: In recent years, advancements in Internet and cloud technologies have led to a significant increase in electronic trading in which
consumers make online purchases and transactions. This growth leads to unauthorized access to users’ sensitive information and damages the
resources of an
enterprise. Phishing is one of the familiar attacks that trick users to access malicious content and gain their information. In terms of website
interface and uniform resource locator (URL), most phishing webpages look identical to the actual webpages. Various strategies for detecting
phishing websites, such as blacklist, heuristic, Etc., have been suggested. However, due to inefficient security technologies, there is an
exponential increase in the number of victims. The anonymous and uncontrollable framework of the Internet is more vulnerable to phishing
attacks. Existing research works show that the performance of the phishing detection system is limited. There is a demand for an intelligent
technique to protect users from the cyber-attacks. In this study, the another proposed a URL detection technique based on machine learning
approaches.A recurrent neural network method is employed to detect phising URL.Researcher evaluvated the proposed method with 7900
malicious and 5800 legitimate sites,respectively.The experiments outcome shows that the proposed method performance is better than the recent
approaches in malicious URL detection.
Keywords-Logistic Regression,Multinomial Naïve Bayes,XG Boost.