Phishing Attack Detection using Collaborative Learning Approach at Dynamic Cloud Platform

The security of personal information belonging to users is a critical concern in the technological community. Fraudsters use a variety of methods to get access to victims' personal information, including their information such as PINs, credit card numbers, & bank account numbers. Various sectors are impacted daily by the abuse of user passwords. Phishing entices more individuals to divulge confidential information Forged websites and emails that mimic the originals are the hackers' tools of choice. There is a variety of anti-phishing learning techniques that employ various approaches to protect people against phishing. Using the proposed method, they may detect phishing at several levels in a dynamic environment. The Proposed process can check emails with embedded links to external websites for validity. There are two ways to look at it. Information on prominent websites is compared to content on well-known reputable sites to determine authenticity. The 2nd step is to examine the relationship quality between the 2 locations by observing when or not any given place increasingly depends on any associated websites. They used virtual passwords for logging into websites to guard against phishing attempts. The proposed model has attained an accuracy of 98.23%, sensitivity of 97.82%, Phishing detection rate of 98.23%, and Recall of 96.73%, which are more improved compared to existing technologies.