An empirical reexamination of global DNS behavior

  • Authors:

  • Hongyu Gao;Vinod Yegneswaran;Yan Chen;Phillip Porras;Shalini Ghosh;Jian Jiang;Haixin Duan

  • Affiliations:

  • Northwestern University, Evanston, IL, USA;SRI International, Menlo Park, CA, USA;Northwestern University, Evanston, IL, USA;SRI International, Menlo Park, CA, USA;SRI International, Menlo Park, CA, USA;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China

  • Venue:
  • Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM
  • Year:
  • 2013

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Abstract

The performance and operational characteristics of the DNS protocol are of deep interest to the research and network operations community. In this paper, we present measurement results from a unique dataset containing more than 26 billion DNS query-response pairs collected from more than 600 globally distributed recursive DNS resolvers. We use this dataset to reaffirm findings in published work and notice some significant differences that could be attributed both to the evolving nature of DNS traffic and to our differing perspective. For example, we find that although characteristics of DNS traffic vary greatly across networks, the resolvers within an organization tend to exhibit similar behavior. We further find that more than 50% of DNS queries issued to root servers do not return successful answers, and that the primary cause of lookup failures at root servers is malformed queries with invalid TLDs. Furthermore, we propose a novel approach that detects malicious domain groups using temporal correlation in DNS queries. Our approach requires no comprehensive labeled training set, which can be difficult to build in practice. Instead, it uses a known malicious domain as anchor, and identifies the set of previously unknown malicious domains that are related to the anchor domain. Experimental results illustrate the viability of this approach, i.e. , we attain a true positive rate of more than 96%, and each malicious anchor domain results in a malware domain group with more than 53 previously unknown malicious domains on average.