{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T13:26:13Z","timestamp":1767965173460,"version":"3.49.0"},"reference-count":119,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,12,16]],"date-time":"2021-12-16T00:00:00Z","timestamp":1639612800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCP"],"abstract":"<jats:p>Over the last two decades, we have witnessed a fundamental transformation of the advertising industry, which has been steadily moving away from the traditional advertising mediums, such as television or direct marketing, towards digital-centric and internet-based platforms. Unfortunately, due to its large-scale adoption and significant revenue potential, digital advertising has become a very attractive and frequent target for numerous cybercriminal groups. The goal of this study is to provide a consolidated view of different categories of threats in the online advertising ecosystems. We begin by introducing the main elements of an online ad platform and its different architecture and revenue models. We then review different categories of ad fraud and present a taxonomy of known attacks on an online advertising system. Finally, we provide a comprehensive overview of methods and techniques for the detection and prevention of fraudulent practices within those system\u2014both from the scientific as well as the industry perspective. The main novelty of our work lies in the development of an innovative taxonomy of different types of digital advertising fraud based on their actual executors and victims. We have placed different advertising fraud scenarios into real-world context and provided illustrative examples thereby offering an important practical perspective that is very much missing in the current literature.<\/jats:p>","DOI":"10.3390\/jcp1040039","type":"journal-article","created":{"date-parts":[[2021,12,16]],"date-time":"2021-12-16T11:27:36Z","timestamp":1639654056000},"page":"804-832","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Ads and Fraud: A Comprehensive Survey of Fraud in Online Advertising"],"prefix":"10.3390","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0045-657X","authenticated-orcid":false,"given":"Shadi","family":"Sadeghpour","sequence":"first","affiliation":[{"name":"Engineering and Computer Science, York University, Toronto, ON M3J 1P3, Canada"}]},{"given":"Natalija","family":"Vlajic","sequence":"additional","affiliation":[{"name":"Engineering and Computer Science, York University, Toronto, ON M3J 1P3, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhu, X., Tao, H., Wu, Z., Cao, J., Kalish, K., and Kayne, J. (2017). Fraud Prevention in Online Digital Advertising, Springer.","DOI":"10.1007\/978-3-319-56793-8"},{"key":"ref_2","unstructured":"(2021, November 04). Digital Ad Industry Will Gain $8.2 Billion By Eliminating Fraud and Flaws in Internet Supply Chain, IAB & EY Study Shows. Available online: https:\/\/www.iab.com\/news\/digital-ad-industry-will-gain-8-2-billion-by-eliminating-fraud-and-flaws-in-internet-supply-chain-iab-ey-study-shows."},{"key":"ref_3","unstructured":"(2021, November 04). Ad Fraud Stats. Available online: https:\/\/www.businessofapps.com\/research\/ad-fraud-statistics\/."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2494","DOI":"10.1109\/COMST.2021.3118271","article-title":"Online advertising security: Issues, taxonomy, and future directions","volume":"23","author":"Pooranian","year":"2021","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_5","first-page":"1","article-title":"Threats to online advertising and countermeasures: A technical survey","volume":"1","author":"Cai","year":"2020","journal-title":"Digit. Threats Res. Pract."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.comcom.2016.12.016","article-title":"Online advertising: Analysis of privacy threats and protection approaches","volume":"100","year":"2017","journal-title":"Comput. Commun."},{"key":"ref_7","unstructured":"(2021, November 04). What Is an Ad Network and How Does It Work?