Phishing Website Detection Using Machine Learning

Phishing Website Detection Using Machine Learning

Vaishnavi Indurkar, Shruti Jagtap, Tejasvi Khote, Dhanashri Mane

Padmabhooshan Vasantdada Patil Institute of Technology, Pune,India Department of Computer Engineering

Prof. Soniya Waghmare

Abstract
Phishing Website URL data has become a severe issue in today's society, along with the rapid rise of internet users. People are sending URLs with dangerous links that may damage our system and your system and engage in unlawful and unethical behavior, phishing, fraud, and other activities. The process of setting up a fake profile and URLs is much simpler for Phishing. They prey on gullible victims of these scams by posing as real people in their phishing website URL. Therefore, it's important to recognize Phishing Website URL data. In this research, machine learning methods will be used to detect Phishing Website URL data that are false. The most effective algorithm for Phishing Website URL detection will be determined after discussing machine learning methods and applying them to our data sets. The proposed research detects Phishing Website URLs using a mixed machine-learning approach. The system initially uses natural language processing (NLP) to extract features and train the module. With machine-learning hybrid models, a model for Phishing Website URLs detection is created in the current study. The Phishing Website URL dataset was subjected to various machine learning techniques: Multilayer Perceptron's (ANN), NB, RF and SVM. A comparison study is carried out to verify the effectiveness and performance of the suggested approach.

Keywords— phishing detection, Phishing URL’s, Cyber Attack, Machine Learning classifiers.