Machine Learning-Based Malicious URL Detection System

Cyber threats have been heightened due to the need to have effective ways of detecting and thwarting malicious links that are often employed to perform phishing, malware downloading, and unauthorized data access. This paper describes a machine learning-based approach to detecting malicious URLs, utilizing a substantial amount of lexical, structural, and semantic data. It was verified using the random forest, extra trees, and XGBoost classifiers on a balanced dataset, which had been trained using the system. The experiments displayed that our model is efficient, accurate, and has low error rates. It classified URLs into five categories, namely benign, defacement, legitimate, malware, and phishing. It offers a scalable and effective method for identifying dangerous links at the initial stage and raising cybersecurity.