\u2014Clearcode Blog. Available online: https:\/\/clearcode.cc\/blog\/what-is-an-ad-network-and-how-does-it-work\/."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1561\/1500000045","article-title":"Computational advertising: Techniques for targeting relevant ads found","volume":"8","author":"Dave","year":"2014","journal-title":"Trends Inform. Retr."},{"key":"ref_9","unstructured":"Cook, K. (2021, November 04). A Brief History of Online Advertising. Available online: https:\/\/blog.hubspot.com\/marketing\/history-of-online-advertising."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Panwar, A., Onut, I.-V., and Miller, J. (2014). Towards real time contextual advertising. International Conference on Web Information Systems Engineering, Springer.","DOI":"10.1007\/978-3-319-11746-1_33"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1080\/00913367.2017.1339368","article-title":"Online behavioral advertising: A literature review and research agenda","volume":"46","author":"Boerman","year":"2017","journal-title":"J. Advert."},{"key":"ref_12","unstructured":"(2021, November 04). Types Of Online Advertising. Available online: https:\/\/www.adskills.com\/blog\/7-types-of-online-advertising\/."},{"key":"ref_13","unstructured":"(2021, November 04). Understanding RTB, Programmatic Direct, and PMP\u2014Clearcode Blog. Available online: https:\/\/clearcode.cc\/blog\/rtb-programmatic-direct-pmp\/."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1561\/1500000049","article-title":"Display advertising with Real-Time Bidding (RTB) and behavioural targeting","volume":"11","author":"Wang","year":"2017","journal-title":"Found. Trends\u00ae Inf. Retr."},{"key":"ref_15","unstructured":"(2021, November 04). Ultimate Guide to the Private Marketplace for Publishers|Publift. Available online: https:\/\/www.publift.com\/\/adteach\/ultimate-guide-to-the-private-marketplace-for-publishers."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"DeBlasio, J., Guha, S., Voelker, G.M., and Snoeren, A.C. (2017, January 1\u20133). Exploring the dynamics of search advertiser fraud. Proceedings of the 2017 Internet Measurement Conference, London, UK.","DOI":"10.1145\/3131365.3131393"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1287\/mksc.1080.0397","article-title":"Click Fraud","volume":"28","author":"Wilbur","year":"2009","journal-title":"Market. Sci."},{"key":"ref_18","first-page":"3","article-title":"User Profiling-a Short Review","volume":"108","author":"Cufoglu","year":"2014","journal-title":"Int. J. Comput. Appl."},{"key":"ref_19","unstructured":"Haveliwala, T.H., Jeh, G.M., and Kamvar, S.D. (2021, December 04). Targeted Advertisements Based on User Profiles and Page Profile. Available online: https:\/\/patents.google.com\/patent\/US8321278B2\/en."},{"key":"ref_20","unstructured":"Fleuren, M.C.W. (2012). User Profiling Techniques: A Comparative Study in the Context of e-Commerce Websites. [Bachelor\u2019s Thesis, Utrecht University]."},{"key":"ref_21","unstructured":"Degeling, M., and Herrmann, T. (2016). Your interests according to google-a profile-centered analysis for obfuscation of online tracking profiles. arXiv."},{"key":"ref_22","unstructured":"(2021, November 04). Google Data Collection Research. Available online: https:\/\/digitalcontentnext.org\/blog\/2018\/08\/21\/google-data-collection-research\/."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Dennis, W.L., Erwin, A., and Galinium, M. (2016, January 5\u20136). Data mining approach for user profile generation on advertisement serving. Proceedings of the 2016 8th International Conference on Information Technology and Electrical Engineering (ICITEE), Yogyakarta, Indonesia.","DOI":"10.1109\/ICITEED.2016.7863269"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1476","DOI":"10.1109\/JPROC.2016.2637878","article-title":"A Survey on web tracking: Mechanisms, implications, and defenses","volume":"105","author":"Bujlow","year":"2017","journal-title":"Proc. IEEE"},{"key":"ref_25","unstructured":"Schmucker, N. (2021, December 04). Web Tracking. In SNET2 Seminar Paper-Summer Term, Available online: https:\/\/www.semanticscholar.org\/paper\/Web-Tracking-SNET-2-Seminar-Paper-Summer-Term-2011-Schm%C3%BCcker\/304bb388a1e4e74a2109f39ff8ae0b6f66f0dd02."