Phishing Domain Detection Project

Project Overview

This project focuses on developing a machine learning solution for detecting phishing domains. Phishing, a prevalent form of cyber fraud, involves attackers impersonating reputable entities to obtain sensitive information. The primary goal is to predict whether domains are real or malicious, thereby enhancing cybersecurity measures.

Problem Statement

The challenge lies in differentiating between legitimate and malicious domains. Traditional machine learning tasks such as data exploration, cleaning, feature engineering, model building, and testing are employed to address this issue.

Dataset

• Paper Link: Phishing Websites Dataset
• Dataset Link: Mendeley Dataset

Approach

The project employs various machine learning algorithms tailored to the problem at hand. Feature engineering includes URL-based, domain-based, page-based, and content-based features later decided to used Random Forest.

Technologies Used

• Machine Learning Technology scikt-learn
• Python

Database

• Cassandra database is utilized for this project saving every transaction as history and saving datbase for safe and phising url.

Cloud Platform

• Cloud platform Azure is used for hosting the solution.

Logging

• Python logging library is employed for logging every action performed by the code. finally logs are being saved in GitHub Repository Logging.

Wireframe

• WireFrame

Solution Design

• Complete solution design strategies High-Level Design (HLD) and Low-Level Design (LLD) documents.

 

To Build this project

git clone https://github.com/rishabh11336/iNeuron-Internship-Phishing-Domain-Detection.git

create virtual enviroment

python -m venv venv

Learn more about virtual enviroment refer to

https://medium.com/@asusrishabh/requirements-txt-in-python-947b0b43bbe6

use requirement.txt in Phishing Domain Detection App to install compatible packages

pip install -r requirement.txt

python app.py

For more details, refer to the GitHub repository for the project.