Fraud Prevention in Online Digital Advertising

In this chapter, we briefly introduce the computational advertising, including search advertising and display advertising. We explain the reality of fraud in digital advertising, and summarize types of fraud methods commonly observed in the industry for making illicit returns. 1.1 Computational Advertising Recent advancement in networking and communication technologies have witnessed a rapid growth of digital advertising [1], which uses the Internet to promote and deliver advertisements (Ad) to consumers [24]. Compared to traditional media, such as Radio, TV, or news papers, the Internet offers tremendous advantages such as real-time interaction, consumer information availability, transparent user engagement, and effective assessment of the campaign results etc. As a result, on-line digital advertising is quickly dominating the advertising market, and its market revenue is projected to reach over $250 billions in 2018 [22]. One of the prominent and most sought characteristics of the Internet is that it allows the Ad industry to obtain fine-grained information from specific geographic locations, regions, households, or even individual users, and is able to serve highly customized advertisement to users in real-time. Such tools and methods, used in digital advertising, are commonly referred to as computational advertising, which mainly covers two types of advertising: search advertising and display advertising. The essential goal is to identify the user context, such that the Ads most interesting to the users are served with minimum advertising costs. In search advertising [9, 14] (also referred to as sponsored search), the context information is obtained through the search query users provided to the system. More specifically, the search keywords users entered in the search engine are used to find users’ interests and the best matching Ads are then displayed to the users, along © The Author(s) 2017 X. Zhu et al., Fraud Prevention in Online Digital Advertising, SpringerBriefs in Computer Science, DOI 10.1007/978-3-319-56793-8_1 1

[1]  Christopher Krügel,et al.  Understanding fraudulent activities in online ad exchanges , 2011, IMC '11.

[2]  Paul Barford,et al.  Impression Fraud in On-line Advertising via Pay-Per-View Networks , 2013, USENIX Security Symposium.

[3]  Angelos Stavrou,et al.  Click Fraud Detection on the Advertiser Side , 2014, ESORICS.

[4]  JOHN B. KILLORAN,et al.  How to Use Search Engine Optimization Techniques to Increase Website Visibility , 2013, IEEE Transactions on Professional Communication.

[5]  S. Savage,et al.  Got traffic?: an evaluation of click traffic providers , 2011, WebQuality '11.

[6]  R. Govindan,et al.  AdReveal : Improving Transparency and Control in Online Targeted Advertising , 2012 .

[7]  Chris Kanich,et al.  No Please, After You: Detecting Fraud in Affiliate Marketing Networks , 2015, WEIS.

[8]  Jean-Pierre Hubaux,et al.  Online Advertising Fraud , 2011 .

[9]  Bobji Mungamuru,et al.  Competition and Fraud in Online Advertising Markets , 2008, Financial Cryptography.

[10]  Wei Meng,et al.  Understanding Malvertising Through Ad-Injecting Browser Extensions , 2015, WWW.

[11]  Dawn Xiaodong Song,et al.  Insights from the Inside: A View of Botnet Management from Infiltration , 2010, LEET.

[12]  Vern Paxson,et al.  What's Clicking What? Techniques and Innovations of Today's Clickbots , 2011, DIMVA.

[13]  Yong Guan,et al.  Detecting Click Fraud in Pay-Per-Click Streams of Online Advertising Networks , 2008, 2008 The 28th International Conference on Distributed Computing Systems.

[14]  Lorrie Faith Cranor,et al.  Phinding Phish: An Evaluation of Anti-Phishing Toolbars , 2007, NDSS.

[15]  Yin Zhang,et al.  Measuring and fingerprinting click-spam in ad networks , 2012, SIGCOMM.

[16]  Tyler Moore,et al.  Measuring the Perpetrators and Funders of Typosquatting , 2010, Financial Cryptography.

[17]  Hamed Haddadi,et al.  Fighting online click-fraud using bluff ads , 2010, CCRV.

[18]  Tong Zhang,et al.  Crowd Fraud Detection in Internet Advertising , 2015, WWW.

[19]  Vern Paxson,et al.  Ad Injection at Scale: Assessing Deceptive Advertisement Modifications , 2015, 2015 IEEE Symposium on Security and Privacy.

[20]  Divyakant Agrawal,et al.  Using Association Rules for Fraud Detection in Web Advertising Networks , 2005, VLDB.

[21]  Stefan Savage,et al.  Cloak and dagger: dynamics of web search cloaking , 2011, CCS '11.

[22]  Fang Yu,et al.  Knowing your enemy: understanding and detecting malicious web advertising , 2012, CCS '12.

[23]  Foster J. Provost,et al.  Using co-visitation networks for detecting large scale online display advertising exchange fraud , 2013, KDD.

[24]  Ali Jalali,et al.  Real time bid optimization with smooth budget delivery in online advertising , 2013, ADKDD '13.

[25]  Saikat Guha,et al.  Characterizing Large-Scale Click Fraud in ZeroAccess , 2014, CCS.

[26]  Richard J. Enbody,et al.  Malvertising – exploiting web advertising , 2011 .

[27]  P. Nowak.  Deceptibots: when machines go bad , 2012 .