},{"key":"ref_26","unstructured":"(2021, December 04). What Is a Session ID?. Available online: https:\/\/www.ionos.ca\/digitalguide\/hosting\/technical-matters\/what-is-a-session-id\/."},{"key":"ref_27","unstructured":"Alaca, F. (2018). Strengthening Password-Based Web Authentication through Multiple Supplementary Mechanisms. [Ph.D. Thesis, Carleton University]."},{"key":"ref_28","unstructured":"(2021, December 04). Session Management\u2014OWASP Cheat Sheet Series. Available online: https:\/\/cheatsheetseries.owasp.org\/cheatsheets\/Session_Management_Cheat_Sheet.html."},{"key":"ref_29","unstructured":"HTTP Cookie (2021, December 04). Wikipedia. Available online: https:\/\/en.wikipedia.org\/wiki\/HTTP_cookie#window.name."},{"key":"ref_30","unstructured":"(2021, December 04). DOM Standard. Available online: https:\/\/dom.spec.whatwg.org\/."},{"key":"ref_31","unstructured":"(2021, December 04). Tracking Cookies\u2014How to Limit Third-Party Data Collection. Available online: https:\/\/www.comparitech.com\/blog\/information-security\/tracking-cookies\/."},{"key":"ref_32","unstructured":"Nasir, M. (2014). Tracking and Identifying Individual Users in a Web Surfing Session, Computer and Network Security, Middlesex University."},{"key":"ref_33","unstructured":"(2021, December 04). Web Caching Basics: Terminology, HTTP Headers, and Caching Strategies. Available online: https:\/\/www.digitalocean.com\/community\/tutorials\/web-caching-basics-terminology-http-headers-and-caching-strategies."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Klein, A., and Pinkas, B. (2019, January 24\u201327). DNS Cache-Based User Tracking. Proceedings of the Network and Distributed Systems Security (NDSS) Symposium 2019, San Diego, CA, USA.","DOI":"10.14722\/ndss.2019.23186"},{"key":"ref_35","unstructured":"(2021, December 04). HTTP Caching\u2014HTTP|MDN. Available online: https:\/\/developer.mozilla.org\/en-US\/docs\/Web\/HTTP\/Caching."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1048","DOI":"10.1109\/COMST.2021.3064259","article-title":"A survey on device behavior fingerprinting: Data sources, techniques, application scenarios, and datasets","volume":"23","author":"Valero","year":"2021","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Kaur, N., Azam, S., Kannoorpatti, K., Yeo, K.C., and Shanmugam, B. (2017, January 5\u20136). Browser fingerprinting as user tracking technology. Proceedings of the 2017 11th International Conference on Intelligent Systems and Control (ISCO), Coimbatore, India.","DOI":"10.1109\/ISCO.2017.7855963"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Iqbal, U., Englehardt, S., and Shafiq, Z. (2021, January 24\u201327). Fingerprinting the Fingerprinters: Learning to Detect Browser Fingerprinting Behaviors. Proceedings of the 2021 IEEE Symposium on Security and Privacy (SP), San Francisco, CA, USA.","DOI":"10.1109\/SP40001.2021.00017"},{"key":"ref_39","unstructured":"(2021, December 04). OS and Application Fingerprinting Techniques|SANS Institute. Available online: https:\/\/www.sans.org\/white-papers\/32923\/."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Al-Shehari, T., and Shahzad, F. (2014). Improving operating system fingerprinting using machine learning techniques. Int. J. Comput. Theory Eng., 57\u201362.","DOI":"10.7763\/IJCTE.2014.V6.837"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Geradin, D., Katsifis, D., and Karanikioti, T. (2021, December 04). Google as a de Facto Privacy Regulator: Analyzing Chrome\u2019s Removal of Third-Party Cookies from an Antitrust Perspective. Available online: https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3738107#.","DOI":"10.2139\/ssrn.3738107"},{"key":"ref_42","first-page":"6","article-title":"Planning for a cookie-less future: How browser and mobile privacy changes will impact marketing, targeting and analytics","volume":"7","author":"Thomas","year":"2021","journal-title":"Appl. Market. Anal."