Using and Comparing Machine Learning Techniques for Automatic Detection of Spam Website URLs

With the developing technology, issue of cyber security has become one most common and current issues in recent years. Spam URLs are dangerous for cybersecurity. widely used attacks to defraud users. These cause users suffer monetary losses, steal private information, install malicious software on their devices. It is very important detect such threats promptly take precautions against these threats. Detection mostly done by using blacklists. However, lists insufficient newly created URLs. In years, machine learning techniques have been developed overcome this deficiency. study, URL classification was made different techniques. 9 classifiers were preferred classification. The performances compared process. addition, similar studies literature comprehensively examined discussed. since preparation data sets natural language processing process a great effect training models, steps discussed detail.