State of digital ad fraud 2017 by augustine fou

State of Digital Ad FraudJanuary 1, 2017 Update

January 2017Augustine Fou, [email protected] 212. 203 .7239

mailto:[email protected]

Ad Fraud is VeryLucrative and Scalable

January 2017 / Page 3marketing.scienceconsulting group, inc.

linkedin.com/in/augustinefou

How profitable is ad fraud? EXTREMELY

Source: https://hbr.org/2015/10/why-fraudulent-ad-networks-continue-to-thrive

“the profit margin is 99% … [especially with pay-for-use cloud services ]…”

Source: Digital Citizens Alliance Study, Feb 2014

“highly lucrative, and profitable… with margins from 80% to as high as 94%…”

https://hbr.org/2015/10/why-fraudulent-ad-networks-continue-to-thrive

https://hbr.org/2015/10/why-fraudulent-ad-networks-continue-to-thrive

https://media.gractions.com/314A5A5A9ABBBBC5E3BD824CF47C46EF4B9D3A76/4af7db7f-03e7-49cb-aeb8-ad0671a4e1c7.pdf



How scalable are fraud operations? MASSIVELYCash out sites are massively scalable

131 ads on pageX

100 iframes=

13,100 ads /page

One visit redirected dozens of timesKnown blackhat technique to hide real referrer and replace with faked referrer.

Example how-to:http://www.blackhatworld.com/blackhat-seo/cloaking-content-generators/36830-cloaking-redirect-referer.html

Thousands of requests per pageSingle mobile app calling 10k impressions

Source: Forensiq



Example – AppNexus cleaned up 92% of impressions

Increased CPM prices by 800%

Decreased impression volume by 92%

Source: http://adexchanger.com/ad-exchange-news/6-months-after-fraud-cleanup-appnexus-shares-effect-on-its-exchange/

260 billion

20 billion

> $1.60

< 20 cents

“pity those advertisers who bought before the cleanup”

Ad Fraud Harms The Digital Ad Ecosystem



Ad fraud/ad spend are hitting all-time highs

Digital ad FRAUD

Digital ad SPENDSource: IAB 2016 F1H Report

$ billions



Ad fraud is now the largest form of crime

$20 billion

CounterfeitGoods U.S.

$18 billion

Somalipirates

$70B 2016E Digital Ad Spending

Bank robberies

$38 million

$31 billionU.S. alone

$1 billion

ATM Malware

Payment Card Fraud 2015

$22 billion

Source: Nilson Report Dec 2016

Source: ICC, U.S. DHS, et.

al

Source: World Bank Study 2013

Source: Kaspersky 2015

$7 in $100$3 in $100

“this is a PER YEAR number”

Digital Ad Fraud

Source: IAB H1 2016

$44 in $100

https://www.nilsonreport.com/

https://www.nilsonreport.com/

https://en.wikipedia.org/wiki/Counterfeit_consumer_goods

http://www.cnn.com/2013/04/12/business/piracy-economy-world-bank/

http://www.cnn.com/2013/04/12/business/piracy-economy-world-bank/

http://www.securityweek.com/hackers-hit-100-banks-unprecedented-1-billion-cyber-attack-kaspersky-lab



CPM/CPC buckets (91% of spend) is most targeted

Impressions(CPM/CPV)

Clicks(CPC)

Search27%

91% digital spend

Display10%

Video7%

Mobile47%

Leads(CPL)

Sales(CPA)

Lead Gen$2.0B

Other$5.0B

• classifieds• sponsorship• rich media

(89% in 2015)Source: IAB 1H 2016 Report

(86% in 2014)



Two key ingredients of CPM and CPC FraudImpression (CPM) Fraud

(includes mobile display, video ads)

1. Put up fake websites and load tons of display ads on the pages

Search Click (CPC) Fraud

(includes mobile search ads)

