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

• Main Authors: Noor Hazirah, Hassan, Abdul Sahli, Fakharudin
• Format: Article
• Language: English
• Published: The Science and Information (SAI) Organization Limited 2023
• Subjects: QA75 Electronic computers. Computer science; QA76 Computer software
• Online Access: http://umpir.ump.edu.my/id/eprint/38249/1/Paper_59-Web_Phishing_Classification_Model_using_Artificial_Neural_Network.pdf; http://umpir.ump.edu.my/id/eprint/38249/; http://www.ijacsa.thesai.org; https://thesai.org/Downloads/Volume14No7/Paper_59-Web_Phishing_Classification_Model_using_Artificial_Neural_Network.pdf
• Institution: Universiti Malaysia Pahang

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