Impression Fraud in Online Advertising via Pay-Per-View Networks

16 Pages·2013·0.68 MB·English

Impression Fraud in Online Advertising via Pay-Per-View
Networks
Kevin Springborn Paul Barford
BroadcastInteractiveMedia BroadcastInteractiveMedia
[email protected] UniversityofWisconsin-Madison
[email protected]
ABSTRACT Theonlineadecosystemcanroughlybedividedintothree
groups: advertisers, publishers and intermediaries. Adver-
Advertisingisoneoftheprimarymeansforrevenuegener-
tiserspaypublisherstoplaceaspecifiedvolumeofcreative
ation for millions of websites and mobile apps. While the
contentwithembeddedlinks(i.e.,text,displayorvideoads)
majority of online advertising revenues are based on pay-
onwebsitesandapps. Intermediaries(e.g.,adserversandad
per-click, alternative forms such as impression-based dis-
exchanges)areoftenusedtofacilitateconnectionsbetween
play and video advertising have been growing rapidly over
publishers and advertisers. Intermediaries typically place a
thepastseveralyears. Inthispaper,weinvestigatetheprob-
surchargeonthefeespaidbyadvertiserstopublishersforad
lem of invalid traffic generation that aims to inflate adver-
placements and/or ad clicks. What is immediately obvious
tising impressions on websites. Our study begins with an
fromthissimpledescriptionisthatpublisherandintermedi-
analysisofpurchasedtrafficforasetofhoneypotwebsites.
aryplatformrevenuesaredirectlytiedtothenumberofdaily
Data collected from these sites provides a window into the
visitstoawebsiteorapp. Thus, therearestrongincentives
basic mechanisms used for impression fraud and in partic-
forpublishersandintermediariestouseanymeansavailable
ular enables us to identify pay-per-view (PPV) networks.
todriveusertraffictopublishersites.
PPVnetworksarecomprisedoflegitimatewebsitesthatuse
There are certainly legitimate methods for traffic gener-
JavaScript provided by PPV network service providers to
ation for publisher sites. The most widely used are the
renderunwantedwebpages"underneath"requestedcontent
text-based ad words that appear in search results e.g., from
on a real user’s browser so that additional advertising im-
Google or Bing. However, it can be quite difficult and ex-
pressions are registered. We describe the characteristics of
pensive to drive large traffic volumes to target sites using
thePPVnetworkecosystemandthetypicalmethodsforde-
ad words alone.1 Thus, other methods for traffic genera-
liveringfraudulentimpressions.Wealsoprovideacasestudy
tionhaveemerged,manyofwhicharedeededasfraudulent
ofscopeofPPVnetworksintheInternet. Ourresultsshow
by advertisers and intermediaries. Google defines invalid
that these networks deliver hundreds of millions of fraudu-
(fraudulent)trafficasfollows:2
lent impressions per day, resulting in hundreds of millions
of lost advertising dollars annually. Characteristics unique Invalid traffic includes both clicks and impres-
totrafficdeliveredviaPPVnetworksarealsodiscussed. We sions that Google suspects to not be the result
conclude with recommendations for countermeasures that ofgenuineuserinterest[21].
canreducethescopeandimpactofPPVnetworks.
Standard methods for generating invalid traffic includes (i)
1. INTRODUCTION using employees at publisher companies to view sites and
click on ads, (ii) hiring 3rd parties to view sites and click
Advertisingisoneoftheprimarymethodsforgenerating
onads,(iii)click/viewpyramidschemesand(iv)usingsoft-
revenues from websites and mobile apps. A recent report
wareand/orbotnetstoautomateviews/clicks[21].Thechal-
from the Internet Advertising Bureau (IAB) places ad rev-
lengesforadvertisersandintermediariesfocusedonoffering
enuesintheUSforthefirsthalfof2012at$17B,whichrep-
trustworthyplatformsaretounderstandtheseandpotentially
resents a 14% increase over the previous year [15]. While
other threats so that effective countermeasures can be de-
the majority of that revenue is search-based, ad words ad-
ployed.
vertising,displayandvideoadvertisinghavebeengrowing.
Inthispaper,weinvestigatearelativelynewthreatfordis-
Indeed, a recent report places display and video advertis-
play and video advertising called Pay-Per-View (PPV) net-
ingintheUSat$12.7BforFY2012,growingat17%annu-
1ThishasledtotheemergenceofalargenumberofSearchEngine
ally[27]. Atahighlevelthebasicnotionofsellingspaceon
Optimizationcompaniesinrecentyears.
webpagesandappsforadvertisingissimple. However,the
2WhileGoogleisnottheonlycompanyinthisdomain,wereferto
mechanisms and infrastructure that are required for online themasanauthoritativesourceofinformationduetotheirsizeand
advertisingarehighlydiverseandcomplex. experienceinonlineadvertising.
1
works. ThebasicideaforPPVnetworksistopaylegitimate canbecompiledthroughprogrammaticenumerationofPPV
publishers to run specialized JavaScript when users access destinations.Finally,refererfieldscanbequeriedatthetime
their sites that will display other publishers websites in a ofadvertisementloadinordertoidentifytrafficoriginating
camouflagedfashion.Thiswillresultinimpressionsandpo- fromknownPPVdomains.
tentially even clicks that are registered on the camouflaged The remainder of this paper is organized as follows. In
pageswithout"genuineuserinterest"i.e.,invalidtrafficgen- Section 2 we provide a description of the online advertis-
eration. Legitimate publishers view this as another way to ingecosystemandanoverviewofinvalidtrafficgeneration
monetizetheirsiteswithoutimpacttotheirusers. PPVnet- threats. In Section 3, we describe the details of our hon-
works sell their traffic generation capability by touting real eypot websites and our traffic purchases for these sites. In
anduniqueusers,geolocationandcontextspecificityamong Section4,wedescribethedetailsoftheevaluationsthatwe
otherthings. Thefactthatpagesareappearingonrealusers’ conduct on our data including analyses of additional data
systemsmakesdetectingandpreventingPPVtrafficgenera- setsandmeasurementsthatenableustoprojectsomeofthe
tionchallenging. broader characteristics of PPV networks. We provide rec-
TostudyPPVnetworks,weemployasmallsetofhoney- ommendations for counter measures that can be employed
potwebsitesthatweuseasthetargetfortrafficgeneration. toreducetheimpactofPPVnetworksinSection5. Wedis-
These sites were constructed to include what appears to be cuss prior studies that inform our work in Section 6. We
legitimate content and advertising. We then use search to summarize,concludeanddiscussfutureworkinSection7.
