Fake Everything 2019 Update

May 2019 / Page 0marketing.scienceconsulting group, inc.
linkedin.com/in/augustinefou
Everything Fake
2019 Update
May 2019
Augustine Fou, PhD.
acfou [at] mktsci.com
212. 203 .7239

(2013) Everything Fake
https://www.slideshare.net/augustinefou/everything-fake-click-fraud-fake-pages-botnets-ad-waste-reduction
“fake since (at least) 2013”

Fake sites - programmatic
Using software to create thousands of sites automatically
Source: SimilarWeb

Fake Websites - random
Get paid to make fake websites (“cash-out” sites) for ad fraud
• No content or
content that is
assembled (i.e.
plagiarized)
• Content not human
readable
• Stuffed with large
numbers of ads
• Page auto-reloads
• Large abrupt traffic
changes

Fake Content – made by bot
Pages stuffed with search keywords to attract free organic traffic
Characteristics
• Auto-generated by bots, stuffed with
search keywords
• Stuffed with affiliate links and ads
“More news is
being written
by robots than
you think.”
Souce:
Singularity Hub
March 2014
Mr. Johansson's program
scrubs databases and
other digital sources for
information, and then
packages it into an article.
On a good day, he says his
"Lsjbot" creates up to
10,000 new entries (PER
DAY)
Source: WSJ July 2014

Fake Videos – referral links
For driving fake referral traffic to sites, attribution fraud
http://www.youtube.com /watch?v=xnkM9RrDzhM
Banned Celebrity Sex Tapes
Banned sex tapes .com

Fake YouTube Videos - SEO
Keyword-stuffed for video SEO for fake sites (free traffic)
http://www.youtube.com /watch?v=upSOCzlSoHk
http://www.youtube.com/
watch?v=lhbDGpqCmZQ
http://www.youtube.com/
watch?v=UcdiM4uD6fM
http://www.youtube.com
/watch?v=an6xRpQ5Wh8
Duplicated videos
Some carry ads to
generate ad revenue

Fake video views - purchased
Straight line in video views – same quantity added per time period
http://www.youtube.com/watch?v=iP6XpLQM2Cs
Actual interest
Straight line
– purchased views

Fake Sites / Apps
1221e236c3f8703.com
62b70ac32d4614b.com
a6f845e6c37b2833148.com
da60995df247712.com
d869381a42af33b.com
a1b1ea8f418ca02ad4e.com
1de10ecf04779.com
2c0dad36bdb9eb859f0.com
a6be07586bc4a7.com
fe95a992e6afb.com
42eed1a0d9c129.com
da6fda11b2b0ba.com
afa9bdfa63bf7.com
739c49a8c68917.com
baa2e174884c9c0460e.com
d602196786e42d.com
153105c2f9564.com
8761f9f83613.com
20a840a14a0ef7d6.com
31a5610ce3a8a2.com
5726303d87522d05.com
3ac901bf5793b0fccff.com
b014381c95cb.com
2137dc12f9d8.com
06f09b1008ae993a5a.com
fbfd396918c60838.com
97ff623306ff4c26996.com
b1f6fe5e3f0c3c8ba6.com
23205523023daea6.com
6068a17eed25.com
b1fe8a95ae27823.com
f4906b7c15ba.com
eac0823ca94e3c07.com
1f7de8569ea97f0614.com
21c9a53484951.com
24ad89fc2690ed9369.com
efd3b86a5fbddda.com
34c2f22e9503ace.com
0926a687679d337e9d.com
6a40194bef976cc.com
33ae985c0ea917.com
02aa19117f396e9.com
f8260adbf8558d6.com
9376ec23d50b1.com
pushedwebnews.com
a0675c1160de6c6.com
0f461325bf56c3e1b9.com
850a54dbd2398a2.com
com.dxnxbgj.mkridqxviiqaogw
com.obugniljhe.fptvznqwhmcjm
com.bpo.ksuhpsdkgvbtlsw
com.rlcznwgouw.vvtexstbfttngc
com.kasbgf.sbzwtgpcbjexi
com.bprlgbl.vbze
com.zka.lzhsoueilo
com.alxsavx.mizzucnlb
com.jxknvk.lrwfdfirdzpsw
com.tvwvqbt.wbshaguqy
com.iwnxtpahcu.leyuehdwdbb
com.okf.rhvemtykfibzpxj
com.obpmirzste.ldsjpv
com.zmm.shmxvjxnsagndui
com.nqzwr.leusrmpmsq
com.rced.zcdsglptpdlwpu
com.kerms.ehlsgnc
com.cmia.iabhheltm
com.skggynmtx.tyyjnwpefvqtll
com.kgdtltnuv.hayvfhob
com.ztzsiqg.dyojlxdscxws
com.xlwuqe.ddrdhsuosbn
com.rkrhmzee.wjcoznxu
com.ebhzb.hbzvomzpcctovj
Fake sites Fake sites Fake apps

