Machine Learning Algorithms for URLs Classification

Phishing is a technique used to collect sensitive data from a user (password or credit card information) for future misuse by posing as a trustworthy source. It often takes advantage of the user’s gullibility in ways that the user will not detect at first look, and in the worst-case scenario, the attacker maintains the user’s data without the user’s awareness. Typically, the URL is the first and simplest piece of information we know about a website. As a result, it is logical to design algorithms for distinguishing harmful from benign URLs. Additionally, accessing and downloading the website’s material may be time-consuming and involves the danger of downloading potentially hazardous information. Machine Learning techniques are used to train a model on a collection of URLs specified as a set of characteristics and then predict and categorize the URLs as benign or dangerous. This technology enables us to identify and avoid possibly dangerous URLs shortly.

I. Introduction

Phishing is a method of obtaining sensitive data (passwords or credit card numbers) from a user by impersonating a trustworthy institution. Most of the time, it takes advantage of the user’s gullibility in a way that the user will not notice at first look, and in the worst-case scenario, the attacker keeps the user’s data without their knowledge [1] .