CYBER SECURITY FOR IDENTIFICATION OF URL BASED ATTACKS USING IP DATA

Abstract

With the rapid growth of internet usage, cyber threats such as phishing, malware distribution, and malicious URL attacks have significantly increased. Identifying harmful URLs in real time is crucial to ensure user safety and protect sensitive information. This project focuses on the detection of URL-based attacks using IP data by analyzing various characteristics associated with URLs and their corresponding IP addresses. The proposed system utilizes machine learning techniques to classify URLs as either legitimate or malicious based on features such as IP address patterns, domain information, URL length, presence of special characters, and hosting details. By extracting and analyzing these features, the system can effectively identify suspicious activities and potential threats.