A Novel Phishing Attack Prediction Model With Crowdsouring in Wireless Networks

Abstract

As the web and applications for knowledge technology developed, many attacks and security problems started to emerge. The last couple of years have seen a significant development in 6G wireless networking. It is challenging to create a secure wireless network. In phishing attacks on wireless networks, attackers create phishing websites that allow users to enter personal information such as usernames, passwords, security numbers, and credit card details. Phishing emails that contain links to websites that are used to spread malware. This project suggests a real time phishing detection plug-in for the web browser which uses a random forest classifier to identify and notify users. As a result, the consumer can get an alert right away. The suggested systems specify wireless phishing attack detection in the current context and produce superior results. The authors proposed 18 traits in order to cover every aspect of phish behavior. With the help of an accepted dataset, the suggested phishing detection system was trained.