Zahid, ImranUdatta, Asif Ad-DeenNahid, Naimur RahmanSifat, Shafayet NoorAnoy, Raiyan RafizMiraj, Sayeed Bin2026-08-302026-08-3020262026-01ID 21301630ID 21301709ID 21341002ID 24141230ID 24241329https://hdl.handle.net/10361/29612This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2026.Cataloged from PDF version of thesis.Includes bibliographical references (pages 56-57).Phishing attacks remain one of the most critical cybersecurity threats, exploiting malicious URLs to deceive users and extract sensitive information. Traditional detection approaches, such as blacklist-based and rule-based systems, are increasingly ineffective against rapidly evolving and previously unseen phishing techniques. To address this challenge, this study proposes a hybrid deep learning framework that integrates a supervised transformer-based model (RoBERTa) with an unsupervised Autoencoder for anomaly detection using structured URL features. The RoBERTa model captures contextual and semantic patterns from raw URLs, while the Autoencoder identifies deviations through reconstruction error, enabling detection of unknown phishing behaviors. The framework is evaluated using standard metrics including accuracy, precision, recall, F1-score, and AUC, along with cross-validation to ensure robustness. Experimental results demonstrate that the hybrid model achieves near-perfect performance, significantly outperforming individual approaches while maintaining strong generalization capability. The combination of supervised and unsupervised learning enhances detection reliability and reduces false positives. Overall, this research provides an effective, scalable, and adaptive solution for real-world phishing detection in dynamic cybersecurity environments.57 pagesen-USAttribution-NonCommercial-NoDerivatives 4.0 InternationalBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permissionhttp://creativecommons.org/licenses/by-nc-nd/4.0/Phishing detectionDeep learningAutoencoderTransformer modelsAnomaly detectionPhishing--Detection.Deep learning (Machine learning).Electric transformers.Anomaly detection (Computer security).Thesis