TruthGuard: Verifying Website Authenticity

Authors: Anugunj Barange, Tanishka Pandagre, Parul Pawar, Pooja Makode, Khushi Waghmode, Prof. Nilesh Mishra

Country: India

Full-text Research PDF File:   View   |   Download

Abstract: With the Internet and improvement of intensive merging of social life, the Internet looks different that people are learning and jobs, in the meantime, we need to open to increase serious security attacks. Ways to identify different network threats, especially not first seen attacks, is a primary question that needs to be seen immediately. URL for phishing spots aims to collect personal information such as user identification, password and online money -related exchanges. The web uses websites that are visually and semantic as authentic websites. Since most of the customers go online to get the administration provided by the authorities and money -related organizations, there has been a significant increase in phishing hazards and attacks over the years. As technology increases, the phish methods have begun to make severe progress, and it should be avoided by using anti-phishing techniques to detect phishing. Machine learning is an official unit that can be used to target the phish attack. This study develops and creates a model that can guess if a URL connection is valid or phishing
People for cyber security are now looking for reliable and stable identity techniques to detect phishing sites. By removing and evaluating many aspects of authentic and phishing URL, the project uses machine learning techniques to detect phishing URL.
Finally, the study provided a model for phishing and URL classification in a valid URL. It will be very valuable to identify the phish attacks by certifying any link that is delivered to them to prove its validity.

Keywords: Security Attacks, Personal Information, Phishing Hazards, Anti-Phishing Techniques, Machine Learning, Cyber Security, Valid URL

Paper Id: 232313

Published On: 2025-03-23

Published In: Volume 13, Issue 2, March-April 2025

Share this