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Web Bot Detection System Based on Divisive Clustering and K-Nearest Neighbor Using Biostatistics Features Set

Web Bot Detection System Based on Divisive Clustering and K-Nearest Neighbor Using Biostatistics Features Set

Rizwan Ur Rahman, Deepak Singh Tomar
Copyright: © 2021 |Volume: 13 |Issue: 6 |Pages: 27
ISSN: 1941-6210|EISSN: 1941-6229|EISBN13: 9781799867531|DOI: 10.4018/IJDCF.20211101.oa6
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MLA

Rahman, Rizwan Ur, and Deepak Singh Tomar. "Web Bot Detection System Based on Divisive Clustering and K-Nearest Neighbor Using Biostatistics Features Set." IJDCF vol.13, no.6 2021: pp.1-27. http://doi.org/10.4018/IJDCF.20211101.oa6

APA

Rahman, R. U. & Tomar, D. S. (2021). Web Bot Detection System Based on Divisive Clustering and K-Nearest Neighbor Using Biostatistics Features Set. International Journal of Digital Crime and Forensics (IJDCF), 13(6), 1-27. http://doi.org/10.4018/IJDCF.20211101.oa6

Chicago

Rahman, Rizwan Ur, and Deepak Singh Tomar. "Web Bot Detection System Based on Divisive Clustering and K-Nearest Neighbor Using Biostatistics Features Set," International Journal of Digital Crime and Forensics (IJDCF) 13, no.6: 1-27. http://doi.org/10.4018/IJDCF.20211101.oa6

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Abstract

Web bots are destructive programs that automatically fill the web form and steal the data from web sites. According to numerous web bot traffic reports, web bots traffic comprises of more than fifty percent of the total web traffic. An effective guard against the stealing of the data from web sites and automated web form is to identify and confirm the human user presence on web sites. In this paper, an efficient k-Nearest Neighbor algorithm using hierarchical clustering for web bot detection is proposed. Proposed technique exploits a novel taxonomy of web bot features known as Biostatistics Features. Numerous attack scenarios for web bot attacks such as automatic account registration, automatic form filling, bulk message posting, and web scrapping are created to imitate the zero-day web bot attacks. The proposed technique is evaluated with number of experiments using standard evaluation parameters. The experimental result analysis demonstrates that the proposed technique is extremely efficient in differentiating human users from web bots.