State of Ad Fraud #RampUp17

State of Digital Ad Fraud
#RampUp17 Publisher Update
March 2017
Augustine Fou, PhD.
acfou@mktsci.com
212. 203 .7239

Ad Fraud is VERY
Profitable and Scalable

March 2017 / Page 2marketing.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%…”

How scalable are fraud operations? MASSIVELY
Cash out sites are massively scalable
131 ads on page
X
100 iframes
=
13,100 ads /page
One visit redirected dozens of times
Known blackhat
technique to hide
real referrer and
replace with faked
referrer.
Example how-to:
http://www.blackhatworld.co
m/blackhat-seo/cloaking-
content-generators/36830-
cloaking-redirect-referer.html
Thousands of requests per page
Single 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

Ad fraud is now the largest form of crime
$20 billion
Counterfeit
Goods U.S.
$18 billion
Somali
pirates
$70B 2016E
Digital Ad
Spending
Bank
robberies
$38 million
$31 billion
U.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

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 publishers
Pretending to be good
publishers to cover tracks
3. Simulated human actions
Actively faked clicks, mouse
movements, page scrolling
4. Obfuscated data center origins
Data center bots pretended to
be from residential IP addresses

Where is Ad Fraud
Concentrated?

CPM/CPC buckets (91% of spend) is most targeted
Impressions
(CPM/CPV)
Clicks
(CPC)
Search
27%
91% digital spend
Display
10%
Video
7%
Mobile
47%
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 Fraud
Impression
(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”

Countless fraud sites made by template
100%
bot

Bots are automated browsers used for ad fraud
Headless Browsers
Selenium
PhantomJS
Zombie.js
SlimerJS
Mobile Simulators
35 listed
Bots are made from malware
compromised PCs or headless
browsers (no screen) in datacenters.
Bots

Bots range in sophistication, and therefore cost
Javascript installed
on webpage
Malware on PCsData Center BotsOn-Page Bots
Headless browsers
in data centers
Malware installed on
humans’ devices
Less sophisticated Most sophisticated
Source: AdAge/Augustine Fou, Mar 2014 Source: Forensiq Source: Augustine Fou, Oct 2015
“the official industry lists of bots catch NONE
of these bots, not one.”
1 cent CPMs
Load pages, click
10 cent CPMs
Fake scroll, mouse
movement, click
1 dollar CPMs
Replay human-like mouse
movements, clone cookies

“The equation of ad fraud is simple:
buy traffic for $1 CPMs, sell ads for
$10 CPMs; pocket $9 of pure profit.”

How Ad Fraud Harms
Good Publishers

What I heard from Publishers
“Ad fraud doesn’t affect us”
“I wasn’t really aware of bots and fraud”
“Our SSP has an anti-fraud vendor”
“we checked, we have very low bots”

Top-line ad revenue stolen
1. Bot visits good
publisher site to
collect “cookie”
2. Bot then visits fake sites to
cause ad impressions to load
there; those sites make the
ad revenue
www.nejm.org healthsiteproductionalways.com

Bottom-line profit margin squeezed
www.nejm.org healthsiteproductionalways.com
$100 CPMs $0.10 CPMsvs
“Media agencies will buy more of the low-cost
stuff to lower their average costs.”

http://www.olay.co
m/skin-care-
products/OlayPro-
X?utm_source=msn
&utm_medium=cpc
&utm_campaign=Ol
ay_Search_Desktop
Reputations at-risk, bad guys cover tracks
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 page
fake source declared
3. Click through to
destination page

Premium audiences stolen by cookie matching
specialized audience:
oncologists
jco.ascopubs.org
specialized audience can
be targeted elsewhere
“cookie matching”
(by placing javascript on your site)

Bad measurements wrongly accuse publishers
Publisher 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

In-ad JS measurements could be entirely wrong
Publisher Webpage
publisher.com
Foreign Ad iFrames
adserver.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 pixel
js ad tags
ride along
inside iframe
incorrectly reported as
100% viewable

Unfair fight because bad guys cheat
“Bad guys have higher (fake) viewability”
AD
Bad guys cheat by
stacking all ads
above the fold to
fake 100% viewability
Good guys have to array
ads on the page – e.g.
lower average viewability.

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

How Ad Fraud Harms
Advertisers

Messes up your analytics
click on links
load webpages tune bounce rate
tune pages/visit
“bad guys’ bots are advanced enough to fake most metrics”

Messes up your KPIs
Programmatic display
(18-45% clicks from advanced bots)
Premium publishers
(0% clicks from bots)
0.13% CTR
(18% of clicks by bots)
1.32% CTR
5.93% CTR
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.”

Current State of NHT Detection

Fraud bots are NOT on any list
user-agents.org
bad guys’ bots
2% and “on the wane”
Source: GroupM, Feb 2017
bot list-matching
4%
Source: IAB Australia, Mar 2017
400
bot names in list
“not on any list”
disguised as popular
browsers – Internet
Explorer; constantly
adapting to avoid
detection
10,000
bots observed
in the wild

Three main places for NHT detection
In-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

5% bots doesn’t mean 95% humans
good publishers
ad exchanges/networks
volume bars (green)
Stacked percent
Blue (human)
Red (bots)
red v blue trendlines

“Having fraud DETECTION is not the
same as having fraud PROTECTION.”

Example of publishers taking action to reduce bots
Publisher 1 – stopped buying traffic
Publisher 2 – filtered data center traffic

Publishers filtering bots – on-site vs in-ad
On-Site measurement, bots
are still coming
In-Ad measurement, bots
and data centers filtered
10% red
-7%
3%

About the Author
March 2017
Augustine Fou, PhD.
acfou@mktsci.com
212. 203 .7239

Dr. Augustine Fou – Independent Ad Fraud Researcher
2013
2014
Follow me on LinkedIn (click) and on Twitter
@acfou (click)
Further reading:
http://www.slideshare.net/augustinefou/presentations
https://www.linkedin.com/today/author/augustinefou
2016
2015

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.

State of Ad Fraud #RampUp17

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