},{"key":"ref_43","unstructured":"Wilander, J. (2021, December 04). Intelligent Tracking Prevention. Available online: https:\/\/webkit.org\/blog\/7675\/intelligent-tracking-prevention\/."},{"key":"ref_44","unstructured":"(2021, December 04). Today\u2019s Firefox Blocks Third-Party Tracking Cookies and Cryptomining by Default|The Mozilla Blog. Available online: https:\/\/blog.mozilla.org\/en\/products\/firefox\/todays-firefox-blocks-third-party-tracking-cookies-and-cryptomining-by-default\/."},{"key":"ref_45","unstructured":"(2021, December 04). The Privacy Sandbox\u2014The Chromium Projects. Available online: https:\/\/www.chromium.org\/Home\/chromium-privacy\/privacy-sandbox."},{"key":"ref_46","unstructured":"Bogna, J. (2021, December 04). What Is Google\u2019s FLoC, and How Will It Track You Online?. Available online: https:\/\/www.howtogeek.com\/724441\/what-is-googles-floc-and-how-will-it-track-you-online\/."},{"key":"ref_47","unstructured":"(2021, December 04). Chrome is Removing Third-Party Data. What\u2019s Next?. Available online: https:\/\/www.match2one.com\/blog\/how-removal-of-third-party-cookies-affects-digital-marketers\/."},{"key":"ref_48","first-page":"11","article-title":"Malvertising\u2013exploiting web advertising","volume":"2011","author":"Sood","year":"2011","journal-title":"Comput. Fraud Secur."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1109\/MSP.2014.107","article-title":"Accountable? The problems and solutions of online ad optimization","volume":"12","author":"Edelman","year":"2014","journal-title":"IEEE Secur. Priv. Mag."},{"key":"ref_50","unstructured":"(2021, December 04). What Is Ad Fraud and How to Prevent It?|CLICKTRUST. We Are CLICKTRUST. Available online: https:\/\/clicktrust.be\/en\/blog\/ppc\/what-is-ad-fraud-and-how-to-counter-it\/."},{"key":"ref_51","unstructured":"(2021, November 04). Ads.txt: A White Ops Perspective. Available online: https:\/\/www.humansecurity.com\/blog\/ads.txt-a-white-ops-perspective-1."},{"key":"ref_52","unstructured":"Vidakovic, R. (2021, December 04). The Beginner\u2019s Guide to Digital Ad Fraud. Available online: https:\/\/adprofs.co\/beginners-guide-to-digital-ad-fraud\/."},{"key":"ref_53","unstructured":"(2021, November 04). Comcast Wi-Fi Serving Self-Promotional Ads via JavaScript Injection|Ars Technica. Available online: https:\/\/arstechnica.com\/tech-policy\/2014\/09\/why-comcasts-javascript-ad-injections-threaten-security-net-neutrality\/."},{"key":"ref_54","unstructured":"Springborn, K., and Barford, P. (2013, January 14\u201316). Impression fraud in on-line advertising via pay-per-view networks. Proceedings of the 22nd USENIX Security Symposium (USENIX Security 13), Washington, DC, USA."},{"key":"ref_55","unstructured":"(2021, December 04). Dr. Augustine Fou\u2014Independent Ad Fraud Researcher. Ad Fraud Ecosystem 2017 Update, 11:52:29 UTC. Available online: https:\/\/www.slideshare.net\/augustinefou\/ad-fraud-ecosystem-2017-update."},{"key":"ref_56","unstructured":"(2021, November 04). What Is Retargeting and Which Problems Might Be Damaging Your Campaign?. Available online: https:\/\/www.cheq.ai\/retargeting."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1362\/146934717X14909733966092","article-title":"Affiliate marketing: An overview and analysis of emerging literature","volume":"17","author":"Dwivedi","year":"2017","journal-title":"Mark. Rev."},{"key":"ref_58","unstructured":"(2021, November 04). Adware\u2014What Is It & How to Remove It?. Available online: https:\/\/www.malwarebytes.com\/adware."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Dam, T., Klausner, L.D., and Schrittwieser, S. (2020). Typosquatting for fun and profit: Cross-country analysis of pop-up scam. J. Cyber Secur. Mobil., 265\u2013300.","DOI":"10.13052\/jcsm2245-1439.924"},{"key":"ref_60","unstructured":"Szurdi, J., Kocso, B., Cseh, G., Spring, J., Felegyhazi, M., and Kanich, C. (2014, January 20\u201322). The long \u201ctaile\u201d of typosquatting domain names. Proceedings of the 23rd USENIX Security Symposium, San Diego, CA, USA."