Fake Apps for loading ads
Hundreds of fake or cloned apps, make money via ads

Cloned apps, adware SDKs
Apps are easily cloned; free software development kits given away
Apps are cloned
thousands of times;
some didn’t even
bother to change the
colors or cover
graphics.
Bad guys accidentally
cloned apps that
already had detection
SDK in it – from 312, to
750, to 1,330 copies.
Source: CNBC, Aug 2017

Fake Antivirus Apps
Fake apps that get lots of permissions on user device
https://www.zdnet.com/article/two-thirds-
of-all-android-antivirus-apps-are-frauds/
https://www.techspot.com/news/79226-more-
than-two-thirds-all-android-antivirus-apps.html

Fake App Installs, Re-installs
Fake mobile devices install legit apps, get paid cost-per-install (CPI)
“Machine has analysed over 22.4 million app installs,
56% of which we detected as fraudulent.
Of these 88 ad networks, only a single one delivered
less than 10% fraudulent app installs. The rest fell
somewhere between 15% and 100% fraudulent. Five
of the networks delivered 100% fraudulent installs.”
https://www.tune.com/blog/install-or-reinstall-42-of-mobile-app-
installs-are-actually-reinstalls-and-in-some-categories-its-75/
34 mobile networks were >50% fraud
42% Installs were Actually Re-Installs
https://www.linkedin.com/pulse/machine-report-10-gary-danks/

Fake downloads, boost rank
Download/purchase own apps with bots to get to top 25 list

Fake IDFAs on real devices
Bad apps rotate faked/copied device IDs to defeat frequency caps
Source: Cinarra Systems

tOLKnMEzcnARvLTvChnt
Random vs Replayed DeviceIDs
Techniques to defeat fraud detection and frequency caps
RANDOM deviceIDs
lXvBEeRXPURtcKILYFYE
IdUkQeWgqshMmfMdzlAx
INIjBzHJHywhgRsMdQPe
tiAnxwuKBNCjoMetZaPN
UjtRbuUTvYUwmABhmPGH
MDSUUgkENQkQDztavzfl
iljoJEXUcLCEFwSdrwZn
APbLSRUvlrIoofIchhLg
NZXVVKCbymRYBSStNRYz
UiSBmuDpYLkNvsHBKcri
LZyhgblHtMIMaAliHWYB
vKFknsnhGouIucYgxmdu
• If fraud detection hasn’t seen a device
before, the default action is to let the ad
serve
• Frequency caps based on deviceIDs are
defeated, each device appears as new
• Valid deviceIDs are harvested from real
devices and sent to fake devices or apps to
replay
• Replayed deviceIDs are used by fraudulent
apps to defeat fraud detection
REPLAYED deviceIDs
validated deviceID harvested

Fake apps to infect devices
Fake versions of popular apps are used to infect humans’ devices
Source: Independent, Jun 2018Source: Fortune, July 2016

Fake VPN – malware/adware
Fake “free VPNs” track users’ browsing history, serve more ads
Source: PC Magazine, Jun 2018 Source: ZDNet, May 2015 Source: TechCrunch, Feb 2019

Faked Google Analytics
Manipulate Google analytics to show traffic that is non-existent
Source: https://youtu.be/6_F-NAvr39o
https://www.youtube.com/watch?v=ztiBQASKld8

Faked mouse moves/clicks
Create fake mouse movements and clicks on ads using javascript
Source: https://youtu.be/HeGYr3jwubY

Fake clicks – attribution scams
Directly loading attribution urls and SDKs are to show fake clicks
Source: Method Media Intelligence

Fake data
Software tool to rotate referrer, browser agent, IP address
Source: Ratko Vidakovic

Fake Geolocation
Houston, TX
5am local time
Bozeman, MT
4am local time
Same set of 15 apps calling ads from both locations, at times when humans are not awake yet

