skip to main content
10.1145/3055186.3055192acmconferencesArticle/Chapter ViewAbstractPublication Pagesasia-ccsConference Proceedingsconference-collections
research-article

SIPHON: Towards Scalable High-Interaction Physical Honeypots

Authors Info & Claims
Published:02 April 2017Publication History

ABSTRACT

In recent years, the emerging Internet-of-Things (IoT) has led to rising concerns about the security of networked embedded devices. In this work, we propose the SIPHON architecture---a Scalable high-Interaction Honeypot platform for IoT devices. Our architecture leverages IoT devices that are physically at one location and are connected to the Internet through so-called \emph{wormholes} distributed around the world. The resulting architecture allows exposing few physical devices over a large number of geographically distributed IP addresses. We demonstrate the proposed architecture in a large scale experiment with 39 wormhole instances in 16 cities in 9 countries. Based on this setup, five physical IP cameras, one NVR and one IP printer are presented as 85 real IoT devices on the Internet, attracting a daily traffic of 700MB for a period of two months. A preliminary analysis of the collected traffic indicates that devices in some cities attracted significantly more traffic than others (ranging from 600 000 incoming TCP connections for the most popular destination to less than 50 000 for the least popular). We recorded over 400 brute-force login attempts to the web-interface of our devices using a total of 1826 distinct credentials, from which 11 attempts were successful. Moreover, we noted login attempts to Telnet and SSH ports some of which used credentials found in the recently disclosed Mirai malware.

