Author Affiliations
[1] Associate Professor, Dept. of CSE Er. Perumal Manimekalai College of Engineering, Hosur, Krishnagiri, Tamil Nadu.
[2] [3] [4] [5] Dept. of CSE Er. Perumal Manimekalai College of Engineering, Hosur, Krishnagiri, Tamil Nadu.
Abstract
Phishing is one of the most dangerous and rapidly growing cyber threats in the digital world. Attackers use deceptive techniques such as fake emails and malicious URLs to trick users into revealing sensitive information, including passwords, banking details, and personal data. Traditional detection methods are often ineffective in identifying new and evolving phishing techniques. This project presents an AI-based Phishing Detection System that is capable of detecting both phishing URLs and phishing emails. The system integrates rule-based detection techniques for analyzing URLs and machine learning algorithms for classifying email content. The application is developed using Python and Flask framework, providing a simple and interactive web interface for users. The system evaluates the given input and generates a phishing risk score based on the likelihood of malicious intent. The project is deployed as a web application, allowing users to access it in real-time.
Keywords - Phishing Detection, Machine Learning, Naive Bayes, TF-IDF, Cyber Security, URL Analysis, Email Classification, Flask, Risk Score, NLP
How to Cite This Article
Pamila K, Poornimadevi V M, Mythili S, Sreelekha P, Subiksha R (2026). Phishing Email and Malicious URL Detection System. International Journal of Innovative Research in Multidisciplinary Education & Technology (IJIRMET), 11(3).