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Chachra, N., Savage, S., and Voelker, G.M. (2015, January 28\u201330). Affiliate crookies: Characterizing affiliate marketing abuse. Proceedings of the 2015 Internet Measurement Conference, IMC\u201915, Association for Computing Machinery, New York, NY, USA.","DOI":"10.1145\/2815675.2815720"},{"key":"ref_62","first-page":"1","article-title":"Online Advertising Fraud","volume":"Volume 40","author":"Daswani","year":"2008","journal-title":"Crimeware: Understanding New Attacks and Defenses"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"961","DOI":"10.3233\/IFS-141378","article-title":"Image segmentation by generalized hierarchical fuzzy C-means algorithm","volume":"28","author":"Zheng","year":"2015","journal-title":"J. Intel. Fuzzy Syst."},{"key":"ref_64","unstructured":"Zhang, Y., Egelman, S., Cranor, L., and Hong, J. (2007). Phinding Phish: Evaluating Anti-Phishing Tools, Carnegie Mellon University."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1109\/MC.2009.306","article-title":"A Comparison of tools for detecting fake websites","volume":"42","author":"Abbasi","year":"2009","journal-title":"Computer"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Thomas, K., Bursztein, E., Grier, C., Ho, G., Jagpal, N., Kapravelos, A., Mccoy, D., Nappa, A., Paxson, V., and Pearce, P. (2015, January 17\u201321). Ad injection at scale: Assessing deceptive advertisement modifications. Proceedings of the 2015 IEEE Symposium on Security and Privacy, San Jose, CA, USA.","DOI":"10.1109\/SP.2015.17"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Almahmoud, S., Hammo, B., Al-Shboul, B., and Obeid, N. (2021). A Hybrid Approach for Identifying Non-Human Traffic in Online Digital Advertising. Multimed. Tools Appl., 1\u201334.","DOI":"10.1007\/s11042-021-11533-4"},{"key":"ref_68","unstructured":"Neal, A., Kouwenhoven, S., and Sa, O. (2015). Quantifying Online Advertising Fraud: Ad-Click Bots vs. Humans, Oxford Bio Chronometrics."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Zhang, L., and Guan, Y. (2008, January 17\u201320). Detecting click fraud in pay-per-click streams of online advertising networks. Proceedings of the 2008 28th International Conference on Distributed Computing Systems, Washington, DC, USA.","DOI":"10.1109\/ICDCS.2008.98"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Stitelman, O., Perlich, C., Dalessandro, B., Hook, R., Raeder, T., and Provost, F. (2013, January 11\u201314). Using Co-Visitation networks for detecting large scale online display advertising exchange fraud. Proceedings of the 19th ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, New York, NY, USA.","DOI":"10.1145\/2487575.2488207"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Tian, T., Zhu, J., Xia, F., Zhuang, X., and Zhang, T. (2015, January 18\u201322). Crowd fraud detection in internet advertising. Proceedings of the 24th International Conference on World Wide Web, WWW\u201915, International World Wide Web Conferences Steering Committee, Geneva, Switzerland.","DOI":"10.1145\/2736277.2741136"},{"key":"ref_72","unstructured":"Shekhter, H. (2011). System and Method for Detecting Fraudulent Affiliate Marketing in an Online Environment. (20110251869A1), U.S. Patent."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Budak, C., Goel, S., Rao, J., and Zervas, G. (2016, January 24\u201328). Understanding Emerging Threats to Online Advertising. Proceedings of the 2016 ACM Conference on Economics and Computation, Maastricht, The Netherlands.","DOI":"10.1145\/2940716.2940787"},{"key":"ref_74","unstructured":"(2021, November 04). Fight Ad Fraud with SecureAd. Fight Digital Fraud with Oxford BioChronometrics. Available online: https:\/\/oxford-biochron.com\/fight-ad-fraud-with-securead\/."