Malicious URL detection is a critical task in today's internet landscape. This project aims to develop a machine learning model capable of accurately predicting whether a given URL is malicious or not. By analyzing various features of URLs, such as length, presence of suspicious keywords, and host characteristics, the model will learn to identify patterns associated with malicious websites.
This project will contribute to enhancing internet safety by:
- Protecting users: The model can be used to warn users about potentially harmful websites, preventing them from falling victim to phishing attacks, malware downloads, and other online threats.
- Improving security: Integration with web browsers or security software can enable real-time URL filtering, proactively blocking access to malicious websites.
- Aiding further research: The developed model and the analysis of its effectiveness can contribute to the ongoing development of more sophisticated URL classification techniques.
This project has the potential to significantly improve internet safety and user experience by providing a robust and efficient system for malicious URL detection.