Web Phishing Classification Model using Artificial Neural Network and Deep Learning Neural Network

Phishing is an online crime in which a cybercriminal tries to persuade internet users reveal important and sensitive personal information, such as bank account details, usernames, passwords, social security numbers, the phisher, usually for mean purposes. The target victim of fraud suffers financial loss, well loss information reputation. Therefore, it essential identify effective approach phishing website classification. Machine learning approaches have been applied classification websites recent years. objectives this research are classify using artificial neural network (ANN) convolutional (CNN) then compare results models. This study uses dataset collected from machine database, University California, Irvine (UCI). There were nine input attributes three output classes that represent types either legitimate, suspicious, or phishing. data was split into 70% 30% training testing purposes, respectively. indicate modified ANN with Rectified Linear Unit (ReLU) activation function model outperforms other models by achieving least average root square error (RMSE) value 0.2703, while CNN produced RMSE 0.2631. Sigmoid obtained highest 0.3516 0.3585 testing.