Identification of URL-Based Attacks from IP Data

With the rapid expansion of internet usage and web-based services, cyber threats such as phishing, malware distribution, and malicious URL attacks have significantly increased. Attackers often exploit IP-based patterns and URL structures to bypass traditional security mechanisms. This project focuses on identifying URL-based attacks using IP data analysis combined with machine learning techniques to improve detection accuracy and cybersecurity resilience. The system analyzes URLs by extracting features such as IP address patterns, domain behavior, request frequency, and URL structure. By leveraging IP intelligence and classification models, the system can distinguish between legitimate and malicious URLs in real time. This approach enhances traditional URL filtering mechanisms by incorporating behavioral and network-level insights. The proposed solution aims to provide a scalable, efficient, and automated detection system capable of preventing cyber threats before they reach end users. The system can be deployed as a web-based application or integrated into network security tools, contributing to safer browsing environments and improved threat intelligence systems.