In today's digital age, the internet is an essential tool for communication and information sharing. However, it also poses various security risks, with malicious actors often concealing harmful content behind shortened URLs. To address this challenge, we propose a project that combines web scraping, URL lengthening, and malicious URL detection using the power of Gradient Boosting.

The primary goal of this project is to create a robust system capable of performing the following tasks:

1. Web Page Scraping: Develop a web scraping module capable of retrieving web pages, with a focus on platforms like LinkedIn, which often contain shortened URLs in user profiles, posts, or comments.

2. URL Lengthening: Implement a URL lengthening mechanism to expand shortened URLs, making them easier to analyze. This process may involve using publicly available URL lengthening services or custom algorithms.

3. Malicious URL Detection: Utilize Gradient Boosting, a powerful machine learning algorithm, to build a model that can classify URLs as either benign or malicious. This model should be trained on a diverse dataset of known malicious URLs and legitimate URLs to ensure accuracy.

4. User Interface: Develop a user-friendly interface that allows users to input web page URLs or text containing URLs. The interface should display the results of URL expansion and the classification of each URL as either safe or malicious.

5. Reporting and Alerting: Implement a reporting and alerting system to notify users of potentially malicious URLs, ensuring quick response and mitigation of security threats.

• Web scraping libraries (e.g., BeautifulSoup)
• URL lengthening services or custom algorithms
• Gradient Boosting machine learning framework (e.g., XGBoost)
• Python for programming and model development
• User interface development tools (e.g., HTML, CSS, Flask)
• Database for storing URL and classification data
• Security measures for handling potentially malicious URLs

• Enhanced Security: The system will help users identify potentially harmful URLs, protecting them from phishing attacks, malware, and other online threats.
• Efficiency: Automating the URL expansion and classification process will save users time and effort.
• Scalability: The system can be expanded to include more advanced features and additional URL lengthening and classification algorithms.
• Learning Opportunity: This project provides an excellent opportunity to gain experience in web scraping, machine learning, and web application development.

The "Web URL Detection and Malicious URL Detection using Gradient Boosting" project aims to make the internet a safer place by automating the process of identifying and classifying potentially malicious URLs hidden behind shortened links. By combining web scraping, URL lengthening, and machine learning techniques, this project will empower users to navigate the web with greater confidence and security.