Phishing E-mail Detector

Phishing attacks are a major cybersecurity threat, causing financial losses and data breaches. Conventional spam filters and human detection are not effective against advanced phishing attacks, so AI-based solutions need to be employed. This project creates a Phishing Email Detector based on Python Flask and machine learning to scan the content of emails, identify suspicious patterns, and report potential phishing threats. Through the use of pre- trained data sets and real-time adaptive learning, the system improves detection rates and resists changing phishing techniques. The system under consideration utilizes context- aware detection, behavior link analysis, emotional and tone analysis, and computer vision- based image analysis using AI to detect more efficient fraudulent emails. It derives major email features like headers, body, and included links using machine learning techniques to enhance accuracy. The web application based on Flask provides real-time phishing detection information with a simple interface for advanced email security. The execution takes a systematic methodology, which consists of data input and preprocessing, feature extraction, model training and testing, and deployment. With integration of real-time learning mechanisms and multi-factor email verification, this project is designed to create an intelligent and dynamic anti-phishing system. The primary innovations like context- aware analysis, real-time link behavior observation, and adaptive AI processes contribute to this solution being an efficient and effective counter to contemporary phishing attacks.