OSINT using Twitter & Python
Upcoming SlideShare
Loading in...5

OSINT using Twitter & Python






Total Views
Views on SlideShare
Embed Views



3 Embeds 13

https://twitter.com 11
http://kred.com 1
https://web.tweetdeck.com 1



Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
Post Comment
Edit your comment

    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