identifyawidevarietyoftrafficgenerationofferingsonthe
Internet.Wepurchasedimpressionsforourhoneypotsitesin 2. ONLINEADVERTISINGECOSYSTEM
variousquantitiesfromaselectionofdifferenttrafficgener-
Inthissectionweprovideanoverviewoftheonlineadver-
ationservicesoverthecourseofa3.5monthperiod. Byen-
tisingecosystemincludingboththebusinessframeworkand
gagingwithtrafficgenerationservicesdirectly,wewereable
technical framework for delivering advertisements to pub-
touncoverthebasicmechanismsofPPVnetworksandini-
lisherwebsitesandapps. Somepriorstudieshaveprovided
tiate additional measurements to characterize their deploy-
similaroverviewsincluding[16,34,41]. Wealsoprovidean
ments.
overview of invalid traffic generation threats and the chal-
Thecharacteristicsofthetrafficpurchasedforourhoney-
lengestheyposeintheecosystem.
pot sites is dictated at a high level by the service offerings,
which enable volume, time frame and geographic location, 2.1 BusinessFramework
etc. of users to be specified. Our results show that impres-
As mentioned in Section 1, there are three main partici-
sionsaretypicallyspreadinasomewhatburstyfashionover
pantgroupsinadnetworks: advertisers, intermediariesand
thespecifiedtimeframeandthatusercharacteristicsarewell
publishers. As shown in Figure 1 there are two other im-
matchedwithspecifications. Byconsideringtherefererfield
portant groups: brands and users. Brands pay advertisers
oftheincomingtraffic,wewereabletoidentifythefactthat
tohelpthemselltheirproductsandservices. Internet-based
our honeypot sites were being loaded into a frame (along
campaignsareattractivetobrandsandadvertiserssincecon-
with as many as ten other sites) for display on remote sys-
sumers/users spend a growing proportion of their time on-
tems. By considering names of a small selection of traf-
line. An important appeal of online advertising (especially
fic generation services, we use a recent, publicly-available,
for consumer goods) is that it offers the opportunity to tie
Internet-wide web crawl to identify the scope of PPV net-
ad campaigns and associated costs directly to sales e.g., by
works. Wefindtagsfromtheseservicesare,infact,widely
tracking clicks from online ads to purchases on a brand’s
deployed – on tens of thousands of sites. By appealing to
ecommercesite.
MuStat [29], we conservatively estimate the number of in-
Advertisersarecompaniesthatcreateandmanageadver-
valid impressions that are generated from this small set of
tising campaigns for brands. Advertisers pay publishers to
PPVnetworkstobeontheorderof500millionperday. As-
makeadplacementsonwebsitesandappsusingoneofsev-
sumingamodestqualitylevelforsitesthatarepartofPPV
eraldifferentmodels. OneisthewidelyusedPay-Per-Click
networks,weestimatetheannualcosttoadvertisersforthis
(PPC)model,whereanadvertiseronlypaysapublisherfor
invalidtraffictobeontheorderof$180millionannually.
anadwhenauserclicksonit. PPCcampaignsaretypically
Finally,weofferthreedifferentmethodstodefendagainst
associatedwithadwords(short,text-basedads)campaigns.
PPV networks. First, observing viewport dimensions of ad
An alternative payment method that is common in display
requestscandetermineiftheendusercanpossiblyviewthe
and video advertising is Cost Per Mille/Thousand (CPM),
advertisement. Inanefforttoincreasetraffic,PPVnetworks
where advertisers pay publishers whenever users view an
commonly display destinations in zero sized frames. Sec-
ad (CPM prices are given per thousand impressions). The
ond,blacklistsofwebsitesthatparticipateinPPVnetworks
CPM-basedpaymentmodelistheprimaryfocusforthispa-
canpotentiallybeused. Theideaistoblockadvertisingon
per. The goal for advertisers is to place ads on sites that
websites that commonly receive PPV traffic until the pub-
they believe attract a brand’s target demographic in a cost-
lisher discontinues purchasing PPV traffic. Such blacklists
effectivefashion.Thus,theirchallengeisinidentifyingthese
2
Figure 1: Key participants in the online advertising
ecosystem. Paymentsflowfrombrandstoadvertisersto
intermediariesandpublishers.
Figure 2: Typical data exchanges required to render an
adinauser’sbrowser.(1)Userrequesttopublisherpage.
sitesandfacilitatingadplacement.
(2) Base page delivered. (3) Ad tag request to CDN. (4)
Inadditiontoworkingwithpublishersdirectly,advertisers
JavaScript delivered. (5) Update to JavaScript in CDN
oftenworkwithintermediariesinordertoactuallyplaceads
ifnecessary. (6)Requesttoadserver. (7)Redirectedre-
onwebsitesandapps. Thetwomainreasonsforthisarethe
questdelivered. (8)Requesttoexchangeor3rdpartyad
complexityofInternetadvertising’stechnicallandscape(see
server. (9)Adcreativedelivered.
below)andtheenormousandgrowingdiversityofwebsites
and apps. Among other things, intermediaries offer "one-
hostedinaCDNinfrastructure.
stopshopping"foradvertisers,andcompetitiveCPMratesto
TheJavaScripttypicallygatherscontextkeywordsandother
publisherswhomaynotbeabletofillalloftheirplacements
information from the publisher page or user browser and
viadirectcampaigns.
