Abstract
Phishing is an online fraud that deceives visitors by impersonating a legitimate website to steal their confidential or personal information. This is a well-known form of cybercrime. With the aim of detecting phishing sites, several phishing site detection techniques have recently been created. However, it fails to achieve the desired goal and has a large number of drawbacks, including low accuracy, long learning curve, and low-power embedded hardware. For covering such drawbacks, this work proposes an efficient URLs Phishing detection technique. Our technique depends on Software Defined Network (SDN) technology, clustering and feature method, and Conventional Neural Network (CNN) algorithm. Feature selection technique is based on Recursive Feature Elimination (RFE) with Support Vector Machine (SVM) algorithm. The SDN is used to transfer the URLs phishing detection process out of the user's hardware to the controller layer, continuously train on new data, and then send its outcomes to the SDN-Switches. RFE-SVM and CNN are used to increase accuracy of phishing detection. Therefore, the proposal model does not require retrieving the content of the target website or using any third-party services. It captures the information and sequential patterns of URL strings without requiring a prior knowledge about phishing, and then uses the sequential pattern features to quickly classify the actual URL. The experimental results showed that our proposal highlighted the robustness and accuracy in distinguishing between phishing and legitimate sites. Our suggestion achieves 99.5% phishing detection accuracy.