Abstract
In this paper, a neural network model is presented to identify fraudulent visits to a website, which are significantly different from visits of human users. Such unusual visits are most often made by automated software, i.e. bots. Bots are used to perform advertising scams or to do scraping, i.e., automatic scanning of website content frequently not in line with the intentions of website authors. The model proposed in this paper works on data extracted directly from a web browser when a user or a bot visits a website. This data is acquired by way of using JavaScript. When bots appear on the website, collected parameter values are significantly different from the values collected during usual visits made by human website users. However, just knowing what values these parameters have is simply not enough to identify bots as they are being constantly modified and new values that have not yet been accounted for appear. Thus, it is not possible to know all the data generated by bots. Therefore, this paper proposes a neural network with an autoencoder structure that makes it possible to detect deviations in parameter values that depart from the learned data from usual users. This enables detection of anomalies, i.e., data generated by bots. The effectiveness of the presented model is demonstrated on authentic data extracted from several online stores.
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Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT press (2016)
Zhou, C., Paffenroth, R.C.: Anomaly detection with robust deep autoencoders. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2017)
Farahnakian, F., Heikkonen, J.: A deep auto-encoder based approach for intrusion detection system. In: 2018 20th International Conference on Advanced Communication Technology (ICACT). IEEE (2018)
Nguyen, Q.P.: GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection (2019)
2019. https://www.emarketer.com/content/digital-ad-fraud-2019
Barker, S.: Future Digital Advertising, Artificial Intelligence & Advertising Fraud 2019–2023, Juniper Research (2019)
Xiong, Y., Zuo, R.: Recognition of geochemical anomalies using a deep autoencoder network. Comput. Geosci. 86, 75–82 (2016)
Gabryel, M., Grzanek, K., Hayashi, Y.: Browser fingerprint coding methods increasing the effectiveness of user identification in the web traffic. J. Artificial Intelligence and Soft Computing Res. 10 (2020)
Gabryel, M., et al.: Decision making support system for managing advertisers by ad fraud detection. J. Artificial Intelligence and Soft Computing Res. 11 (2021)
Kim, T., Park, C.H.: Anomaly pattern detection in streaming data based on the transformation to multiple binary-valued data streams. J. Artificial Intelligence and Soft Computing Res. 12(1), 19–27 (2022)
Brunner, C., Kő, A., Fodor, S.: An autoencoder-enhanced stacking neural network model for increasing the performance of intrusion detection. J. Artificial Intelligence and Soft Computing Res. 12(2), 149–163 (2022)
Bilski, J., Kowalczyk, B., Marjański, A., Gandor, M., Żurada, J.: A novel fast feedforward neural networks training algorithm. J. Artificial Intelligence and Soft Computing Res. 11(4), 287–306 (2021). https://doi.org/10.2478/jaiscr-2021-0017
Bilski, J., Rutkowski, L., Smola̧g, J., Tao, D.: A novel method for speed training acceleration of recurrent neural networks. Information Sciences 553, 266–279 (2021). https://doi.org/10.1016/j.ins.2020.10.025
Grycuk, R., Scherer, R.: Novel fast binary hash for content-based solar image retrieval. In: 2020 International Joint Conference on Neural Networks (IJCNN). IEEE (2020)
Grycuk, R., Scherer, R.: Solar image hashing by intermediate descriptor and autoencoder. In: 2021 International Joint Conference on Neural Networks (IJCNN). IEEE (2021)
Acknowledgments
The presented results are obtained within the realization of the project “Traffic Watchdog 2.0 – verification and protection system against fraud activities in the on-line marketing (ad frauds) supported by artificial intelligence and virtual finger-print technology” financed by the National Centre for Research and Development; grant number POIR.01.01.01–00-0241/19–01.
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Gabryel, M., Lada, D., Kocić, M. (2023). Autoencoder Neural Network for Detecting Non-human Web Traffic. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2022. Lecture Notes in Computer Science(), vol 13589. Springer, Cham. https://doi.org/10.1007/978-3-031-23480-4_19
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