Comparative Study on Malicious URL Using Classifiers and Boosting Algorithms

The expeditious hike of the Internet has impelled an expansion in the number of malicious URLs. These URLs, when clicked, can infect a computer with malware, steal sensitive information, or redirect to phishing sites. As a result, malicious URL detection has become a crucial aspect of Internet security. We broadly survey techniques and challenges in detecting malicious URLs. In this paper we have used seven classifiers and five boosting algorithms such as Gaussian NB, decision tree, random forest, KNN, AdaBoost, ExtraTrees, gradient boosting, categorical boosting, logic regression, SGD, XGboost, and Light GBM.