Authors :
S Ashok Kumar; Dr D Brindha
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/2shaxv3y
Scribd :
https://tinyurl.com/4emx3d2u
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAR1199
Abstract :
Finding dangerous websites has grown more
important as online risks have multiplied in order to
protect users' security and privacy. This research uses
machine learning techniques to providea new method for
spotting dangerous websites. In order to build a strong
classifier that can differentiatebetween websites that are
harmful and those that arebenign, the suggested approach
makes use of a wide range of variables that are taken from
user behavior,network traffic, and website content.
Analyzing a variety of parameters, including domain age,
IP repute, URL structure, HTML content, SSL certificate
information, and user interaction patterns,is part of the
feature extraction process. These characteristics offer
insightful information about the behavior and
characteristics of websites, which helps the classifier
distinguish between dangerous and legitimate entities.
Finding dangerous websites has grown more
important as online risks have multiplied in order to
protect users' security and privacy. This research uses
machine learning techniques to providea new method for
spotting dangerous websites. In order to build a strong
classifier that can differentiatebetween websites that are
harmful and those that arebenign, the suggested approach
makes use of a wide range of variables that are taken from
user behavior,network traffic, and website content.
Analyzing a variety of parameters, including domain age,
IP repute, URL structure, HTML content, SSL certificate
information, and user interaction patterns,is part of the
feature extraction process. These characteristics offer
insightful information about the behavior and
characteristics of websites, which helps the classifier
distinguish between dangerous and legitimate entities.
