Phishing website is one of the internet security problems that target the human vulnerabilities rather than software vulnerabilities. It can be described as the process of attracting online users to obtain their sensitive information such as usernames and passwords.The objective of this project is to train machine learning models and deep neural network on the dataset created to predict phishing websites. Both phishing and legitimate URLs of websites are gathered to form a dataset and from them required URL and website content-based features are extracted. The performance level of each model is measured and compared.

phishing URL Dataset The set of phishing URLs are collected from opensource service called PhishTank. This service provide a set of phishing URLs in multiple formats like csv, json etc. that gets updated hourly. To download the data: https://www.phishtank.com/developer_info.php. From this dataset, 5000 random phishing URLs are collected to train the ML models.

legitimate URL Dataset The legitimate URLs are obatined from the open datasets of the University of New Brunswick, https://www.unb.ca/cic/datasets/url-2016.html. This dataset has a collection of benign, spam, phishing, malware & defacement URLs. Out of all these types, the benign url dataset is considered for this project. From this dataset, 5000 random legitimate URLs are collected to train the ML models.

The above mentioned datasets are uploaded to (https://github.com/goodydeves/PhishBuster/tree/master/ML%20work/DataSets)' folder of this repository.

The below mentioned category of features are extracted from the URL data:

1. Address Bar based Features In this category 9 features are extracted.
2. Domain based Features In this category 4 features are extracted.
3. HTML & Javascript based Features In this category 4 features are extracted.

So, all together 17 features are extracted from the 10,000 URL dataset and are stored in file in the DataFiles folder. The features are referenced from the https://archive.ics.uci.edu/ml/datasets/Phishing+Websites.

Before stating the ML model training, the data is split into 80-20 i.e., 8000 training samples & 2000 testing samples. From the dataset, it is clear that this is a supervised machine learning task. There are two major types of supervised machine learning problems, called classification and regression.

This data set comes under classification problem, as the input URL is classified as phishing (1) or legitimate (0). The supervised machine learning models (classification) considered to train the dataset in this project are:

• Decision Tree
• Random Forest
• Multilayer Perceptrons
• XGBoost
• Autoencoder Neural Network
• Support Vector Machines

All these models are trained on the dataset and evaluation of the model is done with the test dataset.

The model XGBoost has the highest accuracy and also The listed (feature extraction) to validate URLs were integrated to a webapplication using django framework to effeciently detect phishing URL links.

Tools Used ->Jupyter notebook ->Pycharm ->Postman ->Chrome Browser

->>> Presentation

The short video presentaion for this project named "Project Summary.mp4" The slide presentaion for this project is named "presentation slides.ppt" The Write-up Documentation for this project is named "Chapter 1-5 DETECTING PHISHING WEBSITES USING MACHINE LEARNING.pdf"

-> Webapplication I created a Web application named "Phishbuster" to help Users Validate Website Links. Name of folder is "Project_Webapp.7z"

-> ML work The ML work folder consist of "Dataset" and "Jupyter notebook of the project" as stated above ** Phishing Website Detection Training & Testing Models on Datasets.ipynb ** ** URL_Feature_Extraction_from_Datasets.ipynb **

-> Next Steps This project can be further extended to creation of browser extention (for chrome, brave, opera mini etc) to help Users by adding it to browser extension to detect Phishing URLs .