then sends an ad request to the target ad server infrastruc-
Thescopeofintermediariesisquitebroad.Themostcom-
ture. Ad servers process the ad request and either respond
mon offerings include targeting services, ad servers and ad
withanaddirectly(e.g.,fromadirectadvertisercampaign)
exchangestofacilitateplacements. Oneofthemostwidely
or send a redirect to a third party such as an ad exchange.
usedintermediariesinthedisplayadvertisingspaceisGoogle
Theredirectisforwardedbybrowsertothetargetserveror
AdExchange(AdX)[20,30].Therevenuemodelthatismost
exchange,whichwillrespondwithanadthatisrenderedin
commonly used by intermediaries is to take a small CPM
the browser. The redirect usually includes sufficient infor-
paymentforeachadthattheyparticipateinservingandthen
mationforadtargetingandbilling. Thisentireprocessmust
to pass the remainder of the CPM paid by the advertiser to
takeplacequickly(typicallyontheorderoftensofmillisec-
thepublisher.
onds) in order to ensure a good user experience. When the
Internetpublishersarecompaniesthatcreatecontentthat
ad is delivered, an impression is registered for the ad serv-
isofinteresttousers. Publishersdisplayadsontheirpages
ingentity. Clicktrackingistypicallymanagedbydirecting
using standard sized creatives that typically appear in an
clickstotheadserver,whichthenredirectstotheadvertiser.
iframe.Apublisher’sgoalistomaximizetheirrevenueyield
by attracting (i) premium advertisers that pay high CPM’s
2.3 InvalidTrafficGenerationThreats
and(ii)ahighvolumeofusers,somewhomwillclickthrough
onads. Itisimportanttonotethatwhileadwords-basedad- Impression-based advertising has a number of potential
vertising(e.g.,throughAdSense)iswidelyavailable,display threats. Thefocusofthispaperisontrafficgenerationthat
and video ads are typically only available to sites that have causesinvalidimpressionandtherebyinflatespublisherand
somewhathighervolumesofusers. (some)intermediaryrevenues. Specifically,wefocusonin-
validtrafficgenerationviaPPVnetworks,whichwedescribe
2.2 TechnicalFramework
indetailinSection4.
Displayinganadvertisementonapublisher’spageincludes Validmethodsfortrafficgenerationincludesearchandad
potentially a large number of data exchanges between par- words-basedadvertising. However, websearchrevealsthat
ticipantsintheadvertisingecosystem. Asimpleexampleis there is a wide variety of other traffic generation offerings
depictedinFigure2. Theprocessbeginswiththeplacement available. Manyofferaspecifiedvolumeoftrafficatatarget
of an ad tag in a section of a publisher page. Ad tags (of- siteoveraspecifiedtimeperiod. Manyalsoincludeguaran-
ten supplied by intermediaries that manage ad servers) are teesofspecificfeaturesinthetrafficsuchasgeographiclo-
simpleHREFstringsthattypicallyreferenceJavaScriptcode cationsofhostsystems. Mostdonotdescribetheirmethod-
3
ology in detail if at all. One of the important objectives of
traffic generation is that it appear to come from real users.
Appealing to the definition of invalid traffic given in Sec-
tion1above,therearemanywaysinwhichsuchtrafficmight
begenerated.
Commonmethodsforinvalidtrafficgenerationhavebeen
borrowed directly from click generation services that have
been offered for some time. Examples include hiring peo-
ple to view pages, bots of various types, and using expired
domainstodivertusersto3rd-partypages.
PPV networks are sites that load 3rd-party pages in an
obfuscatedfashionwhenaccessedbyusers. Publishersbe-
comepartofaPPVnetworksimplybyplacingatagontheir
Figure3:Screenshotofoneofthehoneypotwebsitesthat
sitethatlooksverymuchlikeastandardadtag. Wedefine
wasatargetfortrafficgenerationpurchases.
a "network" as a series of sites that run tags from the same
PPVservice. ParticipatingpublishersarepaidonaCPMba-
tisementplacements,identicaltostandardCPMplacements
sisforsomethingthatappearstobelowornoimpactontheir
excepttheycontaineddummycreativesinsteadofdisplaying
site.
payingadvertisers’placements. Alloftheadshaveembed-
SincethethirdpartypagesthatarerenderedviaPPVnet-
dedlinkstodummylandingpagesthatwealsomonitor.
works are clearly not the interest of the users, all of the re-
DomainnameswereregisteredforeachsitewithGoDaddy
sulting impressions are invalid. Beyond laking the intent
usingtheiranonymousregistrationoption. Weattemptedto
necessarytoqualifyasvalidtraffic, weshowthatPPVnet-
give the sites names that sounded interesting and connoted
worktraffichascharacteristicsunlikeorganictraffic.Forex-
the news-related content of the sites. The sites were cre-
ample,naturaltrafficdisplaysadiurnaltrafficpattern,while
atedusingdotCMSinsideAmazonEC2. Amazon’sCloud-
the PPV traffic we observed often showed highly artificial
FrontCDNwasenabledforthesitesinordertohandlelarger
deliverypatterns.
burstsoftraffic.Weuseda"noindex,nofollow"metatagand
arobots.txtfiletoattempttopreventinclusioninsearchen-
3. DATACOLLECTIONONHONEYPOT
gineresults.
WEBSITES
Instrumentation was facilitated in several ways. Google
To begin our investigation of traffic generation and im- Analyticstagsweredeployedonallpagesforgeneralmon-
pression fraud we established a set of honeypot websites. itoring. Logs from the serving infrastructure were used to
We then purchased traffic from a number of different ser- understandthedetailsofindividualconnections. Aseriesof
vices and captured a diverse set of data from the resulting JavaScript blocks collected information about the site vis-
hits on our sites. In this section we provide details on our itors. The instrumentation reported viewer characteristics
honeypotwebsitesandtrafficpurchases.Theresultsofthese (See Table 1) using 1x1 pixels. Each advertisement on the
activitiesaredescribedindetailinSection4. siteswasinstrumentedwithcodethatreportedthethreekey
events in the life cycle of every ad: (1) JavaScript load (2)
3.1 HoneypotWebsites JavaScript execution and (3) successful delivery. Finally,
Wecreatedthreewebsitesasthestartingpointforourin- the pages contained JavaScript that tracked user interaction
vestigationoftrafficgenerationserviceproviders. Thesites on the site. Simliar to [41] the interaction metrics reported
differed only in styling, formatting, and deployment. The mousemovementsandclicks. Themousepositionwascol-
content on each site was identical. The reason for creating lectedeverytimethecursormovedatleast20pixels.
three different sites was to enable us to conduct A-B com-
3.2 PurchasedTraffic
parisonsbetweendifferenttrafficgenerationservices.
