Abstract : Phishing is an attack on gullible people by making them disclose their personal and unique information. It is a cyber-crime where false sites attract exploited people to give delicate data. This paper describes the various techniques for detecting phishing websites by analyzing different attributes of URLs with the help of ML techniques.This experimentation discusses the techniques used for detecting phishing websites by extracting their features like URL length, port, HTTPS token and many more. We have used data mining techniques for the extraction of the features of an URL in order to get a clear image of URL's structure that spread phishing. To protect the end users from entering these types of phished websites, we can try to predict whether an URL is phished or not. A challenge in this field is that attackers are constantly making new strategies to tackle our defensive methods. To continuously update our system in this domain, we need ML algorithms that adapt to new instances and features of phishing URLs.

Keywords - phishing , anti-phishing , machine learning , cyber-crime , cyber-attack

| DOI: 10.17148/IARJSET.2021.8831