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Project # 121
PhishNet
| Team Lead: | Rabib Husain |
| Other Team Members: | Ashwani Rai | Truc-Vy Hoang | Manan Shah | Shivam Khokhani |
| Faculty Advisor(s): | Chris Conly |
| Department: | CSE |
| Project Type: | Senior Design |
| Title: | PhishNet |
| Abstract: | Phishing emails remain one of the most common and effective cyberattack vectors due to their reliance on social engineering and user curiosity. Traditional defenses primarily focus on blocking or flagging suspicious messages, which limits user understanding of the attacker’s intent and provides little educational value. This project presents PhishNet, an AI-powered phishing analysis and safe preview platform that enables users to inspect suspicious emails without interacting with active malicious content. The system ingests emails through Gmail integration or uploaded .eml files, parses their contents, and applies machine learning techniques to assess phishing risk. Detected phishing messages are rewritten into neutral, safe text that removes emotional manipulation, masks sensitive information, and replaces malicious links with placeholders. In addition, potentially dangerous URLs are executed in an isolated sandbox environment to capture screenshots, sanitized page content, and indicators of compromise for further analysis. The platform displays results through a dual-panel interface that presents both the original sanitized email and the AI-generated rewrite. This approach enhances user awareness, supports security analysis, and introduces a novel method for transforming harmful content into safe, interpretable representations. |
| Poster: | View Poster #121 (PDF) |
| Judges Scoring: | Submit Evaluation (judges only) |