Thedesignobjectiveforourhoneypotswastocreatesites We identified and reviewed 34 traffic generation service
thatlookedrelatively"legitimate". Tothatend,theyhavea providersforthisstudy. Theseserviceproviderswereiden-
standard layout, content changes regularly and the deploy- tifiedusingwebsearch. Wemanuallyreviewedeachservice
mentisstandard. Asecondobjectivewasthatthesiteswere provider’ssitetocatalogavailablepurchasingoptions. De-
instrumentedtogatherasmuchdataaspossibleonarriving tailsofthesitesandoptionsaregiveninTable3. Wemake
traffic. no claims on the completeness of this list of traffic genera-
Each site consisted of a base landing page and four sub- tionserviceproviders. However, giventhecommonalityof
pages. Three of the pages displayed RSS content from the theirofferings,webelievethattheyarearepresentativecross
news feeds of topwirenews.com or espn.com. One page section.
listedlinkstopopularnewssites. Thefinalpagewasanon- Wealsoinvestigatedtheserviceproviderwebsitesthem-
functional search result. Every page contained four adver- selves to gain some insights on their legitimacy. Their do-
4
main names were checked with McAfee SiteAdvisor [6]. at$1.80)andthat"...popunderadswillnotblockanyofyour
The DNS record was inspected using Network Solutions’ sitecontentanddonotleadtoactionswhereusersmightbe
Whoistool[8].Finally,atoolavailablefromSameID.net[9] ledtoleaveyoursite."[23].Inthiscase,pop-underwindows
was used to search for sites sharing the same IP address or arethemethodthatInfinityAdsusestogeneratetraffic. We
GoogleAnalyticstag. describetheseinmoredetailbelow.
4. PAY-PER-VIEWNETWORK
Table 1: Visitor information collected from honeypot
CHARACTERISTICS
websites.
Inthissectionwereporttheresultsofouranalysisofpur-
Timestamp ClientIP
chasedtrafficatourhoneypotsites. Thisanalysisrevealsthe
URL UserAgent
mechanismsusedtodrivetraffictotargetsitesandopensthe
UserUID PageLoadUID
door to a broader analysis of PPV networks, which is also
ViewportDimensions Referer
reportedbelow.
Fromthesetof34trafficgenerationservices,weselected 4.1 TrafficGenerationOfferings
5fromwhichwemadepurchases. Serviceswereselectedto
We reviewed the details of the 34 traffic genera-
get a diversity of delivery rates and price points. The char-
tion/ecommercesitesthatweidentifiedviawebsearchusing
acteristicsofourpurchasedtrafficindicatedtheselectedser-
stringslike"websitetraffic","buyingwebtraffic","webtraf-
viceswereindependentnetworks. Thepurchasedtrafficwas
ficking",etc. Featuressuchastrafficcharacteristics,pricing,
directedtothehoneypotsitesbetweenNovember11th,2012
timing,resellerinformation,andDNSentrieswerenotedfor
andFebruary18th,2013,resultinginover69Kdeliveredim-
eachsite. DetailsarelistedinTable3.
pressions.WeusedtargetURL’sincludingGoogleAnalytics
campaignparameters[5]tohelptodifferentiateoverlapping 4.1.1 Pricing
purchases.
Thereisnouniformpricingfortrafficproviders.Thepric-
Ourpurchasingstrategywasorientedarounddiversityand
inggiveninTable3wasnormalizedtothecostofdelivering
notvolume. Detailsofthepurchasedtrafficcanbefoundin
25,000 visitors from the United States for comparison. Of
Table 2. With the exception of BuildTraffic all traffic pur-
the 34 traffic generation services that we investigated, five
chasedwasdesignatedasonlytrafficfromUnitedStatesand
of them did not allow purchasing traffic originating exclu-
labeledasnewsandinformation. Theintendeddeliveryrate
sively from the United States. One site was deemed fraud-
of purchased traffic varied between 333 visitors per day to
ulentbecauseitdidnothaveaspacetoenteratrafficdesti-
25,000visitorsperday. Weintendtoinvestigatefurtherdi-
nationpriortocheckoutcompletion. Theremaining28sites
versityandhighervolumepurchasesinfuturework.
chargedbetween$29.99and$200topurchase25kvisitors.
4.1.2 Overlap/Reselling
Table2: Trafficpurchasesmadeforthisstudy.
There were significant similarities between many of the
Vendor Amount Runtime Price
traffic purchase sites. Many of the providers made mul-
MaxVisits 10,000 5days $11.99 tiple copies of their site in order to target different pub-
BuildTraffic 20,000 60days $55.00 lisher segments or to simply use another attractive domain
AeTraffic 10,000 7days $39.95 name. Alloftheproviderdomainswereassessedusingthe
BuyBulkVisitor 20,000 5days $53.00 sameid.net domain investigation tool [9]. Seven of the
TrafficMasters 50,000 2days $70.00 providers appeared to be repackaging another site (handy-
traffic, cmkmarketing, visitorboost, revisitors, buybulkvis-
itor, highurlstats, xrealvisitors). Four of the repackaged
3.3 Pay-Per-ViewPublisherSignup
sitessharedaGoogleAnalyticsaccountwithanothertraffic
In addition to traffic generation itself, PPV service provider site (handytraffic, cmkmarketing, visitorboost, re-
providersalsoofferpublisherstheopportunitytoparticipate visitors). ThreeoftherepackagedsitessharedanIPaddress
asatrafficsourceintheirnetwork(thiswasourinitialindi- with another traffic purchase site (buybulkvisitor, highurl-
cationofPPVnetworks). Tofurtherinvestigatethemecha- stats, xrealvisitors). Shared website hosting could cause IP
nisms of traffic generation, we enrolled as a website owner overlap, but it is unlikely that 3 sites in our 34 site sample
willingtodisplaycontentwithaPPVserviceprovidercalled arerandomlyhostedonthesameIP.Furthermoreanimple-
InfinityAds. The signup was completed using InfinityAds’ mentationerrorcausedhighurlstats.comtoloadbuybulkvis-
fully automated publisher signup system on their website. itor.com,makingitplausiblethatthesesitesarerelated.
