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Reseach Article

Online Advertising and Fraud Click in Online Advertisement: A Survey

by Ranjeet Vishwakarma, Rajesh Dhakad
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 1
Year of Publication: 2024
Authors: Ranjeet Vishwakarma, Rajesh Dhakad

Ranjeet Vishwakarma, Rajesh Dhakad . Online Advertising and Fraud Click in Online Advertisement: A Survey. International Journal of Computer Applications. 186, 1 ( Jan 2024), 1-8. DOI=10.5120/ijca2024923300

@article{ 10.5120/ijca2024923300,
author = { Ranjeet Vishwakarma, Rajesh Dhakad },
title = { Online Advertising and Fraud Click in Online Advertisement: A Survey },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2024 },
volume = { 186 },
number = { 1 },
month = { Jan },
year = { 2024 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2024923300 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T01:29:23.413500+05:30
%A Ranjeet Vishwakarma
%A Rajesh Dhakad
%T Online Advertising and Fraud Click in Online Advertisement: A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 1
%P 1-8
%D 2024
%I Foundation of Computer Science (FCS), NY, USA

The advertising industry is rapidly growing, as compared to the past advertising medium has evolved significantly. Previously, the advertisement medium was primarily print-based. With the growth of the internet, it has shifted to internet-based online advertisement. Nowadays, each and every company advertises on the Internet, considering its presence in people’s daily lives, due to this the advertising industry has become a multi-billion-dollar industry. One widely used revenue model in online advertising is Pay-Per-Click (PPC). However, PPC also brings about challenges such as click fraud, where advertising agencies generate fake clicks, resulting in a rise in advertising costs and reduced Return on Investment (ROI). Click fraud, including activities like click farms, automated bots, and manual clicking. It is a significant issue that can significantly impact on business’s financial performance. To address this problem in the past, various approaches have been proposed and implemented. By detecting and preventing click fraud, advertisers can ensure the effectiveness of their advertisements and only pay for legitimate clicks. Fraud click detection can lead to substantial cost savings. This paper presents a survey that aims to provide insights into click fraud detection and the domains actively involved in countering this fraudulent behaviour.

