OSINT using Twitter & Python
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OSINT using Twitter & Python






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OSINT using Twitter & Python OSINT using Twitter & Python Presentation Transcript

  • OSINT FootprintingUsing Twitter and Python
  • Who am I? Raymond Lilly @37point2 Analyst at a Social Media/Customer Relations Management company Senior, Eastern Michigan University Information Assurance/Network Security
  • What are we talking about? OSINT gathering methods Research with implications in  Intelligence  Social Engineering  Marketing
  • Intelligence What are people talking about? Intel vs Counter Intel Targeting concerns Individuals/Groups Geographic regions Time Topics
  • Social EngineeringLeaking information What do your co-workers/employees talk about during/after work? IT talking about new tech deployments? Any employees venting about internal issues? C levels discussing personal hobbies/travel plans?
  • Marketing Can you identify your customers? What are they talking about?/What other interests do they have? Can you profile them and use that to reach new potential customers? Find new markets? Reduce your customer assistance cost or increase customer satisfaction?
  • Fun Stuff New Job info  What’s the corporate culture like?  Does the company embrace new tech/ideas or shun them? Amplify the reach of your messages Find organizations/groups that are interested in the same things you are
  • Key Twitter Concepts Tweets – 140 characters Following  Friends  Followers Did you pick the user?
  • Followers A -> B
  • Friends B -> C
  • A -> B -> C
  • Twitter’s API https://dev.twitter.com/docs/api Authenticated vs. Unauthenticated  How hard is it to get OAuth Tokens? REST Streaming
  • Tweepy! Python module for Twitter’s API https://github.com/tweepy/tweepy/ Joshthecoder
  • GET status/user_timeline Takes a user_id or screen_name since_id count exclude_replies include_rts Tweepy.api.get_status(‘37point2’)
  • GET users/show user_id/screen_name include_entities  ^-- Awesome! Tweepy.api.get_user(‘37point2’)
  •  "id": 286868576, "id_str": "286868576", "name": "37point2", "screen_name": "37point2", "location": "", "description": "Information Assurance student at Eastern Michigan University. rnIntel Analysis, Data Viz, Incident Response", "url": "http://www.linkedin.com/in/raymondlilly", "protected": false, "followers_count": 244, "friends_count": 992, "listed_count": 6, "created_at": "Sat Apr 23 21:25:44 +0000 2011", "utc_offset": -18000, "time_zone": "Eastern Time (US & Canada)",
  • "description": "Information Assurancestudent at Eastern Michigan University.rnIntel Analysis, Data Viz, IncidentResponse",
  • Method to the Madness Information Needed/Gathered Tools used Visualization Analysis
  • Echo Chamber Last 1000 Tweets of everyone followed Basic Word Count Wordle.net
  • Tweets per Day Individual  Last 3200 Tweets Community  Last 1000 Tweets (#infosec – May 18-21) Plot Tweets over weekdays
  • Hashtags/Topics Last 3200 Tweets  include_entities! #lazyhacker  include_rts Google Visualization API  Hashtags & HashtagsWithRetweets
  • Retweets/Replies Last 3200 Tweets  include_entities  include_rts!!! Retweets Replies Best time for a response?
  • Interactions w/ Influence andTopics Klout  BOO!!!  Changes algorithm daily  What is the algorithm? /shrug  Weights social media sites differently  Useful  Topics!!  Score used as guideline
  • Model Last 3200 Tweets Include all the things! Add Klout score and topics
  • Formula for Influence Klout^2 * interactions 50 vs 60 2500 vs 3600
  • Time to get interactive!
  • Clients Last 3200 Tweets Total Counts Client usage over time
  • More moving stuff!
  • Interactions & Topics Last 3200 Tweets include_entities Maltego CaseFile  Community Edition
  • Interesting Tools Tweetstats.com Twopcharts Klout Kred Socialmention NetworkX
  • Contact Info Raymond Lilly @37point2 rlilly@emich.edu