Fake Agencies
Out of the 28 fake ad agencies, only
20 ever had any activity in advertising
markets. We believe Zirconium was
progressively rolling out their agencies
to overcome occasional bans, as they
progressively got caught. We observed
a pace of 1 to 3 releases per month.
Since the majority of agencies were
created around February 2017, the
dormant ones progressively built
precious reputation (mostly history,
and social media following) to pose as
established companies and maximize
their potential of striking deals with
more ad platforms.
Source: Confiant, 2017

Fake traffic, laundered
Fake sites don’t sell ads directly; they feed traffic to other sites
Advertisers
impacted

Fake pageviews – auto-redirect
Webpages auto-redirect to other pages/sites in infinite loops
How much does it cost?How much is available?
a.k.a. “zero-click” “pop-under” “forced-view” “auto-nav”

Traffic sellers’ “high quality traffic”
Many sources to buy “traffic,” tune “quality” level, host bots
Google
“buy real human traffic”
Select vendor and
“traffic quality level”
Host your own bots
(cost $3.99/mo)

Luminati[.]io Oxylabs[.]io Smartproxy[.]com
Residential Proxies
Use: to disguise data center bots to appear to come from
residential IP addresses, avoid detection
40 million
IP addresses
10 million
IP addresses
30 million
IP addresses

Fake or plagiarized ads.txt
Source: MediaMath
Fake sites rushed to put ads.txt files in place, to continue to sell
“the company will only buy
… from publishers who have
an ads.txt file in place.”
“completely useless…
… fake and fraud sites just
put ads.txt files in place so
they can continue to sell
inventory.”

Fake domains, bids
Fake sites disguise themselves as good domains to sell inventory
publisherA.com
PublisherA does
NOT sell ads on
open exchanges!
100% spoofed inventory
“In “domain spoofing,” bad actors intentionally disguise the
nature of the ad space they’re selling. That inventory is
made available via automated marketplaces run by ad tech
companies such as the ones the FT highlighted. In the end, a
marketer might believe they’re paying for ads on FT.com,
but their ads may actually appear on other sites with
questionable content and unknown ownership.”
https://www.wsj.com/articles/financial-
times-finds-counterfeit-ad-space-was-
offered-by-at-least-six-companies-
1507563713

Fake GDPR consent strings
Humans don’t give consent; bots consent to be shown more ads
Source: CNBC, July 2018
• Humans don’t give consent
to ad tech companies they
have never heard of.
• Bots give consent so more
ads can be delivered to
them
• Forged consent flags that
are not verifiable are used
to continue programmatic
ad trading

Fake ad impressions
“dark processes” are continuous loading of ads, in background
https://youtu.be/utoN_VlxtE0
(demo video of page continuously
loading ads in the background)

Fake mobile display ads
May 26 Forbes “Judy Malware”
• 40 bad apps to load ads
• 36 million fake devices to load
bad apps
• e.g. 30 ads per device /minute
• 30 ads per minute = 1 billion
fraud impressions per minute
June 1 Checkpoint “Fireball”
• 250 million infected computers
• primary use = traffic for ad
fraud
• 4 ads /pageview (2s load time)
• fraudulent impressions at the
rate of 30 billion per minuteSource: June 2017 “Chinese click
fraud gang in Thailand arrested”
300 real devices
used for click fraud

Fake ads - malvertising
Ransomware can be delivered through ads with malicious code
Source: ZDNet, March 2017 Source: TechRepublic, June 2017

Fake mobile traffic - apps
Fraud apps repeatedly loading webpages, w/ hidden browsers
Gallery urls repeatedly loaded
By same apps, in the same ratios

Fake Traffic - pop-unders
Porn sites have humans; click play, spawn pop-under, load ads
Source: Digiday Feb 2017 Source: BuzzFeed Dec 2017

Pages auto-load other pages
Code causes pages to auto-load other pages repeatedly
Source: https://youtu.be/KluP64UI9Jg

Fake ad blockers – more ads
Instead of blocking, fake ad blockers load more ads and track users
Source: Engadget, April 2018

Fake profiles
“[LOTAME] purged 400
million of its over 4
billion profiles after
identifying them as
bots or otherwise
fraudulent accounts.
Lotame CEO Andy
Monfried estimated
that 40 percent of all
web traffic is fictional.”
Source: Adweek, Feb 2018

Fake LinkedIn Profiles
Used to simulate “user engagement” (ad clicks), audiences
bot generated content
stock photo

