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Detection of Web site visitors based on fuzzy rough sets


Despite emerging of Web 2.0 applications and increasing requirements to well-behaved Web robots, malicious ones can reveal irreparable risks for Web sites. Regardless of behavior of Web robots, they may occupy bandwidth and reduce performance of Web servers. In spite of many prestigious researches trying to characterize Web visitors and classify them, there is a lack of concentration on feature selection to dynamically choose attributes used to describe Web sessions. On the other hand, depending on an accurate clustering technique, which can deal with huge number of samples in a reasonable amount of time, is practically important. Therefore, in this paper, a new algorithm, fuzzy rough set–Web robot detection (FRS-WRD), is proposed based on fuzzy rough set theory to better characterize and cluster Web visitors of three real Web sites. External evaluations show that in contrast to state-of-the-art algorithms, FRS-WRD achieves better results in terms of G-mean 95%, Jaccard 88%, entropy 0.36, and finally, purity 96%. Moreover, according to confusion matrixes, it can better detect malicious Web visitors.

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Correspondence to Javad Hamidzadeh.

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This article does not contain any studies with animals performed by any of the authors.

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Communicated by V. Loia.



In this section, a summary of all primary features used in this paper is presented. These attributes have been proposed in other related works and indicated to be helpful in separating humans from Web robots. The index column of Table 5 demonstrates whether the related attribute has higher value for Web robots (R) or human users (H).

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Hamidzadeh, J., Zabihimayvan, M. & Sadeghi, R. Detection of Web site visitors based on fuzzy rough sets. Soft Comput 22, 2175–2188 (2018).

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  • Web robot detection
  • Fuzzy rough set
  • Clustering