},{"key":"ref_75","unstructured":"(2021, November 04). DoubleVerify\u2014DoubleVerify Authenticates the Quality of Digital Media for the World\u2019s Largest Brands Ensuring Viewable, Fraud-Free, Brand-Safe Ads. Available online: https:\/\/doubleverify.com\/company\/."},{"key":"ref_76","unstructured":"HUMAN (2021, November 04). HUMAN|Bot Mitigation|Know Who\u2019s Real. Available online: https:\/\/www.humansecurity.com."},{"key":"ref_77","unstructured":"(2021, November 04). Integral Ad Science|Digital ad Tech & Verification. Available online: https:\/\/integralads.com\/uk\/."},{"key":"ref_78","unstructured":"Limited, C. (2021, November 04). \u00a9 2021 P. E. Pixalate\u2014Ad Fraud Protection, Privacy, and Compliance Platform (CTV). Available online: https:\/\/www.pixalate.com."},{"key":"ref_79","unstructured":"(2021, November 04). Ad Fraud Protect & Monitor: Stop Affiliate, Influencer Fraud. Available online: https:\/\/impact.com\/protect-monitor\/."},{"key":"ref_80","unstructured":"(2021, November 12). ClickGUARDTM|Leading Click Fraud Protection Software. Available online: https:\/\/www.clickguard.com\/."},{"key":"ref_81","unstructured":"(2021, November 04). Measurement, Analytics, & Brand Safety|Moat by Oracle Data Cloud. Available online: https:\/\/www.moat.com\/."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Li, Z., Zhang, K., Xie, Y., Yu, F., and Wang, X. (2012, January 16\u201318). Knowing your enemy: Understanding and detecting malicious web advertising. Proceedings of the 2012 ACM Conference on Computer and Communications Security, CCS \u201912, Association for Computing Machinery, New York, NY, USA.","DOI":"10.1145\/2382196.2382267"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Kantardzic, M., Walgampaya, C., Yampolskiy, R., and Woo, R.J. (2010, January 5\u20137). Click Fraud Prevention via multimodal evidence fusion by Dempster-Shafer theory. Proceedings of the 2010 IEEE Conference on Multisensor Fusion and Integration, Salt Lake City, UT, USA.","DOI":"10.1109\/MFI.2010.5604480"},{"key":"ref_84","unstructured":"Ge, L., King, D., and Kantardzic, M. (2021, November 12). Collaborative Click Fraud Detection and Prevention System (CCFDP) Improves Monitoring of Software-Based Click Fraud. Available online: http:\/\/citeseerx.ist.psu.edu\/viewdoc\/download?doi=10.1.1.420.8672&rep=rep1&type=pdf#page=53."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1145\/1764873.1764877","article-title":"Fighting online click-fraud using bluff ads","volume":"40","author":"Haddadi","year":"2010","journal-title":"ACM SIGCOMM Comput. Commun. Rev."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Dave, V., Guha, S., and Zhang, Y. (2012, January 13\u201317). Measuring and fingerprinting click-spam in ad networks. Proceedings of the ACM SIGCOMM 2012 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, Association for Computing Machinery, New York, NY, USA.","DOI":"10.1145\/2342356.2342394"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Nagaraja, S., and Shah, R. (2019, January 15\u201317). Clicktok: Click Fraud Detection Using Traffic Analysis. Proceedings of the 12th Conference on Security and Privacy in Wireless and Mobile Networks, WiSec \u201919, Association for Computing Machinery, New York, NY, USA.","DOI":"10.1145\/3317549.3323407"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Basit, A., Zafar, M., Javed, A.R., and Jalil, Z. (2020, January 5\u20137). A Novel Ensemble Machine Learning Method to Detect Phishing Attack. Proceedings of the 2020 IEEE 23rd International Multitopic Conference (INMIC), Bahawalpur, Pakistan.","DOI":"10.1109\/INMIC50486.2020.9318210"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1007\/s11235-017-0414-0","article-title":"Towards detection of phishing websites on client-side using machine learning based approach","volume":"68","author":"Jain","year":"2017","journal-title":"Telecommun. Syst."},{"key":"ref_90","first-page":"100016","article-title":"A hybrid and effective learning approach for Click Fraud detection","volume":"3","author":"Thejas","year":"2020","journal-title":"Mach. Learn. Appl."