Abstract: Phishing websites pose a severe risk to internet security because they try to obtain private information from gullible visitors. Researchers have created a number of methods, including machine learning algorithms, for identifying phishing websites in order to counter this problem. Large datasets of reputable and phishing websites can be used to train machine learning algorithms to find patterns and traits that differentiate the two. Subsequently, these algorithms can be employed to detect and prevent phishing websites from exploiting users. Feature extraction is one method of machine learning-based phishing website detection in which several aspects of a website, like URL structure, domain age, and content, are examined to detect phishing websites. These methods have the potential to be a valuable weapon in the fight against online phishing assaults with additional study and refinement.

Keywords: : SVM, Xgboost, Gradient boosting, Adaboost, Machine learning techniques

| DOI: 10.17148/IARJSET.2024.11318