Skip to main content

Honeynet-based Botnet Scan Traffic Analysis

  • Chapter
Botnet Detection

Part of the book series: Advances in Information Security ((ADIS,volume 36))

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. M. Bailey et al. The Internet motion sensor: A distributed blackhole monitoring system. In Proc. of NDSS, 2005.

    Google Scholar 

  2. James Binkley and Suresh Singh. An algorithm for anomaly-based botnet detection. In Proceedings of Steps to Reducing Unwanted Traffic on the Internet Workshop (SRUTI ’06), 2006.

    Google Scholar 

  3. E. Cooke, F. Jahanian, and D. McPherson. The zombie roundup: Understanding, detecting, and disrupting botnets. In Proceedings of USENIX Workshop on Steps to Reducing Unwanted Traffic on the Internet, July 2005.

    Google Scholar 

  4. W. Cui et al. GQ: Realizing a system to catch worms in a quarter million places. Technical Report TR-06-004, ICSI, 2006.

    Google Scholar 

  5. D. Dagon, C. Zou, and W. Lee. Modeling botnet propagation using time zones. In Proceedings of the 13th Network and Distributed System Security Symposium (NDSS’06), 2006.

    Google Scholar 

  6. Neil Daswani, Michael Stoppelman, the Google Click Quality, and Inc Security Teams, Google. The anatomy of Clickbot.A. In USENIX First Workshop on Hot Topics in Understanding Botnets (HotBots), 2007.

    Google Scholar 

  7. Julian Grizzard, Vikram Sharma, Chris Nunnery, Brent ByungHoon Kang, and David Dagon. Peer-to-peer botnets: Overview and case study. In USENIX First Workshop on Hot Topics in Understanding Botnets (HotBots), 2007.

    Google Scholar 

  8. Christopher W. Hanna. Using snort to detect rogue irc bot programs, Oct 2004. http://www.giac.org/certified_professionals/practicals/gsec/4095.php.

    Google Scholar 

  9. J. Kannan et al. Semi-automated discovery of application session structure. In Proc. of ACM IMC, 2006.

    Google Scholar 

  10. R. Pang et al. Characteristics of Internet background radiation. In Proc. of ACM IMC, 2004.

    Google Scholar 

  11. V. Paxson. Bro: A system for detecting network intruders in real-time. Computer Networks, 31, 1999.

    Google Scholar 

  12. The Honeynet Project and Research Alliance. Know your enemy: Tracking botnets. http://honeynet.org/papers/bots, March 2005.

    Google Scholar 

  13. N. Provos. A virtual honeypot framework. In Proc. of USENIX Security, 2004.

    Google Scholar 

  14. Niels Provos, Dean McNamee, Panayiotis Mavrommatis, Ke Wang, and Nagendra Modadugu. The ghost in the browser: Analysis of web-based malware. In USENIX First Workshop on Hot Topics in Understanding Botnets (HotBots), 2007.

    Google Scholar 

  15. Moheeb A. Rajab, Jay Zarfoss, Fabian Monrose, and Andreas Terzis. A multifaceted approach to understanding the botnet phenomenon. In Proc. of ACM/USENIX IMC, 2006.

    Google Scholar 

  16. Anirudh Ramachandran and Nick Feamster. Understanding the network-level behavior of spammers. In Proceedings of ACM SIGCOMM ’06, September 2006.

    Google Scholar 

  17. Anirudh Ramachandran, Nick Feamster, and David Dagon. Revealing botnet membership using DNSBL counter-intelligence. In Proceedings of Steps to Reducing Unwanted Traffic on the Internet Workshop (SRUTI ’06), 2006.

    Google Scholar 

  18. M. Vrable et al. Scalability, fidelity, and containment in the potemkin virtual honeyfarm. In Proc. of SOSP, 2005.

    Google Scholar 

  19. V. Yegneswaran, Paul Barford, and Vern Paxson. Using honeynets for Internet situational awareness. In In Proc. of ACM Hotnets IV, 2005.

    Google Scholar 

  20. V. Yegneswaran et al. On the design and use of Internet sinks for network abuse monitoring. In Proc. of RAID, 2004.

    Google Scholar 

  21. Michal Zalewski. the new p0f. http://lcamtuf.coredump.cx/p0f.shtml.

    Google Scholar 

  22. Cliff C. Zou et al. Monitoring and early warning for Internet worms. In Prof. of ACM CCS, 2003.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Li, Z., Goyal, A., Chen, Y. (2008). Honeynet-based Botnet Scan Traffic Analysis. In: Lee, W., Wang, C., Dagon, D. (eds) Botnet Detection. Advances in Information Security, vol 36. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-68768-1_2

Download citation

  • DOI: https://doi.org/10.1007/978-0-387-68768-1_2

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-68766-7

  • Online ISBN: 978-0-387-68768-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics