Everything Fake
and How to Find Out
Dr. Augustine Fou
http://linkd.in/augustinefou
April 2013
-1-

Augustine Fou
Display Ads
Fake Pageviews/Impressions

-2-

Augustine Fou
Percent of Web Traffic From Bots

Source: Solve Media via Marketing Charts April 26, 2013
-3-

Augustine Fou
Hidden Layers and Impressions
Ads are hidden in up to 72
layers
Entire web pages are loaded
into ad iframes to boost
impre...
Bot Traffic - Web vs Mobile

Source: Solve Media via Marketing Charts April 26, 2013
-5-

Augustine Fou
“Suspect” Publishers
Called out by Mike
Shields, “Meet the
Most Suspect
Publishers on the WebThe rise of ghost sites,
wher...
Bad Guys Rob Display Advertisers
1
2
3
4

6

5

7

• Pages are auto generated by script to
optimize for high value search
...
Search Ads
Fake Clicks
-8-

Augustine Fou
Click Fraud % of PPC Impressions

Source: ClickForensics 2006 - 2010

-9-

Augustine Fou
Botnets cause most Click Fraud
42% of click fraud caused by botnets

Source: ClickForensics 2009
- 10 -

Augustine Fou
Bad Guys Do Way Better SEO
For expensive items like
the Vizio 60 inch HDTV,
bad guys appeared in 8 of
the 10 search result...
Bad Guys Kick Pharma in SEO
6 of the 10 page 1 results
are bad guys websites,
siphoning traffic from the
brand website and...
Facebook
Fake Profiles Fake Likes

- 13 -

Augustine Fou
Fake Profiles on Facebook

plagiarized or stock image

- 14 -

Augustine Fou
Bot generated pages
on Facebook

using many of the same images and randomized content
- 15 -

Augustine Fou
Fake Likes on Facebook
How much do you like courgettes, the
green vegetable Americans call zucchini?
According to one Face...
UPDATE: Feb 10, 2014
Here's how Muller says he knows the Likes are
fake: Rather than being distributed across the
engageme...
Affiliate Links
Cookie Stuffing
- 18 -

Augustine Fou
Fake Page w/ Affiliate Links
Characteristics
• Auto-generated by
bots, stuffed with
search keywords
• Attract organic sear...
Man Defrauds eBay of $5.6M
Using “cookie stuffing”
techniques, the company
planted affiliate cookies
which led to revenue ...
Twitter
Fake Accounts/Malware Tweets

- 21 -

Augustine Fou
Buying Followers on Twitter

- 22 -

Augustine Fou
Most Fake Followers

- 23 -

Augustine Fou
YouTube
Fake Views

- 24 -

Augustine Fou
Fake Views on YouTube

- 25 -

Augustine Fou
LinkedIn
Fake Profiles

- 26 -

Augustine Fou
Fake Profiles on LinkedIn
stock photo

bot generated content
- 27 -

Augustine Fou
Email/Spam
- 28 -

Augustine Fou
Phishing Email/Malware Site

- 29 -

Augustine Fou
Phishing /
Spear Phishing
- 30 -

Augustine Fou
Fake Sweepstakes

Users enter email address
and other personal info to
enter sweepstakes.
- 31 -

Augustine Fou
Phishing for Personal Info

Source: Gizmodo, July 14, 2013
- 32 -

Augustine Fou
Fbishing is the new Phishing
Beware of fake Facebook profiles asking
to be friends with you. This is the new
“phishing” th...
Electronic Frontier Foundation
“If we ask whether a fact about a person identifies that
person, it turns out that the answ...
Malware Sites
- 35 -

Augustine Fou
60,000 Malware Sites/week
Detected per week

Source: Google Safe Browsing Initiative, Report

- 36 -

Augustine Fou
Blog Comment Spam

- 37 -

Augustine Fou
Automated Comment Spam

Providing linkbacks to malware websites

- 38 -

Augustine Fou
Inserting Web Pages
Into Browser History
- 39 -

Augustine Fou
Page of Ads Inserted in History
For example, when users
click on a web page from the
Google search results, but
don't find...
Related Articles
Bad Guys Happily Rob Display Advertisers
By: Augustine Fou, July 23, 2012
More on “cookie stuffing”
http:...
Dr. Augustine Fou – Digital Consigliere
“Users can customize their smartphone
precisely, by downloading only the apps
they...
APPENDIX

- 43 -

Augustine Fou
Click Fraud % of PPC Impressions

Source: ClickForensics 2006 - 2010
- 44 -

Augustine Fou
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Transcript of "Everything Fake Click Fraud Fake Pages Botnets Ad Waste Reduction"

  1. 1. Everything Fake and How to Find Out Dr. Augustine Fou http://linkd.in/augustinefou April 2013 -1- Augustine Fou
  2. 2. Display Ads Fake Pageviews/Impressions -2- Augustine Fou
  3. 3. Percent of Web Traffic From Bots Source: Solve Media via Marketing Charts April 26, 2013 -3- Augustine Fou
  4. 4. Hidden Layers and Impressions Ads are hidden in up to 72 layers Entire web pages are loaded into ad iframes to boost impressions Multiple redirects and auto page refreshes Source: Spider.io May 2, 2013 -4- Augustine Fou
  5. 5. Bot Traffic - Web vs Mobile Source: Solve Media via Marketing Charts April 26, 2013 -5- Augustine Fou
  6. 6. “Suspect” Publishers Called out by Mike Shields, “Meet the Most Suspect Publishers on the WebThe rise of ghost sites, where traffic is huge but humans are few.” AdWeek, March 19, 2013 75% audience overlap with other sites from the same publisher American Express Ad -6- Augustine Fou
  7. 7. Bad Guys Rob Display Advertisers 1 2 3 4 6 5 7 • Pages are auto generated by script to optimize for high value search keywords and content • 10 – 15 display ads per page plus text ads and videos ads, in rotation • Advertisers should minimize ad dollars spent on impression (CPM) basis and focus on paying only when they get the click (CPC) • They also auto-refresh pages to load another 10 – 15 ads 8 9 10 • Many other examples of display ads shown next to unsavory content Source: http://www.satelliteguys.us/archive/t-232266.html -7- Augustine Fou
  8. 8. Search Ads Fake Clicks -8- Augustine Fou
  9. 9. Click Fraud % of PPC Impressions Source: ClickForensics 2006 - 2010 -9- Augustine Fou
  10. 10. Botnets cause most Click Fraud 42% of click fraud caused by botnets Source: ClickForensics 2009 - 10 - Augustine Fou
  11. 11. Bad Guys Do Way Better SEO For expensive items like the Vizio 60 inch HDTV, bad guys appeared in 8 of the 10 search results on page 1 of Google. They optimized for the exact model number: Vizio E601i-A3 - 11 - Augustine Fou
  12. 12. Bad Guys Kick Pharma in SEO 6 of the 10 page 1 results are bad guys websites, siphoning traffic from the brand website and legitimate, approved sources. Only drugs.com, wikipedia, medicinenet, and the brand website showed up as alteratives on page 1. - 12 - Augustine Fou
  13. 13. Facebook Fake Profiles Fake Likes - 13 - Augustine Fou
  14. 14. Fake Profiles on Facebook plagiarized or stock image - 14 - Augustine Fou
  15. 15. Bot generated pages on Facebook using many of the same images and randomized content - 15 - Augustine Fou
  16. 16. Fake Likes on Facebook How much do you like courgettes, the green vegetable Americans call zucchini? According to one Facebook page devoted to them, hundreds of people find them delightful enough to click the "like" button – even with dozens of other pages about courgettes to choose from. There's just one problem: the liking was fake, done by a team of low-paid workers in Dhaka, Bangladesh, whose boss demanded just $15 per thousand "likes" at his "click farm". Workers punching the keys might be on a three-shift system, and be paid as little as $120 a year. Source: Business Insider, August 2, 2013 - 16 - Augustine Fou
  17. 17. UPDATE: Feb 10, 2014 Here's how Muller says he knows the Likes are fake: Rather than being distributed across the engagement chart, all the countries with click farms fall to the bottom of the scale, indicating that those are followers who aren't receptive to the page's messaging. Those countries inside that red circle include (from the bottom): Egypt, India, the Philippines, Pakistan, Bangladesh, Indonesia, Nepal and Sri Lanka, all countries where click farms are common. - 17 - This blogger paid Facebook to promote his page. He got 80,000 bogus Likes instead. Source: Washington Post, Feb 10, 2014 Augustine Fou
  18. 18. Affiliate Links Cookie Stuffing - 18 - Augustine Fou
  19. 19. Fake Page w/ Affiliate Links Characteristics • Auto-generated by bots, stuffed with search keywords • Attract organic search traffic • Not human readable • Stuffed with affiliate links and ads - 19 - Augustine Fou
  20. 20. Man Defrauds eBay of $5.6M Using “cookie stuffing” techniques, the company planted affiliate cookies which led to revenue share payouts from eBay, amounting to $5.6M in 2006-07. This means their scheme cleared over $100M in transactions (assuming an average of around 5% revenue share payout). Source: LA Times April 19, 2013 - 20 - Augustine Fou
  21. 21. Twitter Fake Accounts/Malware Tweets - 21 - Augustine Fou
  22. 22. Buying Followers on Twitter - 22 - Augustine Fou
  23. 23. Most Fake Followers - 23 - Augustine Fou
  24. 24. YouTube Fake Views - 24 - Augustine Fou
  25. 25. Fake Views on YouTube - 25 - Augustine Fou
  26. 26. LinkedIn Fake Profiles - 26 - Augustine Fou
  27. 27. Fake Profiles on LinkedIn stock photo bot generated content - 27 - Augustine Fou
  28. 28. Email/Spam - 28 - Augustine Fou
  29. 29. Phishing Email/Malware Site - 29 - Augustine Fou
  30. 30. Phishing / Spear Phishing - 30 - Augustine Fou
  31. 31. Fake Sweepstakes Users enter email address and other personal info to enter sweepstakes. - 31 - Augustine Fou
  32. 32. Phishing for Personal Info Source: Gizmodo, July 14, 2013 - 32 - Augustine Fou
  33. 33. Fbishing is the new Phishing Beware of fake Facebook profiles asking to be friends with you. This is the new “phishing” that I am dubbing “fbishing” (pronounced “fuh-bishing”) Clues to look out for are - they have no friends, except you - they have no posts, photos or other activity - the photo is of a hot girl (or guy) in a bikini Once they are your friend, even your tight Facebook privacy settings like “friends only” wont stop them from seeing your entire list of friends (who become the next round of fbishing attempts). Source: go-Digital Blog - Fbishing - 33 - Augustine Fou
  34. 34. Electronic Frontier Foundation “If we ask whether a fact about a person identifies that person, it turns out that the answer isn't simply yes or no. If all I know about a person is their ZIP code, I don't know who they are. If all I know is their date of birth, I don't know who they are. If all I know is their gender, I don't know who they are. But it turns out that if I know these three things about a person, I could probably deduce their identity! Each of the facts is partially identifying.” https://www.eff.org/deeplinks/2010/01/primer-information-theory-and-privacy - 34 - Augustine Fou
  35. 35. Malware Sites - 35 - Augustine Fou
  36. 36. 60,000 Malware Sites/week Detected per week Source: Google Safe Browsing Initiative, Report - 36 - Augustine Fou
  37. 37. Blog Comment Spam - 37 - Augustine Fou
  38. 38. Automated Comment Spam Providing linkbacks to malware websites - 38 - Augustine Fou
  39. 39. Inserting Web Pages Into Browser History - 39 - Augustine Fou
  40. 40. Page of Ads Inserted in History For example, when users click on a web page from the Google search results, but don't find what they were looking for on that page, normally they would click the back button and return to Google's search results. However, some users are instead being taken to a spoof search results page that is actually entirely advertisements, such as this: Source: Search Engine Watch, July 17, 2013 - 40 - Augustine Fou
  41. 41. Related Articles Bad Guys Happily Rob Display Advertisers By: Augustine Fou, July 23, 2012 More on “cookie stuffing” http://bit.ly/12gnuPu Hackers Using Webcams to Spy By: Augustine Fou, July 22, 2013 Search Kicks Display in Effectiveness By: Augustine Fou, April 23, 2013 Many Forms of Online Fraud By: Augustine Fou, April 20, 2013 - 41 - Augustine Fou
  42. 42. Dr. Augustine Fou – Digital Consigliere “Users can customize their smartphone precisely, by downloading only the apps they want and need; no longer are they forced to by monolithic desktop PC software, 95% of the features of which they never use.” FORMER CHIEF DIGITAL OFFICER, HCG (OMNICOM) MCKINSEY CONSULTANT CLIENT SIDE / AGENCY SIDE EXPERIENCE PROFESSOR AND COLUMNIST ENTREPRENEUR / SMALL BUSINESS OWNER PHD MATERIALS SCIENCE (MIT '95) AT AGE 23 ClickZ Articles: http://bit.ly/augustine-fou-clickz Slideshares: http://bit.ly/augustine-fou-slideshares LinkedIn: http://linkd.in/augustinefou - 42 - @acfou Augustine Fou
  43. 43. APPENDIX - 43 - Augustine Fou
  44. 44. Click Fraud % of PPC Impressions Source: ClickForensics 2006 - 2010 - 44 - Augustine Fou

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