URL Shield: Protecting Users from Phishing Attacks using Flask and ML

Cybersecurity threats, especially phishing attacks, continue to pose a significant risk to internet users. In response to this growing menace, we present "PhishShield," a robust and scalable solution that leverages machine learning within the Flask framework to detect phishing URLs effectively. Phish Shield incorporates a comprehensive set of features, including IP address analysis, URL characteristics, and domain-related attributes, which serve as the foundation for our machine learning models. Specifically, we employ Bernoulli naive bayes (Bernoulli NB) and Multinomial naive bayes (Multinomial NB) algorithms to classify URLs into three categories: Phishing, Legitimate, or Suspicious. Our system follows a systematic approach: it first collects and preprocesses datasets, and subsequently trains and tests the machine learning models. Upon successful training, PhishShield generates a pickle file, which can be used to determine whether a URL is susceptible to a phishing attack. The Flask-based web interface offers a user-friendly platform for users to input URLs and obtain instant assessments. Phish Shield's utilization of static CSS and Bootstrap for web design ensures an intuitive and visually appealing user experience. The templates "base," "check," "home," "index," and "result" enhance the usability and interactivity of the system. PhishShield, we aim to empower users to navigate the web securely, providing a critical defense against phishing attacks. Our project not only showcases the capabilities of Flask and machine learning but also contributes to the ongoing battle against cyber threats.