UponsignupweweregivenablockofJavaScripttoloadon FourofthePPVsellersinvestigatedofferedtheabilityto
oursite. Inreturnforrunningthistag,thewebsiteowneris become a traffic reseller (hitpro, ineedhits, toptrafficwhole-
assuredofarelativelyattractiveCPM(quotedandqualified saler,traffic-masters). Aresellersellstrafficwithouthaving
5
Table3: Trafficproviderdetails.
Site Price2 Geotargeting Category Pacing Adult AllowPop-up/Sound
aetraffic.com $75 Yes Yes Option Option Yes2
allseostar.com NA No No No Opion2 No
bringvisitor.com NA No No No ? Yes2
buildtraffic.com $119 Yes Yes 30days ? No
buybulkvisitor.com $53 Yes Yes Option ? No
buyhitscheap.com $110 Yes No No ? Yes
cheapadvertising.biz NA No No No Option ?
cmkmarketing.com $82 Yes Yes No ? No
cybertrafficstore.com $70 Yes Yes 30days Option ?
easytraffic.biz $100 Yes Yes 60days ? No
fulltraffic.net $220 Yes No No ? ?
getwebsitetraffic.org $75 Yes Yes Option Option Yes2
growstats.com $84 Yes Yes Option ? Yes2
handytraffic.com $99 Yes Yes Option2 ? Yes
highurlstats.com $200 Yes Yes 30days ? ?
hitpro.us $60 Yes Yes 30days ? No
ineedhits.com $120 Yes Yes 30days ? No
masvisitas.net Noinformation,nowheretoenterwebsiteURL
maxvisits.com $30 Yes Yes Option ? Yes
meantraffic.com $30 Yes Yes No Option2 ?
perfecttraffic.com $43 Yes Yes Option ? ?
plusvisites.com $30 Yes Yes Option ? ?
purchasewebtraffic.net $99 No No No ? No
realtrafficsource.com $55 Yes Yes No ? ?
revisitors.com $119 Yes Yes Option2 Option2 Yes2
source4traffic.com $88 Yes Yes 30days ? No
thewebtrafficdominator.com $32 Yes Yes No Option2 ?
toptrafficwholesaler.com $111 Yes Yes2 30days Option2 No
traffic-masters.com $35 Yes Yes Option Option2 No
trafficchamp.com $89 Yes Yes 30days No No
trafficelf.com $55 Yes Yes Option Option2 Yes
trafixtech.com $35 Yes Yes Option Option2 No
visitorboost.com $116 Yes Yes 30days ? No
xrealvisitors.com NA No No No ? ?
1Costtopurchase25,000UnitedStatesvisitors(normalizedwhereneeded)
2Extracost
tomanagetrafficdeliveryinfrastructureorpaymentprocess- the34sitesinvestigated22werelabeledasSafe,11hadnot
ing. The reseller acts only as an intermediary forwarding yetbeenreviewedbySiteAdvisor,and1waslabeledassus-
ordersalongtothetruetrafficprovider. Asperthedescrip- picious.
tions, the reseller is charged a fixed rate for the traffic and
4.1.4 DNSRegistration
can resell the traffic at the price of their choosing. Two of
the reseller packages offered prepackaged websites where A Whois lookup was performed on each of the traffic
thereselleronlyneedstosupplytheirbrandingandmarket- providerswebsitestogaininsightsondeployments. 14out
ing. of the 34 sites listed a DNS anonymization service as their
primarycontact.Fourofthesiteswereregisteredorrenewed
4.1.3 ProviderSiteAnalysis
in the previous 12 months. Expiration and creation dates
Given the potentially fraudulent nature of traffic genera- givetheperiodthedomainregistration. Onaveragethesites
tion,wewereinterestedinageneralmeasureofthetrustwor- wereregisteredfor5.71years. Thelongestregistrationwas
thiness of providers sites. McAfee’s SiteAdvisor [6] rated for16years. Sixsitesareregisteredforonly1year.
most of the provider websites as safe. Specifically, out of Looking at the contract information of the sites not us-
6
inganonymizationgavethefollowingbreakdownofcountry addressesfromthesesourcesaveraged303,968(or0.007%
residency: 10UnitedStates,2Australia,2Canada,2Spain, oftheentireIPspace)forJanuary2013. Onaverage,source
1France,1Italy,1Singapore,1China. IP addresses of the purchased data matched the blacklists
0.97%ofthetime. Thisisperhapsmorethanwouldbeex-
4.1.5 Features
pected by chance, but too low to draw a strong conclusion
Providers offer a variety of options for purchased traffic. aboutoverlapbetweenthesetofsourcesfromtrafficgener-
Manyprovideassurancesthatonly"real"trafficwillbede- ationservicesandmalicioussources.
livered and no "black hat techniques" are used. Every site
promises unique views, such that the same user will not be 4.2.2 Interaction
directedtothesitemultipletimesin24hours. Sixsiteswere
EachofourhoneypotpagestrackedfourJavaScriptevents:
moreprecise,specifyingthatauser’sIPaddresswillonlybe
onmousemove, onmousedown, onblur, onfocus. There was
directed to the destination once in a 24-hour period. Typi-
anextremelysmallnumberofactivityevents(190)reported
cal traffic volumes range between 10K and 1M visitors per
for all purchased traffic. There are a few explanations for
campaign. Direct email was required for campaigns larger
such low interation: (i) it may be an accurate reflection of
than 1M visitors. See Table 4 for other options offered by
reality,(ii)thesitewas0sizedandtheusercouldnotinteract
thetrafficprovidersthatweevaluated.
withit(see4.2.7)or(iii)itcouldbetheresultofJavaScript
eventsnotfiringasexpected. Unfortunatelywecannotrule
Table4: Trafficproviderfeatures. out JavaScript failure. We cannot draw strong conclusions
from the lack of interaction events other than the fact that
AdsenseSafe SafetousewithGoogleAdSense
wedidnotpayforanythingotherthanimpressions.
