Phishing URL Unmasking Using Ensemble Intelligence to Support Cyber Security

URL (Uniform Resource Locator) phishing is considered as a cyber crime or cyber attack through which the cybercriminals influence the people to click on the links which redirects to the fake website and giving away sensitive data. So, it is very much essential to ensure the cyber security by detecting such activities. In this work, an ensemble intelligence based approach is proposed to unmask the phishing URL. This work is focused on the staking of ensemble intelligence models such as XGBoost and CatBoost as base models and AdaBoost as the meta model to predict such fraudulent activities. The proposed method is compared with other models such as random forest (RMF), Gradient Boosting Machines (GBM), Bagging, Voting, XGBoost, CatBoost and AdaBoost in terms of accuracy (ACU), precision (PRC), recall (RCL) and F1 score. From the results, it is observed that the proposed method is able to identify the phishing URL activity in a better way as compared to other methods. This work is carried out using jupyter notebook 7.