<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper presents a neural network model for identifying non-human traffic to a web-site, 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.</p>
</abstract>

This paper presents a neural network model for identifying non-human traffic to a web-site, 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.

Detecting Anomalies in Advertising Web Traffic with the Use of the Variational Autoencoder

Department of Intelligent Computer Systems

Department of Computer Science and Software Engineering Concordia University

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

This paper presents a neural network model for identifying non-human traffic to a web-site, which is significantly different from visits made by regular users....

Detecting Anomalies in Advertising Web Traffic with the Use of the Variational Autoencoder

Pubblicato online: 29 ott 2022

Pagine: 255 - 256

Ricevuto: 02 apr 2022

Accettato: 12 ott 2022

DOI: https://doi.org/10.2478/jaiscr-2022-0017

Parole chiave
anomaly detection, web traffic, ad fraud, variational autoencoder

© 2022 Marcin Gabryel et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Detecting Anomalies in Advertising Web Traffic with the Use of the Variational Autoencoder

Pubblicato online: 29 ott 2022

Pagine: 255 - 256

Ricevuto: 02 apr 2022

Accettato: 12 ott 2022

DOI: https://doi.org/10.2478/jaiscr-2022-0017

Parole chiaveanomaly detection, web traffic, ad fraud, variational autoencoder

© 2022 Marcin Gabryel et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
anomaly detection, web traffic, ad fraud, variational autoencoder