2. Use fake users (bots) to repeatedly load pages to generate fake ad impressions

1. Put up fake websites and participate in search networks

2. Use fake users (bots) to type keywords and click on them to generate the CPC revenue

screen shots of fake sites

Fake Websites(cash-out sites)



Websites – spectrum from bad to good

Ad Fraud Sites

Click Fraud Sites

100% bot

mostly human

Piracy Sites

Premium Publishers

Sites w/ Sourced Traffic

“fraud sites” “sites w/ questionable practices” “good guys”

“real content that real humans want to read”



Identical sites – fraud sites made by template

100% bot



Countless fraud domains used to commit ad fraudhttp://analyzecanceradvice.comhttp://analyzecancerhelp.comhttp://bestcanceropinion.comhttp://bestcancerproducts.comhttp://bestcancerresults.comhttp://besthealthopinion.comhttp://bettercanceradvice.comhttp://bettercancerhelp.comhttp://betterhealthopinion.comhttp://findcanceropinion.comhttp://findcancerresource.comhttp://findcancertopics.comhttp://findhealthopinion.comhttp://finestcanceradvice.comhttp://finestcancerhelp.comhttp://finestcancerresults.comhttp://getcancerproducts.com

100M+ more sites like these, designed to profit from high value display, video, and mobile ads

Fake Visitors(bots)



Bots are automated browsers used for ad fraud

Headless BrowsersSeleniumPhantomJSZombie.jsSlimerJS

Mobile Simulators35 listed

Bots are made from malware compromised PCs or headless browsers (no screen) in datacenters.

Bots

http://www.mobilexweb.com/emulators



Bots range in sophistication, and therefore cost

Javascript installed on webpage

Malware on PCsData Center BotsOn-Page BotsHeadless browsers

in data centersMalware installed on

humans’ devices

Less sophisticated Most sophisticated

Source: AdAge/Augustine Fou, Mar 2014 Source: Forensiq Source: Augustine Fou, Oct 2015

“not many people know that the official industry lists of bots catch NONE of these bots, not one.”

1 cent CPMsLoad pages, click

10 cent CPMsFake scroll, mouse movement, click

1 dollar CPMsReplay human-like mouse movements, clone cookies

http://adage.com/article/digital/bots-scam-advertisers-pretending-human/293428/

http://adage.com/article/digital/bots-scam-advertisers-pretending-human/293428/

http://www.slideshare.net/augustinefou/digital-ad-fraud-ecosystem



Any device with chip/connectivity can be used as a botTraffic cameras used as botnet (Engadget, Oct 2015)

mobile devices

connected traffic lights

connected cars

thermostat connected fridge

Security cams used as DDoS botnet (Engadget, Jun 2016)

(TechTimes, Sep 2016)

Bot/Fraud Detection



Three main types of bot / fraud detectionIn-Ad

(ad iframes)On-Site

(publishers’ sites)

• Used by advertisers to measure ad impressions

• Limitations – tag is in foreign iframe, severe limits on detection

ad tag / pixel(in-ad measurement)

javascript embed(on-site measurement)

In-Network (ad exchange)

• Used by publishers to measure visitors to pages

• Limitations – most detailed and complete analysis of visitors

• Used by exchanges to screen bid requests

• Limitations – relies on blacklists or probabilistic algorithms, least info

ad served

bot

human

fraud site

good site



Fraud bots are NOT on any list

10,000bots observed

in the wild

user-agents.org

bad guys’ bots3%Dstillery

“findings from two independent third parties, Integral Ad Science and White Ops”

3.7%Rocket Fuel

“Forensiq results confirmed that ... only 3.72% of impressions categorized as high risk.”

