As our online presence grows, from online chats to online transactions and information sharing, phishing has turned out to be a significant concern in the world of cybersecurity. The term "phishing" describes a technique where hackers try to trick people into visiting fake websites and then persuade them to reveal their important information, like passwords or bank account numbers. The traditional "blacklist-based" technique, where a website's URL is checked against a list of known phishing websites, may not always be efficient in detecting new phishing websites. This project focuses on designing a machine learning-based phishing website detector that helps identify suspicious websites based on URL-based features. The machine learning algorithm will be trained on a dataset that consists of both phishing and legitimate websites. The features, such as URL length, domain name, presence of special characters, and security features, are extracted from the URLs and then used for classification. A simple web application, developed using the Flask web development library, can be used for this purpose. The results demonstrate that our system provides high accuracy with few false alarms. The approach is simple and flexible and can be easily incorporated into various cybersecurity tools in the real world. In summary, the approach is reliable and provides a solution for detecting phishing sites in the modern web environment.
