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A Novel Phishing Attack Prediction Model With Crowdsouring in Wireless Networks
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Author(s): Senthilkumar Subramanian (University College of Engineering, BIT Campus, Anna University, Tiruchirappalli, India), Nithya Venkatachalam (University College of Engineering, Villupuram, Anna University, India)and Regan Rajendran (University College of Engineering, Villupuram, Anna University, India)
Copyright: 2023
Pages: 21
Source title:
Perspectives on Social Welfare Applications’ Optimization and Enhanced Computer Applications
Source Author(s)/Editor(s): Ponnusamy Sivaram (G.H. Raisoni College of Engineering, Nagpur, India), S. Senthilkumar (University College of Engineering, BIT Campus, Anna University, Tiruchirappalli, India), Lipika Gupta (Department of Electronics and Communication Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University, India)and Nelligere S. Lokesh (Department of CSE-AIML, AMC Engineering College, Bengaluru, India)
DOI: 10.4018/978-1-6684-8306-0.ch003
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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.
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