SURVEY ON DETECTION OF PHISHING WEBSITE USING MACHINE LEARNING

Computer Science and Engineering

Phishing is a form of social engineering attack aimed at stealing personal information from individuals through websites or email. This information can include passwords, credit card details, bank account information, and other personal data. Phishing takes place when a malicious actor attempts to impersonate a reputable service provider. Various traditional methods and new techniques are utilized to enhance the detection accuracy and reduce the success rate of phishing websites in stealing information. Several anti-phishing techniques are there such as blacklist, heuristic, visual similarity and machine learning. Machine Learning is efficient technique to detect phishing. It also removes drawback of existing approach. Different algorithms are used with the assistance of Machine Learning techniques to identify Phishing Websites. The main goal of this research is to identify phishing websites through the utilization of Machine Learning techniques. Machine Learning Algorithms like Random Forest, Decision Tree, K-Nearest Neighbors, Gradient Boosting, XGBoost, Support Vector Machine, Naïve Bayes, and more are used to detect phishing websites.

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