Detecting anomalies in advertising web traffic with the use of the variational autoencoder

• Autorzy: Gabryel Marcin , Lada Dawid , Filutowicz Zbigniew , Patora-Wysocka Zofia , Kisiel-Dorohinicki Marek , Chen Guang Yi

Identyfikatory

DOI

10.2478/jaiscr-2022-0017

• Języki publikacji: EN

Abstrakty

EN

This paper presents a neural network model for identifying non-human traffic to a website, which is significantly different from visits made by regular users. Such visits are undesirable from the point of view of the website owner as they are not human activity, and therefore do not bring any value, and, what is more, most often involve costs incurred in connection with the handling of advertising. They are made most often by dishonest publishers using special software (bots) to generate profits. Bots are also used in scraping, which is automatic scanning and downloading of website content, which actually is not in the interest of website authors. The model proposed in this work is learnt by data extracted directly from the web browser during website visits. This data is acquired by using a specially prepared JavaScript that monitors the behavior of the user or bot. The appearance of a bot on a website generates parameter values that are significantly different from those collected during typical visits made by human website users. It is not possible to learn more about the software controlling the bots and to know all the data generated by them. Therefore, this paper proposes a variational autoencoder (VAE) neural network model with modifications to detect the occurrence of abnormal parameter values that deviate from data obtained from human users’ Internet traffic. The algorithm works on the basis of a popular autoencoder method for detecting anomalies, however, a number of original improvements have been implemented. In the study we used authentic data extracted from several large online stores.

Słowa kluczowe

EN

anomaly detection; web traffic; ad fraud; variational autoencoder

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2022
• Tom: Vol. 12, No. 4
• Strony: 255--266
• Opis fizyczny: Bibliogr. 22 poz., rys.

Twórcy

autor

Gabryel Marcin

• Department of Intelligent Computer Systems, Cze¸stochowa University of Technology, al. Armii Krajowej 36, 42-200 Częstochowa, Poland

autor

Lada Dawid

• Spark Digitup, 31-060 Krakow, Poland

autor

Filutowicz Zbigniew

• Institute of Information Technologies, University of Social Sciences, ul. Sienkiewicza 9, 90-113 Lodz

autor

Patora-Wysocka Zofia

• Management Department, University of Social Science, ul. Sienkiewicza 9, 90–113 Lodz, Poland

autor

Kisiel-Dorohinicki Marek

• Institute of Computer Science, AGH University of Science and Technology, 30-059 Krakow, Poland

autor

Chen Guang Yi

• Department of Computer Science and Software Engineering Concordia University, Montreal, Quebec, Canada H3G 1M8

Bibliografia

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