Detection and Prevention of Phishing Short URLs Using Machine Learning and Blacklist Approaches

Phishing attacks are a common and serious issue in our digital age, short uniform resource locators are frequently used in these attacks to trick unwary visitors into visiting malicious websites. Short uniform resource locators are often used to hide a link's true destination, making it harder for visitors to establish whether a link is legitimate or phishing. Due to this, individuals and organizations attempting to protect themselves from phishing attempts have a significant problem. This research introduces a novel system that integrates machine learning algorithms with a blacklist approach to enhance phishing detection. The system's objective is to support transparency protect user privacy, and increase the precision and efficiency of identifying phishing attacks hidden behind Short URLs, thereby granting users real-time protection against phishing attacks. The findings demonstrate that the proposed system is highly effective. Many machine learning algorithms were used and compared, Gradient Boosting emerged as the best algorithm among those tested, with an excellent accuracy rate of 97.1%. This algorithm outperformed other algorithms in distinguishing between legitimate and phishing uniform resource locators, demonstrating its strong capabilities in the face of the growing threat landscape of phishing attacks via short uniform resource locators. By addressing gaps in prior research, particularly in detecting phishing using short URLs, this study provides a valuable contribution to cybersecurity.