Decision making support system for managing advertisers by ad fraud detection

• Autorzy: Gabryel Marcin , Scherer Magdalena M. , Sułkowski Łukasz , Damaševičius Robertas

Identyfikatory

DOI

10.2478/jaiscr-2021-0020

• Języki publikacji: EN

Abstrakty

EN

Efficient lead management allows substantially enhancing online channel marketing programs. In the paper, we classify website traffic into human- and bot-origin ones. We use feedforward neural networks with embedding layers. Moreover, we use one-hot encoding for categorical data. The data of mouse clicks come from seven large retail stores and the data of lead classification from three financial institutions. The data are collected by a JavaScript code embedded into HTML pages. The three proposed models achieved relatively high accuracy in detecting artificially generated traffic.

Słowa kluczowe

EN

lead management; feedforward neural networks; embedding; online marketing

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2021
• Tom: Vol. 11, No. 4
• Strony: 331--339
• Opis fizyczny: Bibliogr. 14 poz., rys.

Twórcy

autor

Gabryel Marcin

• Faculty of Mechanical Engineering and Computer Science, Czestochowa University of Technology, al. Armii Krajowej 36, 42-200 Częstochowa, Poland

autor

Scherer Magdalena M.

• Faculty of Management, Czestochowa University of Technology, Poland

autor

Sułkowski Łukasz

• Faculty of Management and Social Sciences, Jagiellonian University, Cracow, Poland
• Management Department, University of Social Sciences, 90 - 113 Lodz, Poland
• Clark University, Worcester, MA 01610, USA 6Department

autor

Damaševičius Robertas

• Department of Applied Informatics, Vytautas Magnus University, Kaunas 44404, Lithuania

Bibliografia

• [1] AsSadhan, B., Moura, J.M., Lapsley, D., Jones, C., Strayer, W.T.: Detecting botnets using command and control traffic. In: 2009 Eighth IEEE International Symposium on Network Computing and Applications, pp. 156–162. IEEE (2009)
• [2] Bengio, Y.: Learning deep architectures for AI. Now Publishers Inc (2009)
• [3] Chen, C.M., Lin, H.C.: Detecting botnet by anomalous traffic. journal of information security and applications 21, 42–51 (2015)
• [4] eMarketer: Digital ad fraud 2019. https://www.emarketer.com/content/digital-ad-fraud-2019
• [5] Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Proceedings of the fourteenth international conference on artificial intelligence and statistics, pp. 315–323. JMLR Workshop and Conference Proceedings (2011)
• [6] Lagopoulos, A., Tsoumakas, G., Papadopoulos, G.: Web robot detection in academic publishing. arXiv preprint arXiv:1711.05098 (2017)
• [7] Neal, A., Kouwenhoven, S., Sa, O.: Quantifying online advertising fraud: Ad-click bots vs humans. In: Tech. Rep. Oxford Bio Chronometrics (2015)
• [8] Networks, D.: 2018 bad bot report. https://resources.distilnetworks.com/whitepapers/2018-bad-bot-report
• [9] Rajaraman, A., Ullman, J.D.: Mining of massive datasets. Cambridge University Press (2011)
• [10] Research, J.: Ad fraud - how ai will rescue your budget. https://news.unilead.net/wp-content/uploads/2017/09/Ad-Fraud-How-AI-will-rescueyour-Budget-whitepaper.pdf
• [11] Seyyar, M.B., Çatak, F.Ö., Gül, E.: Detection of attack-targeted scans from the apache httpserver access logs. Applied computing and informatics 14(1), 28–36 (2018)
• [12] Silva, S.S., Silva, R.M., Pinto, R.C., Salles, R.M.: Botnets: A survey. Computer Networks 57(2), 378–403 (2013)
• [13] Soniya, B., Wilscy, M.: Detection of randomized bot command and control traffic on an end-point host. Alexandria Engineering Journal 55(3), 2771–2781 (2016)
• [14] Zhu, X., Tao, H., Wu, Z., Cao, J., Kalish, K., Kayne, J.: Fraud prevention in online digital advertising. Springer (2017)

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).