• Akshay Pawar
• Rahul Gaigawale
• Suyog Late
• Ankit Sharma
• Prof. Tanvi Deshmukh

Phishing attackers spread phishing links through email, text messages, and social media platforms. They use social engineering skills to trick users into visiting phishing websites and entering crucial personal information. In the end, the stolen personal information is used to defraud the trust of regular websites or financial institutions to obtain illegal benefits. With the development and applications of machine learning technology, many machine learning-based solutions for detecting phishing have been proposed. Some solutions are based on the features extracted by rules, and some of the features need to rely on third party services, which will cause instability and time-consuming issues in the prediction service. In this project, we propose a machine learning-based framework for detecting phishing websites. We have implemented the framework as a browser plug-in capable of determining whether there is a phishing risk in real-time when the user visits a web page and gives a warning message. The real-time prediction service combines multiple strategies to improve accuracy, reduce false alarm rates, and reduce calculation time, including whitelist filtering, blacklist interception, and machine learning (ML) prediction. In the ML prediction module, we compared multiple machine learning models using several datasets.

Phishing, Personal information, Machine Learning, Malicious links, Phishing domain characteristics, Phishing attacks, legitimate, trust worthy

"A Novel Phishing Website Detection Approach Using Machine Learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.h729-h732, May-2023, Available :http://www.jetir.org/papers/JETIR2305791.pdf

"A Novel Phishing Website Detection Approach Using Machine Learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. pph729-h732, May-2023, Available at : http://www.jetir.org/papers/JETIR2305791.pdf