skip to main content
research-article

Exposure: A Passive DNS Analysis Service to Detect and Report Malicious Domains

Published:01 April 2014Publication History
Skip Abstract Section

Abstract

A wide range of malicious activities rely on the domain name service (DNS) to manage their large, distributed networks of infected machines. As a consequence, the monitoring and analysis of DNS queries has recently been proposed as one of the most promising techniques to detect and blacklist domains involved in malicious activities (e.g., phishing, spam, botnets command-and-control, etc.). EXPOSURE is a system we designed to detect such domains in real time, by applying 15 unique features grouped in four categories.

We conducted a controlled experiment with a large, real-world dataset consisting of billions of DNS requests. The extremely positive results obtained in the tests convinced us to implement our techniques and deploy it as a free, online service. In this article, we present the Exposure system and describe the results and lessons learned from 17 months of its operation. Over this amount of time, the service detected over 100K malicious domains. The statistics about the time of usage, number of queries, and target IP addresses of each domain are also published on a daily basis on the service Web page.

References

  1. Alexa. 2009. Alexa web information company. http://www.alexa.com/topsites/.Google ScholarGoogle Scholar
  2. Amini, B. 2008. Kraken botnet infiltration. http://dvlabs.tippingpoint.com/blog/2008/04/28/kraken-botnet-infiltration.Google ScholarGoogle Scholar
  3. Antonakakis, M., Perdisci, R., Dagon, D., Lee, W., and Feamster, N. 2010. Building a dynamic reputation system for DNS. In Proceedings of the 19th Usenix Security Symposium. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Antonakakis, M., Perdisci, R., Lee, W., Vasiloglou, N., and Dagon, D. 2011. Detecting malware domains at the upper DNS hierarchy. In Proceedings of the 20th Usenix Security Symposium. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Antonakakis, M., Perdisci, R., Nadji, Y., Vasiloglou, N., Abu-Nimeh, S., Lee, W., and Dagon, D. 2012. From throw-away traffic to bots: Detecting the rise of dga-based malware. In Proceedings of the 21st Usenix Security Symposium. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Basseville, M. and Nikiforov, I. V. 1993. Detection of Abrupt Changes - Theory and Application. Prentice-Hall. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Bayer, U., Kruegel, C., and Kirda, E. 2006. TTAnalyze: A tool for analyzing malware. In Proceedings of the 15th EICAR Conference.Google ScholarGoogle Scholar
  8. Berkhin, P. 2002. Survey of clustering data mining techniques. Tech. rep. http://www.cc.gatech.edu/~isbell/classes/reading/papers/berkhin02survey.pdf.Google ScholarGoogle Scholar
  9. Bilge, L., Kirda, E., Kruegel, C., and Balduzzi, M. 2011. Exposure: Finding malicious domains using passive DNS analysis. In Proceedings of the Annual Network and Distributed System Security Symposium (NDSS’11).Google ScholarGoogle Scholar
  10. Bradley, A. P. 1997. The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recogn. 30, 1145--1159. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Choi, H., Lee, H., and Kim, H. 2007. Botnet detection by monitoring group activities in DNS traffic. In Proceedings of the 7th IEEE International Conference on Computer and Information Technologies. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Chu, S., Keogh, E., Hart, D., Pazzani, M., and Michael. 2002. Iterative deepening dynamic time warping for time series. In Proceedings of the 2nd SIAM International Conference on Data Mining.Google ScholarGoogle ScholarCross RefCross Ref
  13. Cova, M. 2013. Wepawet. http://wepawet.iseclab.org/.Google ScholarGoogle Scholar
  14. DNS. 2010. DNSBL - Spam database lookup. http://www.dnsbl.info/.Google ScholarGoogle Scholar
  15. Domains, M. 2009. Malware domain block list. http://www.malwaredomains.com/.Google ScholarGoogle Scholar
  16. ECJ. 2012. ecj20: A java-based evolutionary computation research system. http://cs.gmu.edu/eclab/projects/ecj/.Google ScholarGoogle Scholar
  17. Felegyhazi, M., Kreibich, C., and Paxson, V. 2010. On the potential of proactive domain blacklisting. In Proceedings of the 3rd USENIX Workshop on Large-scale Exploits and Emergent Threats (LEET’10). Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Google. 2010. Google safe browsing. http://www.google.com/tools/firefox/safebrowsing/.Google ScholarGoogle Scholar
  19. Holz, T., Gorecki, C., Rieck, K., and Freiling, F. 2008. Measuring and detecting fast-flux service networks. In Proceedings of the Annual Network and Distributed System Security Symposium (NDSS’08).Google ScholarGoogle Scholar
  20. ISC. 2010. Internet systems consortium. https://sie.isc.org/.Google ScholarGoogle Scholar
  21. Keogh, E., Chakrabarti, K., Pazzani, M., and Mehrotra, S. 2001. Locally adaptive dimensionality reduction for indexing large time series databases. In Proceedings of the ACM SIGMOD Conference on Management of Data (SIGMOD’01). 151--162. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Konte, M., Feamster, N., and Jung, J. 2009. Dynamics of online scam hosting infrastructure. In Proceedings of the Passive and Active Measurement Conference. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. List, M. D. 2009a. Malware domains list. http://www.malwaredomainlist.com/mdl.php.Google ScholarGoogle Scholar
