamic data analysis and large datasets will be applied to improve further the detection efficiency and responsiveness against the new emerging phishing attacks. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Attention mechanism; BiLSTM; Machine learning; Phishing websites Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981952871-4; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online