Title: The web ad-click fraud detection approach for supporting to the online advertising system

Authors: Pankaj Kumar Keserwani; Mahesh Chandra Govil; Emmanuel Shubhakar Pilli

Addresses: Department of Computer Science and Engineering, National Institute of Technology Sikkim, Barfung Block, Ravangla, Sikkim 737139, India ' Department of Computer Science and Engineering, National Institute of Technology Sikkim, Barfung Block, Ravangla, Sikkim 737139, India ' Department of Computer Science and Engineering, Malaviya National Institute of Technology Jaipur, Jaipur, India

Abstract: With the introduction of e-commercial activities, revenue is generated in several legal and illegal ways. Marketing of a product or service in an online environment is increasing day by day since most users are getting involved in an online environment for buying or selling items. As a result, attackers are getting more space for performing online attacks by different malicious activities. The advertisement (ad) fraud, which is increasing exponentially with each passing day, is one of them. Ad fraud causes defame for the online advertising system and low return on the advertiser's investment (ROI). The paper reveals a methodology for detecting web ad-click frauds so that the ROI of an advertiser can be increased. The developed algorithm to detect the ad-click frauds in the proposed methodology can detect the web ad-click frauds. The results of the ad-click fraud algorithm are verified with the help of popular machine learning (ML) algorithms - k-nearest neighbour (k-NN), random forest (RF), decision tree (DT), logistic regression (LR) and support vector machine (SVM), with accuracies achieved.

Keywords: ad-click fraud; machine learning; methodology; algorithm; Amazon Web Service; AWS.

DOI: 10.1504/IJSI.2022.121091

International Journal of Swarm Intelligence, 2022 Vol.7 No.1, pp.3 - 24

Received: 17 Jun 2020
Accepted: 27 Nov 2020
Published online: 24 Feb 2022 *

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