AdultTraffic Deliverusersinterestedinporn
AlexaBoost TraffictoincreaseAlexaranking 4.2.3 TemporalDistribution
AllowPop-ups/Sound Norestrictionsondestination
The pacing of visitor delivery varied greatly depending
CampaignPacing Selectlengthofcampaign
on traffic service provider. As is described below service
Geo-targeting Deliverusersfromaregion
providers traffic millions if not billions of visitors a day,
Clicks Deliverclicksontargetwebsite
but individual purchases can require delivery of less than
MobileTraffic Deliverusersofmobiledevices
100 visitors a day to a destination. Furthermore, the net-
TrafficClasses Deliveruserswithspecificinterest
work throughput is not guaranteed. So the deliveries need
tobeslightlyfront-loadedtoensurefulldeliveryinthecase
4.2 PurchasedTrafficCharacteristics of lower than expected throughput. The problem of pacing
manifesteditselfinboththetimeofarrivalswithinadayand
Oneofourpurchasesdidnotdeliveranyappreciablevol-
thearrivaldistributionovertheentirecampaign.
umeoftraffic. Thereasonforthefailureoftrafficdeliveryis
Thedailyarrivalpatternsofvisitorsshowedsomeunusual
notclear. Theprovidermayhavedecidednottodeliverdue
artifacts. AeTraffic delivered consistently though the entire
to the instrumentation of the destination site. The provider
dayascanbeseeninFigure 4. Itiswellknownthattypical
still collected payment for the traffic which was not deliv-
user traffic follows a diurnal cycle, reaching the high peak
ered. See Tables 5 and 6 for a summary of our measure-
duringthedayandlowpeakovernightwhenusersaresleep-
ments. Ofthetargetof110,000visitsthatwepurchased,we
ing. AmoreobviousexampleofartificialdeliveryisBuild-
received 69,567. At the time of writing AeTraffic was still
Traffic, which delivered only during the first 10 minutes of
deliveringvisitorsbeyondthecampaignend.TheBuildTraf-
thehour,ascanbeseeninFigure 5.
ficpurchasestoppeddeliveringvisitorsabruptlyattheendof
The arrival of users throughout the campaign was quite
January,28daysintothe60-daycampaign.
burstyinsomecases.Withperiodsofhighdeliveryfollowed
Weanalyzedtrafficdeliveredtoourhoneypotwebsitesfor
byperiodsoflowdelivery. MaxVisistsdeliveredtrafficpri-
avarietyofcharacteristics. Beforeprocessing,thedatawas
marilyinthefirsthalfofeverydayascanbeseeninFigure
filteredtoremoveanyeventsoriginatingfromourhoneypot
6. Meanwhile, TrafficMasters delivery primarily consisted
server’s IP address. Also any user agent containing case-
of two large spikes with little delivery between, as can be
insensitive’bot’wasexcluded. Thiswasdonetoremovethe
seeninFigure 7.
effects of web crawler traffic from our results. All of the
traffic observed appeared to originate from our purchases.
4.2.4 IncompleteLoads
Wedidnotseeanyindicationsofnaturaltraffic.
EverypageonourhoneypotsitescontainedfourJavaScript
4.2.1 BlacklistComparison
blocks which loaded advertising creatives. Each creative
The IP addresses of the purchased traffic showed some wasindependentlyinstrumentedtoreportwhenithadbeen
overlapwithpublicIPblacklists. Everymorningat7GMT loaded. FourblocksofJavaScriptneedtocompleteinorder
IPblocklistswerepulledfromDShield.org[3]andUcePro- to successfully load all of the ads on the pages. Using this
tect[10]aspointsofcomparison.ThecountofblacklistedIP information, we can calculate the percentage of page loads
7
Table5: Purchasedtrafficdelivery.
Vendor ExpectedVisitors DeliveredVisitors ExpectedDuration ActualDuration %Loadingall4Ads
AeTraffic 10,000 17,205 7days 8days1 16.40
BuildTraffic 20,000 1,086 60days 29days 60.75
BuyBulkVisitor 20,000 1 5days 1day Unknown2
MaxVisits 10,000 9,635 5days 5days 12.80
TrafficMasters 50,000 41,640 2days 3days 58.34
1Stillsendingtrafficatthetimeofsubmission
2UserfailedtoloadJavaScript
140 400
350
utes 120 Hour 300
s per 5 Min 1 8000 ssions per 122505000
n e
sio 60 mpr 100
es I 50
pr
m 40 0
I 1 1 1 1 1 1 1 1
1- 1- 1- 1- 1- 1- 1- 1-
1 2 2 2 2 2 2 2
20 9 0 1 2 3 4 5 6
0 0 0 0 0 0 0 0
00:002:004:006:008:010:012:014:016:018:020:022:000:0 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00
0 0 0 0 0 0 0 0 0 0 0 0 0
Time(UTC) Time(UTC)
Figure4: TrafficdistributionfromAeTraffic. Figure6: TrafficdistributionfromMaxVisits.
