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.
Challenge lies in differentiating between legitimate and malicious domains. Performed data exploration, cleaning, feature engineering, model building, and testing.

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

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.

• Machine Learning Technology scikt-learn
• Python

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

• Cloud platform Azure is used for hosting the solution.

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

• WireFrame

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

 

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

python -m venv <name of virtual environment>

or

conda create -n <name of virtual environment> python=3.9

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

pip install -r requirement.txt

python app.py

localhost:5000/predict

or

urlphishingdetection.azurewebsites.net/predict

{
  "url" : "https://www.example.com"
}

localhost:5000/predict

or

urlphishingdetection.azurewebsites.net/fetch

For more details, refer to the GitHub repository for the project.
If you are building this project locally, then new cassandra secret, token and clientid with keyspace and table will be required.