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Birds, Bots and Machines - fraud in Twitter and machine learning

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Birds, Bots and Machines - fraud in Twitter and machine learning

  1. 1. Birds, bots and machines: Detecting fraud in Twitter using Machine LearningVicente DíazSenior Security Analyst,Global Research and Analysis
  2. 2. Expectations vs reality
  3. 3. Why Twitter?
  4. 4. Spam - email90.0080.0070.0060.0050.0040.0030.0020.0010.00 0.00
  5. 5. Using hacked accounts
  6. 6. Using hacked accounts
  7. 7. Anything else interesting? #PalabrasNeciasMovistarSorda
  8. 8. Anything else interesting? #PalabrasNeciasMovistarSorda
  9. 9. Getting profiles
  10. 10. Getting profiles
  11. 11. Getting profiles
  12. 12. A random campaign
  13. 13. Lifespan of bots
  14. 14. Detour – A few words on privacy
  15. 15. Tracking
  16. 16. Identify the user: Advanced tracking Passive data: headers, plugins, browser, OS JS: screen resolution, custom resource detection via Plugins API (i.e. printers via PDF, fonts via Flash, etc.)Track ID Cookies, Flash cookies (allow cross-domain references), HTML5 storage, silverlight Java: own download cache, applets can read embedded resource streamsFuture? Apps and games in social networks.
  17. 17. Let´s play
  18. 18. Experiment • 3 months of tracking• 36 malicious campaigns • 13,490 profiles • 195,801 tweets• 6,519,247 relationships
  19. 19. Machine Learning in 60 seconds• Supervised learning• Training – adaptative models• Classification• Key: choose the right attributes
  20. 20. Machine Learning in 60 seconds• Supervised learning• Training – adaptative models• Classification• Key: choose the right attributes
  21. 21. Twitter Feature selection username coordinates Derived description profileImg lang• Curse of dimensionality followingCount followersCount url meanTimeBetweenTweets createdAt• No new knowledge is generated: choose the tweetsCount fullName friendFollowerRatio timeZone tweetsKnownRecv verified right features! following followers tweetsUnknownRecv percFollowingFollowers numberOfProfile Tweets percProfileTweetsWithLink protected percProfileTweetsToSomeone text percProfileTweetsRT possiblySensitive source numberOfViasUsed location
  22. 22. Mean time between tweets
  23. 23. Tweets to someone
  24. 24. Tweets to someone After some testing and feature-selection algorithms:numberOfViastweetsToSomeonetweetsWithLinkfollowingFollowersfriendFollowerRatiotweetsKnownReceivertweetsUnknownReceiver
  25. 25. Avoiding detection You are doing it wrong!
  26. 26. Avoiding semantic analysis• if its do you me your my do it my be find is but on are its rt that was• I a me at get out your they on rt if I get rt can a• u you rt find in I that that your my my find one you so is is my you this but get all a one its it• they with its your get me of I
  27. 27. Avoiding relationship checks
  28. 28. Avoiding relationship checks Or just overflow with fake profiles …
  29. 29. DIY
  30. 30. Finding malicious profiles• Not so hard …
  31. 31. 1 week later…AdrianaDickson7MyrtleTerry11PatricaFitzpat6RobertP97792514RochelleBeasle8 5200 profiles in this campaignShannonMunoz13 Around 250 new profiles created every day
  32. 32. Following180160140120100 80 Following 60 40 20 0 0 50 100 150 200 250 300
  33. 33. Followers200150100 Followers 50 0 0 50 100 150 200 250 300
  34. 34. Top tweets sent• Mmmm hot chocolate with cream• Beyonce looks so hot in her new ad 1800 different tweets• So Hot• Spain !! Too hot• hot summer• a hot bubble bath is much needed• Tea water supposed to hot ya now• Air conditioner-laying on the bed-naked-relax-heaven! So hot tonight!• playing piano and guitar r the only things i can do right in life does this make me hot enough for a boyfriend yet</p• Austin mahone is just like another justin beiber..he is hot tho!
  35. 35. Top tweets sent• Mmmm hot chocolate with cream• Beyonce looks so hot in her new ad 1800 different tweets• So Hot• Spain !! Too hot• hot summer• a hot bubble bath is much needed• Tea water supposed to hot ya now• Air conditioner-laying on the bed-naked-relax-heaven! So hot tonight!• playing piano and guitar r the only things i can do right in life does this make me hot enough for a boyfriend yet</p• Austin mahone is just like another justin beiber..he is hot tho!
  36. 36. Not only limited to Twitter
  37. 37. Not only limited to Twitter
  38. 38. Not only limited to Twitter
  39. 39. Conclusions It is relatively easy to find anomaliesBots are there for different reasons, mostly fraud-related Machine learning: lots of resources!
  40. 40. Conclusions It is relatively easy to find anomaliesBots are there for different reasons, mostly fraud-related Machine learning: lots of resources!
  41. 41. Conclusions It is relatively easy to find anomaliesBots are there for different reasons, mostly fraud-related Machine learning: lots of resources!
  42. 42. Conclusions It is relatively easy to find anomaliesBots are there for different reasons, mostly fraud-related Machine learning: lots of resources!
  43. 43. Thank you Questions?Vicente Díaz @trompiSenior Security Analyst,Global Research and Analysis

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