60 12000
utes 50 Hour 10000
s per 5 Min 3400 ssions per 468000000000
n e
ssio 20 mpr 2000
e I
pr
m 10 0
I 0 0 0 0 0 0 0 0 0 0 0 0
2-2-2-2-2-2-2-2-2-2-2-2-
1 1 1 1 1 2 2 2 2 2 2 2
0 8 9 9 9 9 0 0 0 0 1 1 1
1 0 0 1 1 0 0 1 1 0 0 1
00:002:004:006:008:010:012:014:016:018:020:022:000:0 8:000:006:002:008:000:006:002:008:000:006:002:00
0 0 0 0 0 0 0 0 0 0 0 0 0
Time(UTC) Time(UTC)
Figure5: TrafficdistributionfromBuildTraffic. Figure7: TrafficDistributionfromTrafficMasters.
thatcompletedforallfourads. lapofIPaddressforthecampaigns:AeTrafficreused0.75%
Traffic from BuildTraffic and TrafficMasters resulted in of IP addresses, BuildTraffic reused 0.64% and MaxVisits
adscompletelyloadingapproximately60%ofthetime. Ae- reused 11.25%. The larger purchase from TrafficMasters
Traffic and MaxVisits only loaded approximately 15% of showed significantly more IP address overlap with 65% of
the time. Reasons for failure to load all the ads include: IPsreused. ThemajorityoftheIPsgeolocatedinsideofthe
JavaScript blockers, JavaScript errors, JavaScript execution US,withtheexceptionoftheBuildTrafficIPs.
timeout,andnavigationawayfromthepage.
4.2.6 UserAgents
4.2.5 IPAddressDistribution
The number of unique user agents across the purchases
IP addresses from an entire traffic generation campaign shows the traffic came from a diverse set of browsers. An
where checked for duplicates to get an idea of the distri- alternative explanation could be that artificial traffic gener-
bution of traffic sources. According to the advertised 24- ators utilized a large set of User Agent strings. However,
hour-unique policy an IP address could be used once per combinedwiththediversesetofIPaddresses,itappearsthe
day. For small purchases our data showed very little over- trafficcouldwellbegeneratedfromgenuineviewers.
8
Table6: Purchasedtrafficcharacteristics.
Vendor IPSources %USIPs %Blacklisted UniqueUserAgents %MobileUserAgents %ZeroSized
AeTraffic 17,075 93.14 .99 3,331 5.44 34.08
BuildTraffic 1,079 .17 1.75 312 14.42 NA1
BuyBulkVisitor 1 100 0 Unknown2 0 Unknown2
MaxVisits 8,551 98.83 .47 1,883 4.03 NA1
TrafficMasters 14,489 99.29 1.65 3,096 4.59 47.34
1Detailedtrackingnotimplementedattimeofpurchase
2UserfailedtoloadJavaScript
About 5% of the traffic from AeTraffic, MaxVisists and licitedwindows. PPVnetworksneedtocircumventthisre-
TrafficMastershadtheUserAgentsignatureofamobilede- striction. One option is the PPV code can explicitly by-
vice. BuildTraffic traffic contained a much higher percent- passbrowserprotections. Areviewoftheissuetrackersfor
age of mobile device User Agents. Possibly due to the in- Chrome or Firefox does not list many bugs related to the
creasedgeographicdiversityofthetraffic. browsers’pop-upblockers,thusthisislikelytobeadifficult
codingchallenge. OurempiricaldatadidnotshowanyPPV
4.2.7 ViewportSize
network tags that attempt to bypass the pop-up blocker di-
Halfwaythroughourpurchasesweinstrumentedthecode rectly. Thecommonapproachistotiepop-undercreationto
torecordtheelementheightandwidth.3 Overall46.51%of auseractionsincebrowserstypicallyallowcreationofnew
adviewshadaheightorwidthof0,meaningthattheadver- windowsontheseevents. Typicallythepop-underactionis
tisementcouldnotpossiblybeviewedbytheuser. 13.42% attached to the onclick event of the body of the page. This
ofviewshadbothaheightandwidthofzero. Theseresults causesthepop-underactiontofirewhenevertheuserclicks
corroboratetheBuildTrafficdeliverytechniqueofzero-sized anywhereonthesite.
framesdescribedin4.3.1. After creation, the pop-under window is directed to load
a specific URL pointing to the network’s ad server. The
4.3 Pay-Per-ViewNetworks
adserver URLcontainsanumber ofparametersdescribing
By examining the JavaScript provided by traffic genera- targeting and attribution of the visitor. The parameters al-
tion services and the referer fields from traffic on our hon- waysincludeanidentifierfortheoriginatingsitesothatthe
eypotsites,wewereabletoidentifythefactthattrafficwas publisher can get paid for the traffic. The list of parame-
generatedprimarilyfrompop-underwindows. Interestingly, tersisclearlydependentonindividualimplementations,but
while we did see evidence of traffic from expired domains, someofthemorecommontargetingparametersare:(i)user-
wesawnoevidenceoftrafficfrombotnets.Thisobservation Token, (ii) indication if adult sites are allowed, (iii) user
ledtoourdeeperinvestigationoftheuseofpop-undersfor IP/geolocation,and(iv)viewportsize. Usingtheseparame-
trafficgenerationandourcharacterizationofPPVnetworks. terstheadserverselectsandreturnsthemostprofitable3rd-
As noted above when publishers participate in a traffic partywebsites(i.e.,thepublishersthathavepurchasedtraf-
generationservicei.e.,aPPVnetwork,theyaregivenablock fic) available. This is presumably the point where the 24
of JavaScript to place on their site, which looks very much houruniqueuserguaranteeisenforced.
likeastandardadtag. InthecaseofPPVnetworks,whena Manually loading a publisher’s PPV network tag often
useraccessesaPPVnetworkpublisherpage,theJavaScript showed multiple redirections through a network of PPV
opens a new window (typically behind the active browser servers. This mimics what is seen in standard advertis-
window,henceapop-under)andloadsthePPVserverURL. ing networks where an individual ad can be redirected
Thepublisherrunningthetaggetsashareoftherevenuefor across many networks in order to optimize the return from
everyPPVURLthatissubsequentlyloaded. ThePPVnet- each user. For example, repeatedly loading the InfinityAds
worksolvestwoproblemswithrespecttomarshalingusers: publisher tag showed network connections being made to
(i) it delivers the JavaScript which creates the pop-under ads.lzjl.com,
windowand(ii)itdeterminesthesitetodisplayinthewin- cpxcenter.com, and 199.21.148.39. Whois and reverse IP
dow. lookups on these all indicate YesUp eCommerce Solutions
Inresponsetoprevalentpop-upadvertising,webbrowsers Inc.forthecontactinformation. YesUpislocatedinOntario
give users the option to prevent pages from opening unso- CanadaandhasahostofeCommerceofferings.
