Malicious Website Detection Using Machine Learning with Chrome Extension

Abstract: The website security is an important issue that must be pursued to protect Internet users. Traditionally, blacklists of malicious websites are maintained, but they do not help in the detection of new malicious websites. This work proposes a machine learning architecture for intelligent detecting malicious URLs. Forty-one features of malicious URLs are extracted from the data processes of domain, Alexa and obfuscation. ANOVA (Analysis of Variance) test and eXtreme Gradient Boost (eXtreme Gradient Boosting) algorithm are used to identify the 16 most important features. Finally, dataset is used to learn the eXtreme Gradient  Boost classifier, which has a detection accuracy of more than 98%. Keywords: eXtreme Gradient Boosting algorithm; Malicious URL;. Feature Analysis; Chrome Extension