Prediction of phishing websites using machine learning

With the growing popularity of the information science, more application is being integrated with websites that can be accessed directly through the internet. This has increased the possibility of attack by ill-legal persons to steal personal information. To identify a phishing assault, several strategies have been presented. However, there is still opportunity for progress in the fight against phishing. The objective of this research paper is to develop a more accurate prediction model using Decision Tree (DT), Random Forest (RF) and Gradient Boosting Classifiers (GBC) with three features selection techniques Extra Tree (ET), Chi-Square and Recursive Feature Elimination (RFE). Since phishing websites dataset contains 89 features, therefore we have applied extra tree and chi-square, feature selection method to identify the limited important features and then recursive features elimination technique has been used to reduce the dataset up-to optimum important features. We have compared the performance of the developed model using machine learning algorithms and find the best prediction performance using GBC, followed by RF and DT. These algorithmic models capture the trends from various cases of phishing with over R-square, Root Mean Square Error (RMSE), and Mean Absolute Error (MAE), in each case.