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Thejas, G.S., Soni, J., Boroojeni, K.G., Iyengar, S.S., Srivastava, K., Badrinath, P., Sunitha, N.R., Prabakar, N., and Upadhyay, H. (2019, January 20\u201321). A multi-time-scale time series analysis for click fraud forecasting using binary labeled imbalanced dataset. Proceedings of the 2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS), Bengaluru, India.","DOI":"10.1109\/CSITSS47250.2019.9031036"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.jnca.2018.02.021","article-title":"An ensemble learning based approach for impression fraud detection in mobile advertising","volume":"112","author":"Haider","year":"2018","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Snyder, P., and Kanich, C. (2015). No Please, After You: Detecting Fraud in Affiliate Marketing Networks. WEIS, Available online: https:\/\/www2.cs.uic.edu\/~ckanich\/papers\/snyder2015noplease.pdf.","DOI":"10.1093\/cybsec\/tyw006"},{"key":"ref_94","unstructured":"(2021, November 04). Best Practices for Ad Placement\u2014Google AdSense Help. Available online: https:\/\/support.google.com\/adsense\/answer\/1282097?hl=en."},{"key":"ref_95","unstructured":"(2021, November 04). About Confirmed Click\u2014Google AdMob Help. Available online: https:\/\/support.google.com\/admob\/answer\/10094971?hl=en#zippy=%2Chow-can-i-fix-accidental-clicks-on-my-ad-units."},{"key":"ref_96","unstructured":"Jakobsson, M., and Ramzan, Z. (2008). Crimeware: Understanding New Attacks and Defenses, Addison-Wesley Professional."},{"key":"ref_97","unstructured":"(2021, November 04). A Digital Publisher s Guide to Measuring and Mitigating Non-Human Traffic\u2014PDF Free Download. Available online: https:\/\/businessdocbox.com\/Advertising\/74441712-A-digital-publisher-s-guide-to-measuring-and-mitigating-non-human-traffic.html."},{"key":"ref_98","unstructured":"(2021, November 04). Bot Baseline: Fraud in Digital Advertising. Available online: https:\/\/www.ana.net\/miccontent\/show\/id\/rr-2019-bot-baseline."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Stone-Gross, B., Stevens, R., Zarras, A., Kemmerer, R., Kruegel, C., and Vigna, G. (2011, January 15\u201319). Understanding fraudulent activities in online ad exchanges. Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference, Association for Computing Machinery, New York, NY, USA.","DOI":"10.1145\/2068816.2068843"},{"key":"ref_100","unstructured":"Kienle, H.M., German, D., and Muller, H. (2004, January 11). Legal concerns of web site reverse engineering. Proceedings of the Sixth IEEE International Workshop on Web Site Evolution Proceedings, Chicago, IL, USA."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"2124","DOI":"10.1109\/COMST.2016.2519912","article-title":"In-depth survey of digital advertising technologies","volume":"18","author":"Chen","year":"2016","journal-title":"IEEE Commun. Surv. Tutorials"},{"key":"ref_102","first-page":"156","article-title":"Marketing professionals\u2019 views on online advertising fraud","volume":"42","year":"2020","journal-title":"J. Curr. Issues Res. Advert."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"122","DOI":"10.2501\/JAR-2016-024","article-title":"Fraud in digital advertising: A multibillion-dollar black hole: How marketers can minimize losses caused by bogus web traffic","volume":"56","author":"Fulgoni","year":"2016","journal-title":"J. Advert. Res."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1109\/MC.2018.2887322","article-title":"Online advertising fraud","volume":"52","author":"Kshetri","year":"2019","journal-title":"Computer"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1177\/0022242920913236","article-title":"Inefficiencies in digital advertising markets","volume":"85","author":"Gordon","year":"2020","journal-title":"J. Mark."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"1512","DOI":"10.1587\/transinf.2019ICP0008","article-title":"Detecting and understanding online advertising fraud in the wild","volume":"E103","author":"Kanei","year":"2020","journal-title":"IEICE Trans. Inf. Syst."