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Social Sentry: AI-Based Cyber Attack Detection of Phishing Links and Emails

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dc.contributor.author Muhammad Waleed Raza, 01-136221-023
dc.contributor.author Huzaifa Inam, 01-136221-009
dc.date.accessioned 2026-08-27T06:52:59Z
dc.date.available 2026-08-27T06:52:59Z
dc.date.issued 2025
dc.identifier.uri http://hdl.handle.net/123456789/21655
dc.description Supervised by Dr. Sadia Nazim en_US
dc.description.abstract Phishing is among the most widespread and harmful cyber threats, exploiting human trust to obtain credentials, financial data, and confidential information. This project introduces Social Sentry, an intelligent phishing detection system designed to identify malicious URLs and email content using modern deep learning techniques. The system incorporates two dedicated datasets: one consisting of phishing and legitimate URLs, and another containing phishing and benign email samples. These datasets enable Social Sentry to learn distinct patterns associated with both web-based and text-based phishing attacks. Over time, phishing has evolved into sophisticated campaigns utilizing automation, AI-generated text, cloned websites, and highly deceptive communication patterns. As traditional blacklist-based or rule-driven systems struggle to detect such modern threats, advanced machine-learning models capable of adaptive and context-aware analysis have become essential. The integrated platform includes URL analysis powered by a TabNet classifier and email phishing detection implemented through a Wide and Deep neural network, enabling the system to produce accurate predictions through feature-aware learning. A Python Django REST backend manages prediction requests, authentication, logging, and access to machine-learning models, while a responsive React frontend delivers intuitive real-time interaction. To promote safer user decision-making, Social Sentry incorporates precautionary mea sures that alert users when a prediction is classified as phishing. Instead of integrating any external explanation API, the system provides clear warnings, recommended safety actions, and contextual cues to help users understand potential risks associated with suspicious URLs or emails. A continuous feedback loop allows users to validate results and contribute new samples for model retraining. Experimental evaluation demonstrates strong performance across both datasets, with high AUC scores and reliable real-time inference. Overall, Social Sentry provides a scalable, interpretable, and user-friendly phishing detection solution suitable for educational, corporate, and individual cybersecurity en vironments. Its modular design supports future enhancements such as browser exten sions, multilingual detection, attachment analysis, and integration with additional threat intelligence sources. en_US
dc.language.iso en en_US
dc.publisher Computer Sciences en_US
dc.relation.ispartofseries BS(AI);P-4001
dc.subject Social Sentry en_US
dc.subject Cyber Attack Detection en_US
dc.subject Phishing Links and Emails en_US
dc.title Social Sentry: AI-Based Cyber Attack Detection of Phishing Links and Emails en_US
dc.type Project Reports en_US


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