Web Cleanser- A Chrome Extension to detect Malicious Websites

Introduction

Web Cleanser was created with the typical internet user in mind, who must navigate among several websites while online and risk falling for a trap from a bad website that may be after the user's information or seeks to infect their machine with malware. Our Chrome plugin allows users to utilise our uniquely trained ML model to determine if a specific website is safe to browse.

• Web Cleanser - A Chrome Extension to detect Malicious Websites
• Introduction
• How it Works ?
  • What Problem it Solves ?
• Tech Stack
• Usage
  • Extension

How it Works ?

The ML model extracts the following features from a url :

Feattures Used
Having IP address	URL Length	URL Shortening service	Having @ symbol
Having double slash	Having dash symbol(Prefix Suffix)	Having multiple subdomains	SSL Final State
URL of Anchor	Links in tags	SFH - Server from Handler.	Submitting to email
Abnormal URL	IFrame	Age of Domain	DNS Record
Web Traffic - using data.alexa.com	Google Index		Statistical Reports

We have iterated multiple times during training phase :

What Problem it Solves ?

In the modern day, every website on the internet seeks to gather user data by duping visitors into divulging their login information for fraud or other vengeful activities. Uninitiated browser users are unaware of the page's back end. Users might be duped into disclosing their login information or downloading malicious files.

We have developed a Chrome extension that will function as middleware between users and harmful websites, relieving users of having to provide their personal information to such websites. Our idea was developed with the typical internet user in mind, who must go through several websites while online and risk falling for a malicious website's trap that aims to steal personal information from users or install malware. Our Chrome extension enables users to determine whether a specific website is secure before visiting it.

Tech Stack

• HTML - The front-end development language used for creating extension.

• CSS - The front-end development language used for creating extension.

• Python - The Programing Language used to parse features from a website and for training/testing of the ML model.

• JavaScript - The scripting language used for creating the extension and sending requests to the served Ml model.

• Php - The scripting language used for serving the Ml model .

• Beautiful Soup - The library used to scrape websites from a url.

• Googlesearch - The library for performing google search's during feature extraction.

• whois - The package for retrieving WHOIS information of domains during feature extraction.

• scikit-learn - The library used for training ML models.

Usage

Project Folder

1. Place the Project Folder in xampp/htdocs/
2. Make sure XAMPP is running before running the Extension
3. Change the address of index.php in the popup.js file.

Extension

1. Go to Chrome Settings by tapping on the three dots availble on the top right corner
2. Select " Extensions "
3. Enable " Developer Mode "
4. Click on " Load Unpacked " and select the " extensions " folder