An Approach Based on Contrast Patterns for Bot Detection on Web Log Files
Authors: 
Octavio Loyola-González, Raúl Monroy, 
Miguel Angel Medina-Pérez, Bárbara Cervantes, José Ernesto Grimaldo-TijerinaAuthors Info & Claims
Pages 276 - 285
Abstract
Nowadays, companies invest resources in detecting non-human accesses on their web traffics. Usually, non-human accesses are a few compared with the human accesses, which is considered as a class imbalance problem, and as a consequence, classifiers bias their classification results toward the human accesses obviating, in this way, the non-human accesses. In some classification problems, such as the non-human traffic detection, high accuracy is not only the desired quality, the model provided by the classifier should be understood by experts. For that, in this paper, we study the use of contrast pattern-based classifiers for building an understandable and accurate model for detecting non-human traffic on web log files. Our experiments over five databases show that the contrast pattern-based approach obtains significantly better AUC results than other state-of-the-art classifiers.
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Information
Published In
Oct 2018
453 pages
ISBN:978-3-030-04490-9
DOI:10.1007/978-3-030-04491-6
© Springer Nature Switzerland AG 2018.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 03 January 2019
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