  1. Paulo S. Almeida and Jo˜ao J. C. Gondim. Click fraud detection and prevention system for ad networks. Journal of Information Security and Cryptography, 5:27, January 2019.
  2. Edd Applegate. Advertising in the US: past, present, future. Journalism Studies, 1(2):285–302, January 2000.
  3. Stuart Barnes. Wireless digital advertising: Nature and implications. International Journal of Advertising, 21:399–420, January 2004.
  4. Ladislav Ber´anek, V´aclav N`ydl, and Radim Remeˇs. Click stream data analysis for online fraud detection in e-commerce. In INPROFORUM 2016, 2017.
  5. Aditya Billore and Ashish Sadh. Mobile advertising: A review of the literature. The Marketing Review, 15(2):161–183, August 2015.
  6. Colin Campbell and Lawrence J. Marks. Good native advertising isn’t a secret. Business Horizons, 58(6):599–606, November 2015.
  7. CircleID. A Look into New Cybersquatting and Phishing Domains Targeting Facebook, Instagram, and WhatsApp.
  8. Anshuman Dash and Satyajit Pal. Auto-detection of click-frauds using machine learning. International Journal of Engineering Science and Computing, 10:27227–27235, 2020.
  9. Neil Daswani, Chris Mysen, Vinay Rao, Stephen Weis, and Kouros Gharachorloo. Online advertising fraud. Crimeware: understanding new attacks and defenses, 40:1–28, 2008.
  10. Marcin Gabryel. Data Analysis Algorithm for Click Fraud Recognition. In Information and Software Technologies, volume 920, pages 437–446. Springer International Publishing, Cham, 2018.
  11. Nayanaba Gohil and Arvind D Meniya. A survey on online advertising and click fraud detection. In 2nd national conference on research trends in information and communication technology, 2020.
  12. Google Inc. Best practices for ad placement - Google AdSense Help.
  13. Md. Shahrear Iqbal, Md. Zulkernine, Fehmi Jaafar, and Yuan Gu. FCFraud: Fighting Click-Fraud from the User Side. In 17th International Symposium on High Assurance Systems Engineering, pages 157–164, Orlando, FL, USA, January 2016.
  14. Shubhangi Jain, Falguni Jindal, Anmolika Goyal, and Savy Mudgal. Identification of Click Fraud and Review of Existing Detection Algorithms. In ICSSIT, pages 894–899, Tirunelveli, India, November 2019. IEEE.
  15. Do Yuon Kim and Hye-Young Kim. Influencer advertising on social media: The multiple inference model on influencer-product congruence and sponsorship disclosure. Journal of Business Research, 130:405–415, June 2021.
  16. Johannes Knoll. Advertising in social media: a review of empirical evidence. International Journal of Advertising, 35(2):266–300, March 2016.
  17. Heejun Lee and Chang-Hoan Cho. Digital advertising: present and future prospects. International Journal of Advertising, 39(3):332–341, April 2020.
  18. John Linden and Tobias Teeter. Method for performing real-time click fraud detection, prevention and reporting for online advertising, jun 2016. US Patent 9,367,857.
  19. Bin Liu, Anmol Sheth, Udi Weinsberg, Jaideep Chandrashekar, and Ramesh Govindan. AdReveal: improving transparency into online targeted advertising. In Proceedings of the 12th ACM Workshop on Hot Topics in Networks, pages 1–7, College Park Maryland, November 2013.
  20. Stylianos Mamais and George Theodorakopoulos. Behavioural Verification: Preventing Report Fraud in Decentralized Advert Distribution Systems. Future Internet, 9(4):88, November 2017.
  21. Bhargavi Mikkili and Suhasini Sodagudi. Advertisement Click Fraud Detection Using Machine Learning Algorithms. In Vikrant Bhateja, Suresh Chandra Satapathy, Carlos M. Travieso-Gonzalez, and T. Adilakshmi, editors, Smart Intelligent Computing and Applications, volume 282, pages 353–362. Springer Nature Singapore, 2022.
  22. Elena-Adriana Minastireanu and Gabriela Mesnita. Light GBM Machine Learning Algorithm to Online Click Fraud Detection. Journal of Information Assurance & Cybersecurity, pages 1–12, April 2019.
  23. Riwa Mouawi, Imad H. Elhajj, Ali Chehab, and Ayman Kayssi. Crowdsourcing for click fraud detection. EURASIP Journal on Information Security, 2019(1):11, December 2019.
  24. Shishir Nagaraja and Ryan Shah. Clicktok: click fraud detection using traffic analysis. In Proceedings of the 12th Conference on Security and Privacy in Wireless and Mobile Networks, pages 105–116, Miami, Florida, May 2019. ACM.
  25. Peter Nowak. Deceptibots: when machines go bad. New Scientist, 214(2870):45–47, June 2012.
  26. Esteban Ortiz-Ospina. The rise of social media. Our World in Data, 2019.
  27. Lijiao Pan, Shibiao Mu, and Yingyan Wang. User click fraud detection method based on Top-Rank- k frequent pattern mining. International Journal of Modern Physics B, 33(15):1950150, June 2019.
  28. Paul Pearce, Vacha Dave, Chris Grier, Kirill Levchenko, Saikat Guha, Damon McCoy, Vern Paxson, Stefan Savage, and Geoffrey M. Voelker. Characterizing Large-Scale Click Fraud in ZeroAccess. In Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, pages 141–152, Scottsdale Arizona USA, November 2014.
  29. Nirma Sadamali Jayawardena, Park Thaichon, Sara Quach, Ali Razzaq, and Abhishek Behl. The persuasion effects of virtual reality (VR) and augmented reality (AR) video advertisements: A conceptual review. Journal of Business Research, 160:113–739, May 2023.
  30. Hala Shaari and Nuredin Ahmed. An extensive study on online and mobile ad fraud. The Third Conference for Engineering Sciences and Technology, 2020.
  31. Peter Snyder and Chris Kanich. No please, after you: Detecting fraud in affiliate marketing networks. In WEIS, 2015.
  32. Kevin Springborn and Paul Barford. Impression fraud in on-line advertising via pay-per-view networks. In USENIX Security Symposium, pages 211–226, 2013.
  33. Apoorva Srivastava. REAL-TIME AD CLICK FRAUD DETECTION. Master of Science, San Jose State University, CA, USA, May 2020.
  34. Statista. Digital ad spend worldwide 2026.
  35. Mayank Taneja, Kavyanshi Garg, Archana Purwar, and Samarth Sharma. Prediction of click frauds in mobile advertising. In 2015 8th International Conference on Contemporary Computing, pages 162–166, Noida, India, August 2015. IEEE.
  36. G. S. Thejas, Kianoosh G. Boroojeni, Kshitij Chandna, Isha Bhatia, S. S. Iyengar, and N. R. Sunitha. Deep Learning-based Model to Fight Against Ad Click Fraud. In Proceedings of the 2019 ACM Southeast Conference, pages 176–181, Kennesaw GA USA, April 2019.
  37. G. S. Thejas, Jayesh Soni, Kianoosh G. Boroojeni, S.S. Iyengar, Kanishk Srivastava, Prajwal Badrinath, N.R. Sunitha, Nagarajan Prabakar, and Himanshu Upadhyay. A Multi-time-scale Time Series Analysis for Click Fraud Forecasting using Binary Labeled Imbalanced Dataset. In 2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution, pages 1–8, Bengaluru, India, December 2019. IEEE.
  38. B Viruthika, Suman Sangeeta Das, E Manish Kumar, D Prabhu, and Srm Ist Chennai. Detection of Advertisement Click Fraud Using Machine Learning. International Journal of Advanced Science and Technology, 2020.
  39. Qianqian Wang, Fang’ai Liu, Shuning Xing, and Xiaohui Zhao. A New Approach for Advertising CTR Prediction Based on Deep Neural Network via Attention Mechanism. Computational and Mathematical Methods in Medicine, 2018:1–11, September 2018.
  40. Sen Zhang, Qiang Fu, and Wendong Xiao. Advertisement Click-Through Rate Prediction Based on the Weighted-ELM and Adaboost Algorithm. Scientific Programming, 2017:1–8, 2017.
  41. Xin Zhang, Xuejun Liu, and Han Guo. A Click Fraud Detection Scheme based on Cost sensitive BPNN and ABC in Mobile Advertising. In 2018 IEEE 4th International Conference on Computer and Communications, pages 1360–1365, Chengdu, China, December 2018.
  42. Xingquan Zhu, Haicheng Tao, Zhiang Wu, Jie Cao, Kristopher Kalish, and Jeremy Kayne. Ad Fraud Categorization and Detection Methods. In Fraud Prevention in Online Digital Advertising, pages 25–38. Springer International Publishing, 2017.
Index Terms

Computer Science
Information Sciences


Fraud Click advertisement Pay-Per-Click click farms automated bots detection method