Abstract

Phishing is a widely used cyber-attack technique in which users are deceived into visiting illegitimate websites that closely resemble legitimate ones. These fake websites are designed to trick users into revealing sensitive information such as usernames, passwords, bank details, and credit card information. Due to the growing sophistication of such attacks, phishing has become a serious security concern. In the proposed method, we focus solely on analyzing the URL of a website to determine whether it is a phishing site, thereby eliminating the need to visit the website and risk exposure to malicious code. This approach enhances user safety and reduces the chances of infection from harmful scripts or malware embedded in phishing pages. Additionally, we explore how metadata extracted from URLs—such as domain age, presence of special characters, URL length, and redirection patterns—can help in identifying phishing attempts. These features are then used to train a machine learning model using the Random Forest algorithm, which is known for its robustness and ability to handle high-dimensional data without overfitting. By relying on URL-based features alone, this method provides a lightweight and effective solution for phishing detection.