Mischievous URL Prediction Through Supervised Machine Learning Algorithms

Mischievous URL (Uniform Resource Locator) is a usual ordinary common and one of the critical cybersecurity threats. They are often used in phishing attacks, where attackers trick users into clicking on a link that leads to a fake website designed to steal sensitive information such as login credentials or credit card numbers. Machine learning techniques can be effective in detecting and preventing these types of attacks. By analyzing patterns in the URLs, machine learning models can identify malicious URLs with a high degree of accuracy. To enhance and boost the generalization of mischievous URLs, certain machine learning mastering and acquiring methods and techniques are being investigated and explored with growing attraction and interest in current new, latest, and upcoming years. This paper represents an up-to-date review of all significant algorithms employed for detecting mischievous URLs,out of which Random forest have achieved highest accuracy. Moreover, various issues in formulating different algorithms for detecting mischievous URL problems are discussed.