2 - 3%comScore

“most campaigns have far less; more in the 2% to 3% range.”

bot list-matching

“not on any list”disguised as popular browsers – Internet Explorer; constantly

adapting to avoid detection



How Fraud HarmsGood Publishers



Significant ad revenue stolen from publishers

1. Bots collect “cookie” 2. Bots cause ad impressions on fake sites.

www.nejm.org healthsiteproductionalways.com

FOR EXAMPLE ONLY



http://www.olay.com/skin-care-products/OlayPro-X?utm_source=msn&utm_medium=cpc&utm_campaign=Olay_Search_Desktop

Bad guys pretend to be good publishers’ sites

Click thru URL passes fake source “utm_source=msn”

buy eye cream online(expensive CPC keyword)

1. Fake site that carries search ads

Olay.com ad in #1 position

2. search ad served, fake click

Destination pagefake source declared

3. Click through to destination page

http://www.olay.com/skin-care-products/OlayPro-X?utm_source=msn&utm_medium=cpc&utm_campaign=Olay_Search_Desktop_Category+Interest+Product.Phrase&utm_term=eye%20cream&utm_content=TZsrSzFz_eye%20cream_p_2990456911









Bad measurements wrongly accuse publishers

Publisher clearly does not have 90% bots and never had

“you have low viewability”

“you have 90% bots”• We want a refund• We won’t pay• We want make-goods



Best Practices of Good Publishers

1. Reduce/eliminate shortcuts – mainstream publisher never sources traffic, never uses audience extension or other practices that artificially inflate impressions

2. Protect data and reputation – news publisher purged 30+ trackers from their sites to minimize “data leakage” and stopped selling remnant/unsold inventory on exchanges

3. Consistently prove ROI – specialty publisher limited ads to 3 per page, lazy loads all ads, filters all known bots by name; better business outcomes proven over time

“hard work and consistency will pay off”



How Fraud HarmsAdvertisers



How many clicks/sessions/views do you want?

click on links

load webpages tune bounce rate

tune pages/visit

“bad guys’ bots are advanced enough to fake most metrics”



What click through rates are you shooting for?Programmatic display

(18-45% clicks from advanced bots)Premium publishers(0% clicks from bots)

0.13% CTR(18% of clicks by bots)



Campaign KPI: CTRs



Want 100% viewability? 0% NHT (bots)?

Bad guys cheat and stack ALL ads above the fold to make 100% viewability.

“100% viewability? Sure, no problem.”

AD

• IAS filtered traffic, • DV filtered traffic• Pixalate filtered traffic, • MOAT filtered traffic, • Forensiq filtered traffic

“0% NHT? Sure, no problem.”



Best Practices of Savvy Advertisers“don’t assume your agency took care of it”

• Challenge all assumptions – don’t assume someone else “took care of it.” Verify, by demanding line-item detailed reports, because fraud hides easily in averages

• Check your Google Analytics - question anything that looks suspicious; more details that can reveal fraud and waste

• Corroborate measurements – measure different parameters together and see if they still make sense together; reduce false positives or negatives

• Use conversion metrics – CPG client uses click-and-print digital coupons; pharma client uses doctor finder zip code searches, plus clicks to doctor pages; retailers use sales



Ad Fraud Hits NewAll Time Highs



Methbot eats $1 in $6 of $10B video ad spend

Source: Dec 2016 Whiteops Discloses Methbot Research

“the largest ad fraud discovered to date, a single botnet, Methbot, steals $3 - $5 million

per day, $2 billion annualized.”

1. Targets video ad inventory$13 average CPM, 10X higher than display ads

2. Disguised as good publishersPretending to be good publishers to cover tracks

3. Simulated human actionsActively faked clicks, mouse movements, page scrolling

4. Obfuscated data center originsData center bots pretended to be from residential IP addresses

http://www.whiteops.com/METHBOT







Mobile fraud is much larger than detected

“bad guys’ apps don’t install fraud detection SDKs; so the reported low rate of fraud is due to only good apps being measured.”

