Blog Backlinks Malicious Domain Name Detection via Supervised Learning

Abdulrahman A. Alshdadi; Ali Daud; Ahmed S. AlghamdiSaqib Hussain

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Blog Backlinks Malicious Domain Name Detection via Supervised Learning

机译：Blog Backlinks Malicious Domain Name Detection via Supervised Learning

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摘要

Web spam is the unwanted request on websites, low-quality backlinks, emails, and reviews which is generated by an automated program. It is the big threat for website owners; because of it, they can lose their top keywords ranking from search engines, which will result in huge financial loss to the business. Over the years, researchers have tried to identify malicious domains based on specific features. However, lighthouse plugin, Ahrefs tool, and social media platforms features are ignored. In this paper, the authors are focused on detection of the spam domain name from a mixture of legit and spam domain name dataset. The dataset is taken from Google webmaster tools. Machine learning models are applied on individual, distributed, and hybrid features, which significantly improved the performance of existing malicious domain machine learning techniques. Better accuracy is achieved for support vector machine (SVM) classifier, as compared to Naïve Bayes, C4.5, AdaBoost, LogitBoost.

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《International journal on Semantic Web and information systems》 |2021年第3期|1-17|共17页
作者
Abdulrahman A. Alshdadi; Ali Daud; Ahmed S. AlghamdiSaqib Hussain;
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作者单位

University of Jeddah;

International Islamic University;

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原文格式 PDF
正文语种英语
中图分类计算机网络;
关键词
Blog Backlinks; Google Webmaster Tools; Keyword Rankings; Malicious Domain Name Detection; Social Computing; Social Media Platforms; Supervised Learning; Web Spam;

Blog Backlinks Malicious Domain Name Detection via Supervised Learning

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