• Shubham Jadhav
• Mr. S. K. Patil
• Ms. M. A. Waghmode
• Rushikesh Kadam
• Aakash Nirhali

Phishing is the most straight forward method of obtaining sensitive information from unsuspecting consumers. The goal of phishers is to obtain sensitive information such as usernames, passwords, and bank account numbers. People working in cyber security are now looking for reliable and consistent detection strategies for phishing websites. The purpose of this work is to use machine learning to detect phishing URLs by extracting and evaluating various aspects of authentic and phishing URLs. Phishing websites are detected using Decision Tree, Random Forest, and Support Vector Machine algorithms. The goal of the study is to detect phishing URLs and to narrow down the best machine learning method by analysing each algorithm's accuracy rate, false positive rate, and false negative rate. Phishing, Feature Classification, Random Forest Classifier, and other terms are used in this article.

Phishing, Extreme Learning Machine, Feature Classification, etc.

"“STUDY OF FAKE WEBSITES DETECTION USING MACHINE LEARNING”", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 8, page no.477-481, August-2022, Available :http://www.jetir.org/papers/JETIRFP06084.pdf

"“STUDY OF FAKE WEBSITES DETECTION USING MACHINE LEARNING”", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 8, page no. pp477-481, August-2022, Available at : http://www.jetir.org/papers/JETIRFP06084.pdf