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1080\/02650487.2019.1642015","article-title":"Digital advertising: Present and future prospects","volume":"39","author":"Lee","year":"2019","journal-title":"Int. J. Advert."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"263","DOI":"10.2501\/JAR-2018-035","article-title":"Artificial intelligence in advertising: How marketers can leverage artificial intelligence along the consumer journey","volume":"58","author":"Kietzmann","year":"2018","journal-title":"J. Advert. Res."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1080\/00913367.2019.1652122","article-title":"The impact of AI on the advertising process: The chinese experience","volume":"48","author":"Qin","year":"2019","journal-title":"J. Advert."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1080\/00913367.2019.1654421","article-title":"Understanding programmatic creative: The role of AI","volume":"48","author":"Chen","year":"2019","journal-title":"J. Advert."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1080\/00913367.2019.1652121","article-title":"Smart Generation system of personalized advertising copy and its application to advertising practice and research","volume":"48","author":"Deng","year":"2019","journal-title":"J. Advert."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1080\/00913367.2019.1652123","article-title":"An algorithm for allocating sponsored recommendations and content: Unifying programmatic advertising and recommender systems","volume":"48","author":"Malthouse","year":"2019","journal-title":"J. Advert."},{"key":"ref_113","unstructured":"Alcantara, C., Schaul, K., Vynck, G.D., and Albergotti, R. (2021, November 04). How Big Tech Got So Big: Hundreds of Acquisitions. Available online: https:\/\/www.washingtonpost.com\/technology\/interactive\/2021\/amazon-apple-facebook-google-acquisitions\/."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"012018","DOI":"10.1088\/1742-6596\/1757\/1\/012018","article-title":"Research on advertising core business reformation driven by artificial intelligence","volume":"1757","author":"Lai","year":"2021","journal-title":"J. Physics Conf. Ser."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1080\/00913367.2019.1654947","article-title":"Special section introduction: Artificial intelligence and advertising","volume":"48","author":"Li","year":"2019","journal-title":"J. Advert."},{"key":"ref_116","first-page":"106","article-title":"Artificial intelligence: Risks to privacy and democracy","volume":"21","author":"Manheim","year":"2019","journal-title":"Yale JL Tech."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.jbusres.2021.01.055","article-title":"The evolving role of artificial intelligence in marketing: A review and research agenda","volume":"128","author":"Corbo","year":"2021","journal-title":"J. Bus. Res."},{"key":"ref_118","unstructured":"(2021, November 04). Juniper Research: Advertising Fraud Losses to Reach $42 Billion in 2019, Driven by Evolving Tactics by Fraudsters. Available online: https:\/\/www.businesswire.com\/news\/home\/20190520005650\/en\/Juniper-Research-Advertising-Fraud-Losses-to-Reach-42-Billion-in-2019-Driven-by-Evolving-Tactics-by-Fraudsters."},{"key":"ref_119","unstructured":"Li, Y. (2017). Deep reinforcement learning: An overview. arXiv."}],"container-title":["Journal of Cybersecurity and Privacy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2624-800X\/1\/4\/39\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:49:46Z","timestamp":1760168986000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2624-800X\/1\/4\/39"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,16]]},"references-count":119,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["jcp1040039"],"URL":"https:\/\/doi.org\/10.3390\/jcp1040039","relation":{},"ISSN":["2624-800X"],"issn-type":[{"value":"2624-800X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,16]]}}}