Detection of Malicious Websites using Machine Learning


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

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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.

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