Detection of Malicious URLs using Ensemble learning techniques

The growing risk of cyber-attacks and information vulnerability has become a major problem in today’s dynamic digital environment. The necessity for strong security solutions is more critical than ever due to the development of attack techniques and the spread of social engineering strategies. Hackers commonly utilize malicious Uniform Resource Locators (URLs) to trick users and launch different kinds of attacks. This research uses the power of ensemble learning to tackle this problem by mixing various machine learning techniques like XGBoost, Extra Tree Classifier, and Random Forest. This ensemble model not only performs exceptionally well at identifying hazardous URLs, but it also provides a scalable cybersecurity solution, acting as a protector against developing online threats.