Mobile app install fraud research (via mxpresso)• 50 – 70% mobile devices were fake• 40 – 50% of the app installs were fake• 10 – 20% were faked Play Store installs



Implications for Digital Media



Humans block ads; fraud bots don’tComparing high human vs high bot samples

96% bots sample

42% ad blocked

1% ad blocked

93% human sample

Comparing ad blocking vs non-ad blocking samples

ad blocking ON

ad blocking OFF



Ad impressions served mostly to bots, by far

Total Human Users – 115 million

Visitors (U.S. Only)

U.S. Internet – 285 million

Source: eMarketer 2016 estimate Source: Distil Networks 2015

Adblock Users (humans) – 45 million

Source: PageFair / Adobe 2015

“subtracting adblocking humans, your open exchange ad impressions are being served to a population that is disproportionally non-human.”

Non-Human Traffic (NHT) HUMAN VISITORS

ads served

“fraud sites” “sites w/ questionable practices” “good guys”Websites

3% IVT caught by industry lists

39%Ad blocking humans

71% 29%



No matter how much traffic, bots don’t convert

102,231 sessions

0 sessions

goal events – no change

bot traffic turned off

bot traffic turned off



Other Hidden Dangers



Analytics are messed up by fake data

7% conversion rate 13% conversion rateartificially low actually correct



Real human audiences stolen from publishers

specialized audience:human oncologists

jco.ascopubs.org

specialized audience can be targeted elsewhere

“cookie matching”(by placing javascript on your site)

FOR EXAMPLE ONLY



In-ad measurements could be entirely wrong

Publisher Webpagepublisher.com

Foreign Ad iFramesadserver.com

Cross-domain (XSS) security restrictions mean iframe cannot:• read content in parent frame• detect actions in parent frame• see where it is on the page

(above- or below- fold)• detect characteristics of the

parent page

1x1 pixeljs ad tags ride along

inside iframe

incorrectly reported as 100% viewable

parent frameforeign iframes



On-site Javascript poses gaping security risks

Source: https://www.exchangewire.com/blog/2016/05/19/%E2%80%8Bon-site-javascript-trackers-open-gaping-security-holes/



From our First-hand Data



Visually show differences in quality / humanness

good publishers

ad exchanges/networks

volume bars (green)

Stacked percentBlue (human)Red (bots)

red v blue trendlines



Traffic surges caused by bots vs real humans

Caused by bots

Caused by humans



Publishers taking action to reduce bots

Publisher 1 – stopped buying traffic

Publisher 2 – filtered data center traffic



Stepwise improvement using our data

Period 1 Period 3Period 2

Initial baseline measurement

Measurement after first optimization

Eliminating several “problematic” networks



Advertisers buying low vs high quality media

Traffic to Site from Buying LOW quality media

Traffic to Site from Buying HIGH quality media



Better media leads to way better outcomesMeasure Ads Measure Arrivals Measure Conversions

clean, good media

low-cost media, ad exchanges

346

1743

5

156

30X better outcomes • More arrivals• Better quality



About the Author

January 2017Augustine Fou, [email protected] 212. 203 .7239

mailto:[email protected]



Dr. Augustine Fou – Independent Ad Fraud Researcher2013

2014

Follow me on LinkedIn (click) and on Twitter @acfou (click)

Further reading:http://www.slideshare.net/augustinefou/presentationshttps://www.linkedin.com/today/author/augustinefou

2016

2015

http://www.linkedin.com/today/author/augustinefou

http://twitter.com/acfou

http://twitter.com/acfou

http://www.slideshare.net/augustinefou/presentations

https://www.linkedin.com/today/author/augustinefou



Harvard Business Review – October 2015

Excerpt:

Hunting the Bots

Fou, a prodigy who earned a Ph.D. from MIT at 23, belongs to the generation that witnessed the rise of digital marketers, having crafted his trade at American Express, one of the most successful American consumer brands, and at Omnicom, one of the largest global advertising agencies. Eventually stepping away from corporate life, Fou started his own practice, focusing on digital marketing fraud investigation.

Fou’s experiment proved that fake traffic is unproductive traffic. The fake visitors inflated the traffic statistics but contributed nothing to conversions, which stayed steady even after the traffic plummeted (bottom chart). Fake traffic is generated by “bad-guy bots.” A bot is computer code that runs automated tasks.