https://github.com/gaya3priya/malicious-url-detection-using-ml

Ever clicked a link and regretted it? Malicious URLs can hide malware, steal your data, or ruin your online experience. Sanchalak is your Chrome extension guardian, protecting you from these hidden threats.
https://github.com/gaya3priya/malicious-url-detection-using-ml

chrome-extension css htmls javacript machine-learning php python

Last synced: 2 months ago
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Ever clicked a link and regretted it? Malicious URLs can hide malware, steal your data, or ruin your online experience. Sanchalak is your Chrome extension guardian, protecting you from these hidden threats.

• Host: GitHub
• URL: https://github.com/gaya3priya/malicious-url-detection-using-ml
• Owner: Gaya3priya
• Created: 2024-06-22T06:03:09.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-06-22T06:23:48.000Z (11 months ago)
• Last Synced: 2025-01-09T05:45:56.588Z (4 months ago)
• Topics: chrome-extension, css, htmls, javacript, machine-learning, php, python
• Language: Python
• Homepage:
• Size: 4.48 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        


Malicious URL Detection using Machine Learning

  

  

  


The rise of malicious activities on the World Wide Web poses a threat to users' sensitive information. In 2021, half of all cybercrime victims were targeted by phishing attacks, demonstrating the scale of the problem. The consequences of falling victim to a phishing attack can be severe, including financial loss, identity theft, and reputational damage. 
 


It is a common misconception that if there is a padlock symbol next to the website URL, the site is always safe. The padlock icon only indicates that the communication between the user's browser and the website is encrypted, which helps protect the data from eavesdropping or interception. Scammers previously didn't bother with digital certificates for fake websites, making them easier to spot. However, scammers have become more sophisticated, using fake certificates or exploiting legitimate sites. While the padlock symbol is helpful, users should verify authenticity through other means like checking the URL and trust indicators. 
 


 

 

![image](assets/img1.png)


## Features

| Lexical Features     | Host-based Features   | Content-based Features |

|----------------------|-----------------------|------------------------|

| url_of_anchor        | registration_length   | web_traffic            |

| sub_domain           | age_of_domain         | favicon                |

| having_-             | having_ip             | redirect               |

| links_in_tags        | google_index          | submitting_to_email    |

| sfh                  | dns_record            | statistical_report     |

| request_url          |                       | mouse_over             |

| url_length           |                       | iframe                 |

| https_token          |                       | rightclick             |

| shortening_service   |                       |                        |

| having_@             |                       |                        |

| abnormal_url         |                       |                        |

| having_//            |                       |                        |

## Flowchart

 ![activity diagram](assets/img2.png)

## Tech Stack

**UI:** HTML5, CSS3.

**Backend:** Python3.

**Libraries:** beautifulsoup4, googlesearch-python, scikit-learn, pandas, requests, whois.

## Screnshots

![image](assets/img3.png)

![image](assets/img4.png)

![image](assets/img5.png)

![image](assets/img6.png)

## Future Scope



The developed model for malicious URL detection exhibits impressive results, but improvement is needed, particularly in reducing the prediction time of 30 seconds for real-time detection. Another area for improvement is to handle situations where a website is down or permission to access it is denied. In such circumstances, the current model cannot predict whether the website is malicious or not. This limitation can be addressed by incorporating additional features that can be extracted from other sources, such as WHOIS data or passive DNS information.