https://github.com/chathushi-r/safe-net

This repository contains the source codes for a web application, SafeNet, a malware detection tool that allows users to scan urls/PE files with the aid of machine learning models to check if it is malicious or benign.
https://github.com/chathushi-r/safe-net

flask html-css-javascript mysql-database node-js python web-application

Last synced: 6 days ago
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This repository contains the source codes for a web application, SafeNet, a malware detection tool that allows users to scan urls/PE files with the aid of machine learning models to check if it is malicious or benign.

• Host: GitHub
• URL: https://github.com/chathushi-r/safe-net
• Owner: chathushi-r
• License: mit
• Created: 2024-05-09T13:29:32.000Z (6 months ago)
• Default Branch: master
• Last Pushed: 2024-08-14T13:12:48.000Z (3 months ago)
• Last Synced: 2024-08-14T14:36:50.586Z (3 months ago)
• Topics: flask, html-css-javascript, mysql-database, node-js, python, web-application
• Language: JavaScript
• Homepage:
• Size: 34.7 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
SafeNet is a web application that acts as a malware detection tool for scanning URLS/PE files. Users are allowed to register, login and scan PE files or URLs to check if it is malicious/benign.

In this implementation, ML classification models are constructed to aid in the processing of the system's two key functionalities: URL scanning and PE file scanning. This application connects to ML models via a Flask API, which allows for URL/PE file scanning. The Python script provides the fundamental scanning functionality, with Flask serving as the intermediary for communication between the web backend and the ML models. In addition, a database is connected that will store all the user details and scanned history of PE files/URLs.

This application will allow the user to signup and login as a client/admin. Thereafter, the user will be directed to a dashboard that will consist of user details, user history and the options to upload urls/PE files for scanning.

After a PE file/url is uploaded for a scan, the system provides a result whether the it is malicious/benign.

If the PE file/url is malicious, the system provides the following details:

- Timestamp of the scan

- General safety recommendations on how to handle malicious files/urls

- General information resources for the user to gain some understanding on malicious files/urls.

If the PE file/url is benign:

- Timestamp of the scan

- For a safe url, a clickable link to direct to that uploaded safe url.

- PE file features and URL features

Technologies used:

- To build the web front-end: HTML/CSS/JavaScript

- To build the web back-end: Nodejs, Express, Flask, Python

- To build the database: MySQL

- To build the ML models for scanning: Python 

IDEs used:

- Visual Studio Code

- Jupyter Notebook

- Google Colab