Ideally we would have identified the referer to the main
3Using document.documentElement.clientHeight, docu-
pop-under page in our purchased traffic. This would en-
ment.body.clientHeight, window.innerHeight depending on
browsertype. able us to identify the sites hosting pop-under tags. Un-
9
fortunately,thesandboxingofchildframes(especiallychild dertomaketheirnetworkappearlargerthantheircompeti-
frames with different domains than the parent) protects the tors. Nonetheless,theselfreportednumbersgiveaninsight
DocumentObjectModeloftheparentframe. Therefore,the into at least the approximate size of the networks. None
document.referrer node of the parent is inaccessible to the of the providers publish throughput numbers broken down
child frame. None of our traffic purchases had a value for byfeaturesordeliverymechanisms. Thus, thenumbersin-
parent.document.referrer. Thebestwecandoisthereferer cludepop-unders,expireddomainsandanyothergeneration
value of the frame loading our honeypot sites. This referer techniques. As shown in Table 4, 8 of the providers offer
pointstotheoriginofthepop-underwindowcode(originat- throughputinformation. Anaverageof17.16Muniquevisi-
ingfromthePPVserviceprovider). torsand6.29Bpageviewsperproviderperdayareclaimed.
While the self report by TrafficMasters on page views is
4.3.1 DeliveryAnalysis much higher than others and could be false, it may be due
In order to gain a better understanding of how traffic is to an extensive affiliate network. Indeed, the use of affili-
deliveredtopurchasingsites,wereviewedthepageslistedin atenetworksmeansthatsimplesummationofthroughputto
therefererfieldsforthetrafficarrivingatourhoneypotsites. assess scope is unlikely to be accurate. However, the self
Acloserexaminationoftwoofthereferersites(BuildTraffic reported numbers still point to a sizable capacity for PPV
and RealTrafficSource) showed methods for increasing the networks.
numberof"pageviews"foreveryuserdelivered.
Loading the referer of traffic purchased from BuildTraf- Table 7: Self reported network throughput from PPV
fic resulted in a simple HTML page, including 11 frames providers.
(see Appendix for example code). The main frame loads
theprimarytargetdestinationin100%ofthebrowserview- Site DailyVisitors DailyDeliveries
port. Followingtheprimaryframethereare10frameswith CMKMarketing 2M 25M
aheightof0pixels.EachoftheseframesloadstheURLofa HitPro 40.5M 112M
PPVnetworkcustomer. Eightoftheframesloadpathsfrom TrafficElf 20M 45M
alinkshorteningservice(itsssl.com),whichresolvedirectly BuildTraffic 3.3M -
to a number of sites (presumably those purchasing traffic). FullTraffic 20M -
Oneoftheframesloadsanothertargetinglinkfromthesame TopTrafficWholesaler - 30M
network. Thefinalframeloadsatargetinglinkfromyetan- BringVisitor - 26.6M
other domain. Resulting in a total of 11 "page loads" each TrafficMasters - 37.5B
time the PPV network URL is loaded. Ten of those page
loadsareinvisibletotheenduserbecausetheyareloadedin
aframe0pixelshigh. 4.4.2 VolumeEstimation
The page listed as the referer in traffic from RealTraffic-
Inordertoestimatethroughputofthenetworksweinves-
Sourcealsousedaframetoloadthefinaldestination. Inthis
tigatedthescopeofthedeploymentofthePPVnetworktags
caseonlyasingleframecoveredtheentireviewport,butthe
across publisher sites. Given the publisher sites where the
outerpagereloadeditselfevery15seconds. Whenthepage
PPVnetworktagsarepresentalongwiththeestimatedtraf-
isdisplayedasapop-underitwillcontinuetoloadadiffer-
fic for those sites we create a conservative estimate for the
ent site every 15 seconds even if the pop-under window is
dailytrafficacrossPPVnetworks.
notvisibletotheuser.
ThefirststepindeterminingwherethePPVnetworktags
aredeployedisidentifyingthetagURLs.ThePPVnetworks
4.4 PPVNetworkThroughput
weconsideredcommonlyusedadomainnamefortheirde-
Based on our evaluation of the pop-under mechanisms liveryinfrastructurethatwasdifferentfromthepublicfacing
usedbyPPVnetworks,weendeavoredtoassessthebroader websitesthatmarkettopublishers.Weusedthreetechniques
issues of the scale of these networks (e.g., number of pub- toidentify10activePPVnetworktagURLs: (i)subscribing
lishersitesandnumberofusers)andthepotentialvolumeof to a PPV network as a publisher, (ii) investigating referer
impressionsthatarebeingdeliveredonadailybasis. While fieldsand(iii)searchingforadcodeonpublicforums.
allofthisanalysisisapproximateandisbasedoncertainas- Where possible we utilized automated signup processes
sumptions, we take a conservative approach and argue that to harvest PPV tags directly from the publishers. This is a
ourresultsprovideameaningfuldepictionofthisthreat. trivialcasewherethecodetoberunonthepublishersiteis
directlysupplied.
4.4.1 SelfReportedNetworkData
Using referer fields to identify PPV tags was more chal-
Many of the PPV providers list the throughput details lenging. Typically the destination is loaded inside a frame,
(unique users and page views) of their network in advertis- so the referer references the outer page hosting the frame.
ing materials. Clearly, when self reporting these numbers, The display page is typically not loaded directly from the
PPV network providers have incentive to over state in or- publishersite.ThepublisherloadsJavaScriptwhichhandles
10