Abstract:

COVID-19 (Coronavirus) disease, observed in the city of Wuhan, China, on December 30, 2019, spread worldwide and caused a global epidemic. Since this epidemic can be transmitted very quickly and easily, some precautions and voluntary quarantine practices that governments have to take have significantly changed the habits of world communities in a short time. This change has especially increased distance activities, such as distance working, distance education, and distance shopping (e-commerce). Therefore, people have felt the need to quickly move the physical platforms they use to digital platforms to meet their daily needs. In this case, web phishing targeting digital platforms has led to a significant increase in online cyber attack types. The increase in phishing and the increasing volume of phishing websites have resulted in greater exposure of the world's information and organizations to various cyberattacks. Thus, after the COVID-19 pandemic in 2019, it has become more important than ever to detect phishing website analysis. In this study, performs the web phishing analysis and makes a comparison of classification performances among five popular methods: Random Forest (RF), Support Vector Machine (SVM), Multilayer Perception (MLP), k-Nearest Neighbour (k-NN), and Deep Learning (DL) by utilizing a Waikato Environment for Knowledge Analysis (WEKA) graphical user interface (GUI). In the experiments conducted with the data set divided into two as training and test, the RF and DL methods were more successful than the other methods compared, but k-NN, achieved a better performance when cross-validation was used. The possible reason for this is a simple approach toward deep learning. We hope the current study can provide guidance in investigating WEKA deep learning for web phishing classification.

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