Detection of Phishing Attack by using LightGBM&Xgbost

Abstract

Phishing attacks provide a significant security risk to both individuals and organizations. To steal sensitive information, these assaults are typically carried out by creating phony websites that substantially resemble actual ones. This research employs these phishing attacks, by using AI techniques that have lately been used to look at the URLs of these phony websites. We have proposed the increasing sophistication and frequency of phishing attacks, highlighting the need for an enhanced AI-based model to detect such attacks effectively. Involves LightGBM, Xgbost, and using a hybrid model of a LightGBM, Xgboost classifier to train and test data for detecting phishing attacks on URLs. There are several feature extraction techniques used to detect URL phishing attacks. To identify if a website is a phishing assault or not, these attributes are then given to a LightGBM and Xgboost classifier. As compared to the previous research model’s accuracy was 93%, Hence The current proposed results of combining training and testing datasets on LightGBM and XgBoost give a 96% accuracy and improve the quality-of-evaluation metrics of the feature of the URLs to detecting phishing attack detection.