Optimizing malicious website prediction: An advanced XGBoost-based machine learning model

Optimizing malicious website prediction: An advanced XGBoost-based machine learning model

<p dir="ltr">In the substantial area of the Internet, some websites can be quite harmful and troublesome for both individuals and businesses. Our methods for identifying and forecasting these malicious websites are not always reliable; they can be slow and inaccurate. What if you had...

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Bibliographic Details
Main Author: Sumaira Hussain (19259669) (author)
Other Authors: Islam Zada (21755819) (author), Moutaz Alazab (17730060) (author), Hessa Alfraihi (21755825) (author), Manal Aldhayan (22330930) (author), Inam Ullah (5227166) (author), Mohammad Asmat Ullah Khan (22330933) (author)
Published: 2025
Subjects:
Information and computing sciences
Applied computing
Cybersecurity and privacy
Data management and data science
Machine learning
software privacy
software defects
machine learning
classification and prediction
malicious websites
websites dataset
data processing and modeling
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