References

  1. DLink dcs-930l camera vulnerability. http://securityaffairs.co/wordpress/49143/breaking-news/d-link.html. Accessed: 2016-08--10.Google ScholarGoogle Scholar
  2. Masscan the internet port scanner. http://tools.kali.org/information-gathering/masscan. Accessed: 2016-08--10.Google ScholarGoogle Scholar
  3. 2005. ITU report : The Internet of Things.Google ScholarGoogle Scholar
  4. The DecoyPort: Redirecting Hackers to Honeypots. Springer Berlin Heidelberg, September 2007.Google ScholarGoogle Scholar
  5. 2016. Gartner report Forecast: IoT Security, Worldwide.Google ScholarGoogle Scholar
  6. 2016. IDC report Internet of Things: Security Practices.Google ScholarGoogle Scholar
  7. Eric Alata, Vincent Nicomette, Marc Dacier, Matthieu Herrb, et al. Lessons learned from the deployment of a high-interaction honeypot. arXiv preprint arXiv:0704.0858, 2007.Google ScholarGoogle Scholar
  8. Eugene Albin. A comparative analysis of the snort and suricata intrusion-detection systems. PhD thesis, Naval Postgraduate School, CA, USA, 2011.Google ScholarGoogle Scholar
  9. E. Androulaki, C. Soriente, L. Malisa, and S. Capkun. Enforcing location and time-based access control on cloud-stored data. In Proceedings of Conference on Distributed Computing Systems (ICDCS), June 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Roland Bodenheim, Jonathan Butts, Stephen Dunlap, and Barry Mullins. Evaluation of the ability of the shodan search engine to identify internet-facing industrial control devices. International Journal of Critical Infrastructure Protection, 7(2):114--123, 2014. Google ScholarGoogle ScholarCross RefCross Ref
  11. Davide Canali and Davide Balzarotti. Behind the scenes of online attacks: an analysis of exploitation behaviors on the web. In 20th Annual Network & Distributed System Security Symposium (NDSS), 2013.Google ScholarGoogle Scholar
  12. Dyn attack 2016. http://dyn.com/blog/dyn-analysis-summary-of-friday-october-21-attack/. Accessed: 2016--12-06.Google ScholarGoogle Scholar
  13. Wenjun Fan, Zhihui Du, and David Fernández. Taxonomy of honeynet solutions. In SAI Intelligent Systems Conference (IntelliSys), 2015, pages 1002--1009. IEEE, 2015. Google ScholarGoogle ScholarCross RefCross Ref
  14. Aurélien Francillon, Boris Danev, and Srdjan Capkun. Relay attacks on passive keyless entry and start systems in modern cars. In Proc. Network and Distributed System Security Symp. (NDSS), 2011.Google ScholarGoogle Scholar
  15. Julian B. Grizzard, Sven Krasser, and Henry L. Owen. The Use of Honeynets to Increase Computer Network Security and User Awareness. Journal of Security Education, 1(2--3):23--37, 2005.Google ScholarGoogle Scholar
  16. M. Guri, Y. Poliak, B. Shapira, and Y. Elovici. JoKER: Trusted detection of kernel rootkits in android devices via JTAG interface. In Proceedings of Trustcom, volume 1, pages 65--73, Aug 2015.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Philip Hane. IPWHOIS: A library for RDAP (HTTP) lookups. https://pypi.python.org/pypi/ipwhois, 2015.Google ScholarGoogle Scholar
  18. Thorsten Holz, Markus Engelberth, and Felix Freiling. Learning more about the underground economy: A case-study of keyloggers and dropzones. In European Symposium on Research in Computer Security, pages 1--18. Springer, 2009. Google ScholarGoogle ScholarCross RefCross Ref
  19. Y-C Hu, Adrian Perrig, and David B Johnson. Packet leashes: a defense against wormhole attacks in wireless networks. In Proc. of the IEEE Conference on Computer Communication (InfoCom), volume 3, pages 1976--1986. IEEE, 2003.Google ScholarGoogle ScholarCross RefCross Ref
  20. Yih-Chun Hu, Adrian Perrig, and David B Johnson. Wormhole detection in wireless ad hoc networks. Technical Report Tech. Rep. TR01--384, Department of Computer Science, Rice University, 2002.Google ScholarGoogle Scholar
  21. Yih-Chun Hu, Adrian Perrig, and David B Johnson. Wormhole attacks in wireless networks. IEEE journal on selected areas in communications, 24(2):370--380, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Miyoung Kim, Misun Kim, and Youngsong Mun. Design and implementation of the honeypot system with focusing on the session redirection. In International Conference on Computational Science and Its Applications, pages 262--269. Springer, 2004. Google ScholarGoogle ScholarCross RefCross Ref
  23. I Kotuliak, P Rybár, and P Trúchly. Performance comparison of ipsec and tls based vpn technologies. In Proceedings of Conference on Emerging eLearning Technologies and Applications (ICETA), pages 217--221. IEEE, 2011. Google ScholarGoogle ScholarCross RefCross Ref
  24. Q. D. La, T. Quek, J. Lee, S. Jin, and H. Zhu. Deceptive attack and defense game in honeypot-enabled networks for the internet of things. IEEE Internet of Things Journal, PP(99):1--1, 2016.Google ScholarGoogle Scholar
  25. John C Matherly. SHODAN the computer search engine. https://www.shodan.io. Accessed: 2016-08-01.Google ScholarGoogle Scholar
  26. Mirai malware 2016. http://blog.malwaremustdie.org/2016/08/mmd-0056--2016-linuxmirai-just.html. Accessed: 2016--12-04.Google ScholarGoogle Scholar
  27. Yin Minn Pa Pa, Shogo Suzuki, Katsunari Yoshioka, Tsutomu Matsumoto, Takahiro Kasama, and Christian Rossow. IoTPOT: Analysing the Rise of IoT Compromises. In 9th USENIX Workshop on Offensive Technologies (WOOT). USENIX Association, 2015.Google ScholarGoogle Scholar
  28. Radek Píbil, Viliam Lisý, Christopher Kiekintveld, Branislav Bosanský, and Michal Pechoucek. Game Theoretic Model of Strategic Honeypot Selection in Computer Networks, pages 201--220. Springer Berlin Heidelberg, November 2012.Google ScholarGoogle Scholar
  29. F Pouget, M Dacier, and VH Pham. on the advantages of deploying a large scale distributed honeypot platform. In Proceedings of the E-Crime and Computer Evidence Conference, 2005.Google ScholarGoogle Scholar
  30. Niels Provos. A virtual honeypot framework. In Proc. of the USENIX Security Symposium, 2004.Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Niels Provos and Thorsten Holz. Virtual honeypots: from botnet tracking to intrusion detection. Pearson Education, 2007.Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Shachar Siboni, Asaf Shabtai, Nils Ole Tippenhauer, Jemin Lee, and Yuval Elovici. Advanced security testbed framework for wearable iot devices. ACM Transactions on Internet Technology (TOIT), 16(4):26, 2016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Lance Spitzner. The honeynet project: Trapping the hackers. IEEE Security & Privacy, 1(2):15--23, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Weizhe Zhang and Baosheng Qu. Security architecture of the internet of things oriented to perceptual layer. International Journal on Computer, Consumer and Control (IJ3C), 2(2):37--45, 2013.Google ScholarGoogle Scholar

Index Terms

  1. SIPHON: Towards Scalable High-Interaction Physical Honeypots

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Conferences
          CPSS '17: Proceedings of the 3rd ACM Workshop on Cyber-Physical System Security
          April 2017
          120 pages
          ISBN:9781450349567
          DOI:10.1145/3055186

          Copyright © 2017 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 2 April 2017

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article

          Acceptance Rates

          CPSS '17 Paper Acceptance Rate10of35submissions,29%Overall Acceptance Rate33of113submissions,29%

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader