Phishing is a type of cyber attack where malicious actors attempt to trick individuals into providing sensitive information, such as usernames, passwords, or financial details, often by masquerading as a trustworthy entity in electronic communications.

By using both datasets and URL features, your model aims to identify and flag potential phishing attempts. This involves analyzing various attributes of URLs and other data to distinguish between legitimate and malicious sites or messages. The goal is to enhance security by preventing users from falling victim to phishing schemes, thereby protecting their personal and financial information.

Collect dataset containing phishing and legitimate websites from the open source platforms.

Write a code to extract the required features from the URL database.
Address Bar based Features
Domain based Features
HTML & JavaScript based Feature

Using web scraping and automation to extract URLs directly from browser using selenium and beautifulsoup.

Analyze and preprocess the dataset by using EDA techniques.

Divide the dataset into training and testing sets.

Run selected machine learning and deep neural network algorithms like SVM, Random Forest, XGBoost,
Auto encoder on the dataset.

Write a code for displaying the evaluation result considering accuracy metrics with respect to each feature.

This project can be used in real world by creating website.