Mapping an Enterprise Network by Analyzing DNS Traffic

  • Minzhao LyuEmail author
  • Hassan Habibi Gharakheili
  • Craig Russell
  • Vijay Sivaraman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11419)


Enterprise networks are becoming more complex and dynamic, making it a challenge for network administrators to fully track what is potentially exposed to cyber attack. We develop an automated method to identify and classify organizational assets via analysis of just 0.1% of the enterprise traffic volume, specifically corresponding to DNS packets. We analyze live, real-time streams of DNS traffic from two organizations (a large University and a mid-sized Government Research Institute) to: (a) highlight how DNS query and response patterns differ between recursive resolvers, authoritative name servers, web-servers, and regular clients; (b) identify key attributes that can be extracted efficiently in real-time; and (c) develop an unsupervised machine learning model that can classify enterprise assets. Application of our method to the 10 Gbps live traffic streams from the two organizations yielded results that were verified by the respective IT departments, while also revealing new knowledge, attesting to the value provided by our automated system for mapping and tracking enterprise assets.


Enterprise network DNS analysis Machine learning 



This work was completed in collaboration with the Australian Defence Science and Technology Group.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Minzhao Lyu
    • 1
    • 2
    Email author
  • Hassan Habibi Gharakheili
    • 1
  • Craig Russell
    • 2
  • Vijay Sivaraman
    • 1
  1. 1.University of New South WalesSydneyAustralia
  2. 2.Data61, CSIROSydneyAustralia

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