Phishing Website Prediction: A Machine Learning Approach

Abstract

Phishing is an act of stealing our precious, personal, sensitive data such as credentials that which we use for accessing the resources and services, available across the cyberspace. Seeing these rapidly growing phishing attacks and their adverse effect on the businesses including individual users, it has now become a need for the organizations and individuals worldwide to be able to effectively predict the phishing website and differentiate them from legitimate ones. The aim of this research paper is to efficiently predict the phishing websites so that users may be benefitted from this study and prevent them from getting trapped. In this paper, machine learning techniques are used for prediction. Data mining is used worldwide by almost every face of the society viz. business organizations, govt. organizations, and other kind of data collectors to extract knowledge from the collected data. On the other hand, machine learning is a data mining technique that is used to analyze, classify the data, and efficiently predict the results for the estimation and planning by all of the organizations all around the globe. Classification algorithms, namely logistic regression, decision tree, and random forest classification, are used to predict the fake websites and presented their comparison of their predictions achieved. The results have been presented in numeric format as well as graphically with the help of chart. The data used is taken from UCI machine learning online repository. The seed value is changed and analyzed, and results achieved are least accuracy of 95.93, 97.96, and 98.78% of accuracy as the highest as well. Some future study and applying some good practices may help in designing a better and more accurate solution for the prediction of the phishing website, just by examining the URL and its features.