Phishing websites are one of the most common cyber threats in today’s digital environment. These websites are designed to imitate legitimate websites in order to deceive users into providing sensitive information such as usernames, passwords, banking details, and other personal data. With the rapid increase in online services and digital transactions, identifying phishing websites at an early stage has become a major challenge in the field of cybersecurity. This project focuses on detecting phishing websites using URL analysis, which is considered an efficient and lightweight method compared to traditional content-based detection techniques. Instead of analyzing the full webpage content, the system studies the structure and characteristics of the website’s URL. Several features are extracted from the URL, including its length, the presence of special characters, the use of IP addresses instead of domain names, the number of subdomains, suspicious keywords, HTTPS usage, and redirection behavior. These characteristics help determine whether a website is legitimate or potentially malicious.Machine learning techniques are used to classify URLs as either phishing or legitimate. A dataset consisting of both phishing and genuine URLs is collected and used for training and testing the model. After performing data preprocessing and feature extraction, a classification algorithm is trained to recognize patterns that are commonly associated with phishing URLs. Once trained, the model can analyze new or unknown URLs and predict whether they are safe or malicious with high accuracy. The proposed system helps protect users from phishing attacks by offering quick and automated detection without the need to access the website’s actual content. This approach makes the system safer and more efficient. Overall, the project demonstrates that combining URL-based analysis with machine learning provides a reliable method for detecting phishing websites and can be integrated into web browsers, email filtering systems, or other cybersecurity tools toimprove online safety.

Ms. Sowmiya, Mr.Rajesh Kumar.B, Mr.Madesh.A,"Phishing Website Detection Using URL Analysis" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(4): Page(323-334) March - April 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.