Ad fraud is not just display ad fraud and click fraud. There is an entire ecosystem set up to support it. Different elements in the ecosystem play specialized roles. And bad guys make money at every point.

1. The Ad Fraud Ecosystem
2017 Update
January 2017
Augustine Fou, PhD.
acfou@mktsci.com
212. 203 .7239

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Fraud siphons 1/2 of dollars out of ad ecosystem
Advertisers
“ad spend” in digital
approaches $70B in 2016
Publishers
are left with only
1/2 of the dollars
Bad Guys
siphon 1/2 of ad spend
OUT of the ecosystem
• Ad dollars are being siphoned OUT of the ecosystem into the pockets of the bad guys
• Advertisers have lower ROI due to fraud (fake impressions/clicks, non-humans/bots)
• Publishers have lower revenues (ad dollars stolen by bad guys)
1/2
1/2
Users
use ad blocking and need
to protect themselves

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The fraud ecosystem makes money at every point
Advertisers
“ad spend” in digital
nears $70B in 2016
Publishers
are left with only
1/2 of the dollars
“Farmers”
Hackers whose job it is to
get malware on computers,
so botnets can be created.
1/2
1/2
Piracy/Porn Sites
Sites that attract real human
visitors in order to attempt
to plant malware on PCs.
Fake Sites
Sites with fake content,
created solely to carry ads
(display, video, search)
Fake Visitors/Bots
Bots that hit webpages to
cause ad impressions, click
on ads, collect cookies.
Sourced Traffic
Unknown users from
unknown places that can be
purchased for low CPMs
Audience Extension
Attracting audiences
through related content
which launders origins.
Fake Accounts
Fake Facebook, Twitter,
LinkedIn, etc. accounts used
to gather data/compromise
Ad Network
Network willing to look the
other way because they get
a cut of the payout
Data Management
Fake or falsified data to
launder or cover up fraud;
also command premiums
fraud ecosystem

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The Role of Piracy/Porn Sites

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Piracy sites provide specialized services to fraud ecosystem
Because piracy sites have real human visitors, they have specialized to provide valuable services
to the fraud ecosystem; and they get paid handsomely for such services.
Malware/Adware (runs on PC) Toolbars / Ad Injection (in browser)
Source: DoubleVerify Case Study
Cookie Cloning (for retargeting) Mouse Movement Recording
Image Source: BenEdelman.org
Source: Forensiq
• smokeybear.com
• rei.com
• travelandleisure.com
• natgeo.com
=
outdoor enthusiast

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Where piracy sites fit in the digital ad fraud ecosystem
• Malware on humans’ PCs
are used to make botnets
• Real human’s cookies
used for retargeting
Piracy Sites Ad Fraud Sites Click Fraud Sites
Specialty • Bots that cause pages and
ads to load
• Generating display and
video ad impressions
• Advanced bots that can do
“human-like” things like
type search term and click
on CPC ad
• CPM on served ads
• Paid to plant malware
Revenue • CPM on served ads
(display, video)
• CPC on clicks on search ads
(search partner network)
Fraud Types • Malware / Toolbar / Virus
• Sourced Traffic
• Fake Ad Impressions
• Bot Traffic
• Fake Display Ads
• Fake Video Ads
• All Bot Traffic
• All Fake Ads
• All Fake Clicks

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Difference between piracy sites and other ad fraud sites
Piracy Sites Ad Fraud Sites
(CPM fraud)
Click Fraud Sites
(CPC fraud)
Real humans that click (e.g.
click play button)
Traffic Bots: create fake ad
impressions on pageload
Bots: create fake searches
and search clicks
To attract real human users
to plant malware/viruses
Purpose
Pirated movies and music
that humans seek
Content
To carry display ads that can
be sold into ad exchanges
None; or plagiarized content
assembled by algorithm
To carry search ads that
earn revenue share
None; or plagiarized content
assembled by algorithm
56 M /mo

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Evolution of Fraud in Display Ads

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History of Banner / Display / Impression (CPM) Advertising
Example
Sales Method
Placement
Quantity
of Sites
Fraud Levels
Automation
1995
Yahoo.com
Portals
Direct
Sales
On-site Ad
Placement
10^2
Nascent
n/a
10^9
2000
ESPN.com
Publishers
Direct
Sales
On-site Ad
Placement
10^3
Low
n/a
10^7
2005
Gizmodo.com
Blogs
Network
Sales
Network Ad
Placement
10^6
Medium
Nascent
10^6
2010
Doubleclick
Ad Networks
Network
Sales
Network /
Automated
10^9
High
Automated
10^5Each Site’s
Traffic
2015
AppNexus
Ad Exchanges
RTB/Auction
Programmatic
10^12
Very High
Fully Automated
10^1

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Evolution of Impression (CPM) Ad Fraud
2005
Blogs
Need more
visitors
n/a
2010
Ad Networks
Need more traffic to
drive ad impressions
Network / Automated /
Self-serve
2015
Ad Exchanges
Programmatic /
RTB
Motive
Opportunity
10^6 10^9 10^12# of Sites
Types of Fraud n/a
n/a 20% 70%Programmatic
10^6 10^5 10^1Site’s Traffic
More revenues for
more ad impressions
n/a Traffic sourcing and bots
(20% of network traffic)
Bots (60% of
network traffic)
Means
Piracy Sites
10^3
yes
10^7
Need more
visitors
Humans seek
pirated content
Web scrapers /
automated tools
• Malware
• Bot Traffic
• Fake Ad Imp.
• Bot Traffic
• Fake Display Ads
• Fake Video Ads
• All Bot Traffic
• All Fake Ads
• All Fake Clicks

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

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Dr. Augustine Fou – Recognized Expert on Ad Fraud
2013
2014
SPEAKING ENGAGEMENTS / PANELS
4A’s Webinar on Ad Fraud
AdCouncil Webinar on Ad Fraud
TelX Marketplace Live Panel on Cybersecurity
ARF Audience Measurement / ReThink
IAB Webinar on Ad Fraud / Botnets
AdMonsters Publishers Forum / OPS
DMA Webinar – Ad Fraud & Measurement
2016
2015

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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.