Phishing Domain Detection ⚡

🪄 About

This is an End-to-End Machine Learning project with deployment. The project aims at developing a model and predict whether the domain is real or fake..

The project was created using Python Flask and deployed in Azure App services.

Link: : https://pishing-domain-checking.azurewebsites.net/

🏋🏻‍♂️ Motivation

Phishing is a type of fraud in which an attacker impersonates a reputable company or person in order to get sensitive information such as login credentials or account information via email or other communication channels. Phishing is popular among attackers because it is easier to persuade someone to click a malicious link that appears to be authentic than it is to break through a computer's protection measures. The mail goal is to predict whether the domains are real or malicious.

📈 DataSource

Phishing Websites Dataset Published: 24 September 2020 | Version 1 | DOI:10.17632/72ptz43s9v.1 | Contributor:Grega Vrbančič

Description

These data consist of a collection of legitimate as well as phishing website instances. Each website is represented by the set of features which denote, whether website is legitimate or not. Data can serve as an input for machine learning process.

In this repository the two variants of the Phishing Dataset are presented.

Full variant - dataset_full.csv

Short description of the full variant dataset: Total number of instances: 88,647 Number of legitimate website instances (labeled as 0): 58,000 Number of phishing website instances (labeled as 1): 30,647 Total number of features: 111

Small variant - dataset_small.csv

Short description of the small variant dataset: Total number of instances: 58,645 Number of legitimate website instances (labeled as 0): 27,998 Number of phishing website instances (labeled as 1): 30,647 Total number of features: 111

Google Drive link to access the Project Docs:

https://drive.google.com/drive/folders/1QPPIWe1lj7g9wUU1M0tqJlZntQHBEXdT?usp=sharing

💻 Web UI

📺 Demo Video

Demo Video Link: https://www.youtube.com/watch?v=Z-ir-Ay82GU

⚙️ SetUp

Step 1 - Install the requirements

pip install -r requirements.txt

Step 2 - Run app.py file

python app.py

To download your dataset

https://data.mendeley.com/datasets/72ptz43s9v/1

Git commands

If you are starting a project and you want to use git in your project

git init

Note: This is going to initalize git in your source code.

You can clone exiting github repo

git clone <github_url>

Note: Clone/ Downlaod github repo in your system

Add your changes made in file to git stagging are

git add file_name

Note: You can given file_name to add specific file or use "." to add everything to staging are

Create commits

git commit -m "message"

git push origin main

Note: origin--> contains url to your github repo main--> is your branch name

To push your changes forcefully.

git push origin main -f

To pull changes from github repo

git pull origin main

Note: origin--> contains url to your github repo main--> is your branch name

🦾 Tools & Technogies Used

✍️ Author

@Amitava Majumder