International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Monitoring and Management of Phishing and Malicious Websites using Machine Learning

Author(s) Tejal Sandip Gite, Sanjana Keshav Jagtap, Komal Gorakh Godse, Sakshi Yogesh Amrutkar, S.H.Sangale
Country India
Abstract - Phishing internet sites are one of the internet protections issues that focus on human vulnerabilities rather than software program vulnerabilities. It can be defined because of the process of attracting online users to gain their touchy facts which include usernames and passwords. In this paper, we provide a sensible machine for detecting phishing websites. The gadget acts as a further functionality to an internet browser as an extension that routinely notifies the consumer whilst it detects a phishing internet site. The system is based on
a device gaining knowledge of approach, particularly supervised mastering.
We have decided on the XGBoost method because of its true overall performance in classification. Our focus is to pursue a better overall performance classifier by analyzing the features of phishing websites and choose the better aggregate of them to train the classifier. As a result, we finished our paper with accuracy of 99% accuracy with 48 features.
Keywords XGBoost (Extreme Gradient Boosting), Classifier, Features, Phishing, Train, Accuracy.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-03-20
Cite This Monitoring and Management of Phishing and Malicious Websites using Machine Learning - Tejal Sandip Gite, Sanjana Keshav Jagtap, Komal Gorakh Godse, Sakshi Yogesh Amrutkar, S.H.Sangale - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.15247
DOI https://doi.org/10.36948/ijfmr.2024.v06i02.15247
Short DOI https://doi.org/gtnj9k
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