A Machine Learning based Approach to Detect Phishing Attack

Phishing is a well-known cybercrime that is increasing rapidly day by day. Phishing involves sending emails with suspicious links or their attachments that can accomplish a number of tasks and includes credentials or personal account information of the individual who experienced harm. These emails hurt the recipients, result in financial loss, and steal identities. Most fraudulent websites design the exact interface and universal resource locator (URL) as the legitimate websites. As anti-hacking persons are now in search for trustworthy and stable phishing detection methods in URLs. In this paper, the focus is on utilizing machine learning technology to identify phishing URLs. The approach involves analyzing a range of features present in both legitimate and phishing URLs. In order to identify phishing websites URLs, we use Decision Tree, random forest, Support vector machine and other useful machine learning algorithms. The conclusion of the studies shows that the considered methods perform more effectively in detecting suspicious URLs than more recent methods.