Facebook purges 1.3 billion fake
“It was barely a year ago that
Facebook proudly declared it had
more than 2.2 billion monthly
users. But on Tuesday, the social
media giant revealed
some stunning data, including
that during the six months ending
in March, Facebook disabled a
total of almost 1.3 billion fake
accounts.
During the first quarter of 2018,
Facebook says it deleted 865
million posts, the vast majority of
it for being spammy, and the
remainder for containing graphic
violence, sexual activity or nudity,
terrorism or hate speech.
Source: Inc. May 2018

Fake Twitter accounts - bots
Used to sell fake followers, likes, retweets, ad views and clicks
https://www.cnbc.com/2017/03/10/nearly-48-million-twitter-accounts-
could-be-bots-says-study.html
“A big chunk of those "likes," "retweets," and
"followers" lighting up your Twitter account
may not be coming from human hands.
According to new research from the
University of Southern California and Indiana
University.
Since Twitter currently has 319 million
monthly active users, that translates to nearly
48 million bot accounts.”

Fake influencers
Fake influencers bought followers to appear to be influential
Source: Adweek, Jun 2018
“an array of entertainers, entrepreneurs,
athletes and media figures, … bought Twitter
followers or artificial engagement. A New York
Times article on Saturday describing a vast trade
in fake followers and fraudulent engagement on
Twitter and other social media sites, often using
personal information taken from real users.
https://www.nytimes.com/interactive/2018/01/27/technology/social-media-bots.html
NY Times: The Follower Factory

Fake segments - seasonality
Bots browse items by season to attract higher retargeting CPMs
Source: DataXu/DoubleVerify Webinar, April 2015
“look at backpacks in back-to-school season”

Fake behaviors
Bots visit collections of sites to make themselves look attractive
“cookie matching”
Bots pretend to be oncologists
by visiting sites, collecting cookie
Attract ad dollars to fake
sites when retargeted

Fake Personality Quizzes
Used to harvest personal info, meta data for later use in hacking
http://www.businessinsider.com/nametestscom-may-have-
exposed-facebook-data-of-120-million-users-2018-6
https://www.wired.com/story/facebook-exposed-87-
million-users-to-cambridge-analytica/

Fake Celebrity Lookalike
Collect self-selected face photos for later use in hacking
Users self-select face
pictures to use for
“which celebrity do you
look like” quizzes. These
are harvested for later
use in hacking.

Fake tech support scams
Designed to trick panicked consumers into downloading or calling
https://arstechnica.com/information-technology/2018/07/tech-support-
scammers-revive-bug-that-sends-chrome-users-into-a-panic/
https://twitter.com/robleathern/status/1014883138684661761

Fake Sweepstakes/PrizesUsed to steal users’ email addresses and other personal information

Preinstalled adware/spyware
Companies get paid to pre-install adware/spyware on devices
Source: TheVerge, Jul 2017 Source: CNN, Feb 2015

Fake botnet for publicity
PRESS RELEASE:
“used highly sophisticated techniques to
fraudulently load ads on the affected sites
without the site owners' consent, leveraging
a new methodology that allows it to
monetize inventory on premium domains.”
“The botnet was completely fabricated for the press
release announcing their new algo. None of this
actually happened; no ads were injected into any of
the sites they named in the press release. This was
confirmed by direct measurement on the good
publishers’ sites. They were falsely accused and their
reputation was harmed by this publicity stunt.
The failure of the fraud detection was due to their
analyzing only pre-bid data, and using big data and
machine learning approaches, without an
understanding of actual ad serving tech, javascript,
and how browsers work.”

Fake Leads (Lead Fraud)
Real data collected from breaches create leads that appear valid
Fake leads
• Previously filled out by hand
• Now, fully automated with
bots using databases of real
postal addresses, etc. (that
trick verification engines)

#defendthespend
“marketers can (and should) reduce the
flow of dollars to cybercriminals that are
committing ‘major economic crimes’.”
Then, and only then, will we get
back to REAL digital marketing.”

Digital Marketing circa 2018

About the Author
Augustine Fou, PhD.
acfou [@] mktsci.com
212. 203 .7239

Dr. Augustine Fou – Researcher
2013
2014
Published slide decks and posts:
http://www.slideshare.net/augustinefou/presentations
https://www.linkedin.com/today/author/augustinefou
2016
2015
2017
20192018

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