The Bot Baseline Fraud in Digital Advertising

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Appendix AGlossary of Terms (Continued)Man-in-the-Browser AttackAn Internet attack that infects a user’s online interactionsby taking advantage of vulnerabilities in browser or appsecurity to modify ads, web pages, or transaction contentor to insert additional ads, content, or transactions,without the knowledge or consent of the user or theresource(s) with which the user intended to interactMicro-BlacklistA blacklist that is updated and expires frequently, toenhance its effectiveness against advanced and adaptivethreatsPage ViewA single request to load a single page of a websitePhantom LayerWebsites operated specifically for the purpose oflaundering ad fraud by obscuring the source of inventoryand impressions entering the online advertising ecosystemPop-UnderWindows that appear or open under the user’s currentbrowser window so that they become visible when thatwindow is closedPop-upWindows that appear or open above or on top of the user’scurrent browser windowPublisherThe operator of a website or network of websites, andthe producer or curator of content for those sites. A sellerof online advertising space and impressions, and oftena buyer of third-party trafficRON (Run-of-Network)An ad or campaign displayed on a large collection ofwebsites without the ability to choose target-specificsites, placements, or domainsSite or WebsiteA set of related web pages, often served from a singledomainSSP (Supply-Side Platform)A technology platform that enables publishers to managetheir ad inventory and maximize revenue from onlineadvertising, usually by interfacing with ad exchanges, andmaking their ad placement inventory available in an automatedfashion to a wide number of potential purchasersSupplierA seller of traffic to publishers and sitesTrafficVisits to a particular site, page, or other online resource;impressions related to a particular adTraffic Sourcing or Sourced TrafficAny method by which publishers acquire more visitorsthrough third partiesTrue DomainThe domain on which an ad actually ran, as determined bydomain detectionUserA person who uses a computer or other device or networkservice. In the context of online advertising, a visitor to apublisher’s site, and a consumer of an advertiser’s adsReachThe total number of different users exposed, at least once,to an ad or campaign during a given period of timeRetargeting (Behavioral Retargeting)The process of delivering ads to particular users basedon their previous online activity54
Appendix BConstraints and LimitationsComplexity of StudyStudy participants joined the initiative for differentperiods of time with varying platform configurations,target audiences, industry verticals, and ad agencies.Because of these differences, not all data from thestudy can be compared directly between participants.Web Framework LimitationsThe study software could only be deployed to systemsthat were JavaScript-enabled.Public Study AwarenessBecause many participants experienced administrativeand technical deployment delays during the publicstudy phase, and because bot numbers may have beenartificially decreased during the public study phase,bot numbers detected during the covert study phasemay be more representative of the numbers occurringin normal ad campaigns outside of the initiativeframework. Because the study was announcedand widely known, it is assumed that bot numbersfor the month of the public study were artificially lowerthan numbers we might otherwise observe.Coordinating With AgenciesSome study participants were not able to deploy thestudy software uniformly throughout their campaignsdue to administrative elements including legalagreements, site policies, and organizational complexity.In some cases, the study participants were not awarethat study software had not been deployed throughtheir ad agencies. When these issues became apparentduring the monitoring and data collection phases, WhiteOps worked with the study participants and their adagencies to attempt to correct the problem.Seasonal Time FrameWhite Ops expects bot numbers to be at their lowestin the late summer months and at their highest whendemand for advertising is highest near the end of thecalendar year. This seasonal snapshot from the monthsof August and September cannot predict the numberof bots in a typical month or during peak months whenadvertising volume is higher.55
Page 1 and 2:
The Bot Baseline:Fraud in DigitalAd
Page 3 and 4: Special Thanks to the FollowingANA
Page 5 and 6: ABOUT THE STUDY
Page 7 and 8: BOTS ARE EVERYWHERE...BUT NOT IN EQ
Page 9 and 10: HOW WE USED THE DATAThis study is a
Page 11 and 12: BOTS WERE IN EVERY KINDOF CAMPAIGN
Page 13 and 14: BOT FRAUD VARIES OVER TIMENot all b
Page 15 and 16: BOT TRAFFIC VARIES BY DOMAIN CATEGO
Page 17 and 18: SOURCING TRAFFIC INCREASESBOT LEVEL
Page 19 and 20: BOTS GET INTO PREMIUM BUYSSignifica
Page 21 and 22: TO REDUCE BOTS IN PREMIUM CAMPAIGNS
Page 23 and 24: BOTS ARE BUILT TO FOOL ALL STAKEHOL
Page 25 and 26: ADWARE: NOT ALL ATTACKS ARE THE SAM
Page 27 and 28: BOTNET CONTROLLERS HIJACK EVERYDAYU
Page 29 and 30: BOTS FAKED ALL OF THE ENGAGEMENT AN
Page 31 and 32: BOTS ARE GETTING TARGETED AND RETAR
Page 33 and 34: HOW BOTSUPPLIERS GETAWAY WITH IT:EV
Page 35 and 36: BOT SUPPLIERS ACTIVELY DISGUISE BOT
Page 37 and 38: SITES THAT ONLYA BOT COULDLOVE37
Page 39 and 40: BOT SUPPLIERS ARE WATCHING YOUBot t
Page 41 and 42: WHEN PUBLISHERSARE VICTIMS TOO:AD I
Page 43 and 44: AD INJECTIONDEGRADES THEQUALITY OFT
Page 45 and 46: BOTNET OPERATORS ARE ALREADYDEFEATI
Page 47 and 48: Action Plan for All StakeholdersCre
Page 49 and 50: Action Plan for Buyers(Continued)Ap
Page 51 and 52: Who is your real audience:BOB SMITH
Page 53: Appendix AGlossary of Terms (Contin
Page 57: Appendix DIllustrative Terms and Co
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The Bot Baseline Fraud in Digital Advertising

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