  24. List, Z. B. 2009b. Zeus domain blocklist. https://zeustracker.abuse.ch/blocklist.php?download=Domainblocklist.Google ScholarGoogle Scholar
  25. Ma, J., Saul, L. K., Savage, S., and Voelker, G. M. 2009. Beyond blacklists: Learning to detect malicious web sites from suspicious urls. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’09). 1245--1254. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. McAfee. 2010. McAfee siteadvisor. http://www.siteadvisor.com/.Google ScholarGoogle Scholar
  27. Nazario, J. and Holz, T. 2008. As the net churns: Fast-flux botnet observations. In Proceedings of the International Conference on Malicious and Unwanted Software.Google ScholarGoogle Scholar
  28. Norton. 2010. Norton safe web. http://safeweb.norton.com/.Google ScholarGoogle Scholar
  29. Open Graph. 2013. The open graph viz platform. https://gephi.org.Google ScholarGoogle Scholar
  30. Passerini, E., Paleari, R., Martignoni, L., and Bruschi, D. 2008. Fluxor: Detecting and monitoring fast-flux service networks. In Proceedings of the 5th International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment (DIMVA’08). Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Perdisci, R., Corona, I., Dagon, D., and Lee, W. 2009. Detecting malicious flux service networks through passive analysis of recursive dns traces. In Proceedings of the 25th Annual Computer Security Applications Conference (ACSAC’09). Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Phishtank. 2009. Phishtank. http://www.phishtank.com/.Google ScholarGoogle Scholar
  33. Porras, P., Saidi, H., and Yegneswaran, V. 2009. A foray into conficker’s logic and rendezvous points. In Proceedings of the USENIX Workshop on Large-Scale Exploits and Emergent Threats. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Quinlan, J. 1995. Learning with continuous classes. In Proceedings of the 5th Australian Joint Conference on Artificial Intelligence. World Scientific. 343--348.Google ScholarGoogle Scholar
  35. RFC. 1995. RFC 1794 - DNS support for load balancing. http://tools.ietf.org/html/rfc1794.Google ScholarGoogle Scholar
  36. RFC. 1996. RFC 1912 - Common dns operational and configuration errors. http://www.faqs.org/rfcs/rfc1912.html.Google ScholarGoogle Scholar
  37. Stone-Gross, B., Cova, M., Cavallaro, L., Gilbert, B., Szydlowski, M., Kemmerer, R., Kruegel, C., and Vigna, G. 2009. Your botnet is my botnet: Analysis of a botnet takeover. In Proceedings of the ACM Conference on Computer and Communication Security (CCS’09). Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Symantec. 2011. Symantec threat report. http://www.symantec.com/business/theme.jsp?themeid=threatreport.Google ScholarGoogle Scholar
  39. Theodoridis, S. and Koutroumbas, K. 2009. Pattern Recognition. Academic Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Turaga, D., Vlachos, M., and Verscheure, O. 2009. On k-means cluster preservation using quantization schemes. In Proceedings of the IEEE International Conference on Data Mining (ICDM’09). Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Villamarn-Salomn, R. and Brustoloni, J. C. 2009. Bayesian bot detection based on dns traffic similarity. In Proceedings of the ACM Symposium on Applied Computing (SAC’09). Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Weimer, F. 2005. Passive DNS replication. In Proceedings of the 1st Conference on Computer Security Incident.Google ScholarGoogle Scholar
  43. WHOIS. 1995. RFC1834 - Whois and network information lookup service, whois++. http://www.faqs.org/rfcs/rfc1834.html.Google ScholarGoogle Scholar
  44. Witten, I. and Frank, E. 2005. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Fransisco, CA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Wolf, J. 2008. Technical details of srizbis domain generation algorithm. http://tinyurl.com/6mdasc.Google ScholarGoogle Scholar
  46. Zdrnja, B., Brownlee, N., and Wessels, D. 2007. Passive monitoring of dns anomalies. In Proceedings of the 4th International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment (DIMVA’07). 129--139. Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. Zitouni, H., Sevil, S., Ozkan, D., and Duygulu, P. 2008. Re-ranking of image search results using a graph algorithm. In Proceedings of the 9th International Conference on Pattern Recognition.Google ScholarGoogle Scholar

Index Terms

  1. Exposure: A Passive DNS Analysis Service to Detect and Report Malicious Domains

    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

    Full Access

    • Published in

      cover image ACM Transactions on Information and System Security
      ACM Transactions on Information and System Security  Volume 16, Issue 4
      April 2014
      154 pages
      ISSN:1094-9224
      EISSN:1557-7406
      DOI:10.1145/2617317
      • Editor:
      • Gene Tsudik
      Issue’s Table of Contents

      Copyright © 2014 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: 1 April 2014
      • Accepted: 1 December 2013
      • Revised: 1 November 2013
      • Received: 1 January 2013
      Published in tissec Volume 16, Issue 4

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader