A Model for Malicious Website Detection Using Feed Forward Neural Network

Abstract

Malicious websites are the most unsafe criminal exercises in cyberspace. Since a large number of users go online to access the services offered by the government and financial establishments, there has been a notable increase in malicious websites attacks for the past few years. This paper presents a model for Malicious website URL detection using Feed Forward Neural Network. The design methodology used here is Object-Oriented Analysis and Design. The model uses a Malicious website URLs dataset, which comprises 48,006 legitimate website URLs and 48,006 Malicious website URLs making 98,012 website URLs. The dataset was pre-processed by removing all duplicate and Nan values, therefore making it fit for better training performance. The dataset was segmented into X_train and y_train, X_test and y_test which holds 60% training data and 40% testing data. The X_train contains the dataset of malicious and benign websites, while the y_train holds the label which indicates if the dataset is malicious or not. For the testing dataset the X_test contains both the malicious and non-malicious websites, while the y_test holds label which indicates if the dataset is malicious or not. The model was trained using Feed Forward Neural Network, which had an optimal accuracy 97%.