HoneySpam 2'0 Profiling Web Spambot Behaviour

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HoneySpam 2.0Profiling Web Spambot Behaviour
Prof. Tharam Dillon Prof. Elizabeth
Chang Digital Ecosystem and Business
Intelligence Institute (DEBII)

• Pedram Hayati
• Kevin Chai
• Vidyasagar Potdar
• Alex Talevsky

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Agenda
Agenda

• Introduction
• Background
• Taxonomy of Spam 2.0 and Web Spambot
• Current Literature Techniques
• HoneySpam 2.0 Architecture
• Navigation Component
• Form Tracking Component
• Deploying HoneySpam 2.0
• Experimental Results
• Related Works
• Conclusion and future works

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Little bit whats going on?
Web 2.0
Spam 2.0
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Web Spambot

• A kind of Web Robot or Internet Robot
• Distribute Spam content in Web 2.0 applications
• Scope
• Application-Specific
• Website-Specific

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Countermeasures
Web 2.0 Submission Workflow

• CAPTCHA
• HashCash
• Form variation
• Nonce

• Decrease user convenience and increase complexity
  of human computer interaction.
• As programs become better at deciphering CAPTCHA,
  the image may become difficult for humans to
  decipher.
• As computers get more powerful, they will be able
  to decipher CAPTCHA better than humans.

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HoneySpam 2.0

• Monitor and Track Web Spambots
• Idea of Honeypots
• Implicitly Track
• Click-steam
• Page navigation
• Keyboard activity
• Mouse movement
• Page Scrolling

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HoneySpam 2.0
HoneySpam 2.0 Architecture
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HoneySpam 2.0 in Action!
of Origin of WebSpam Bots
of Content Contribution
of Browser Type
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HoneySpam 2.0 in Action!
No. of Posts vs. Date No. of Users vs. Date No.
of Online Users vs. Date
No. of SpamBot vs. Hits
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HoneySpam 2.0 in Action!
No. Session vs. Dwell Visit Time
No. of Spambots Vs. Return Visits
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Web Spambot Behaviour

• Use of search engines to find target websites
• Create numerous user accounts
• Low website webpage hits and revisit rates
• Distribute spam content in a short period of time
• No web form interaction
• Generated usernames

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Conclusion

• HoneySpam 2.0 as framework to monitor/track Web
  spambot behaviour
• Integrated to popular open source web
  applications
• Web Spambots
• use search engines to find target websites,
• create numerous user accounts,
• distribute spam content in a short amount of
  time,
• do not revisit the website,
• do not interact with forms on the website,
• and register with randomly generated usernames

Future Work Using of Machine Learning, Neural
Network (SOM), extract features to do the
classification
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Thank You!
debii.curtin.edu.au
www.curtin.edu.au
asrl.debii.curtin.edu.au www.antispamresearchlab.c
om
HoneySpam 2.0Profiling Web Spambot Behaviour
Pedram Hayati, Kevin Chai, Vidyasagar Potdar,
Alex Talevsky
Homepage debii.curtin.edu.au/pedram/