Mapping an Enterprise Network by Analyzing DNS Traffic
- 616 Downloads
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.
KeywordsEnterprise network DNS analysis Machine learning
This work was completed in collaboration with the Australian Defence Science and Technology Group.
- 1.DNS Security Introduction and Requirements (2018). https://www.ietf.org/rfc/rfc4033.txt. Accessed 28 May 2018
- 2.Ahmed, J., Gharakheili, H.H., Russell, C., Sivaraman, V.: Real-time detection of DNS exfiltration and tunneling from enterprise networks. In: Proceedings of IFIP/IEEE IM, Washington DC, USA, April 2019Google Scholar
- 3.Almeida, M., Finamore, A., Perino, D., Vallina-Rodriguez, N., Varvello, M.: Dissecting DNS stakeholders in mobile networks. In: Proceedings of ACM CoNEXT, Incheon, Republic of Korea, December 2017Google Scholar
- 5.Chung, T., et al.: Understanding the role of registrars in DNSSEC deployment. In: Proceedings of ACM IMC, London, UK, November 2017Google Scholar
- 6.Deloitte: Elevating cybersecurity on the higher education leadership agenda (2018). https://www2.deloitte.com/insights/us/en/industry/public-sector/cybersecurity-on-higher-education-leadership-agenda.html
- 8.Hao, S., Feamster, N., Pandrangi, R.: Monitoring the initial DNS behavior of malicious domains. In: Proceedings of ACM IMC, Berlin, Germany, November 2011Google Scholar
- 9.Hao, S., Kantchelian, A., Miller, B., Paxson, V., Feamster, N.: PREDATOR: proactive recognition and elimination of domain abuse at time-of-registration. In: Proceedings of ACM CCS, October 2016Google Scholar
- 10.MacFarland, D.C., Shue, C.A., Kalafut, A.J.: Characterizing optimal DNS amplification attacks and effective mitigation. In: Proceedings of PAM, New York, NY, USA, March 2015Google Scholar
- 12.Marshall, S.: CANDID: classifying assets in networks by determining importance and dependencies. Technical report, Electrical Engineering and Computer Sciences, University of California at Berkeley, May 2013Google Scholar
- 13.Müller, M., Moura, G.C.M., de O. Schmidt, R., Heidemann, J.: Recursives in the wild: engineering authoritative DNS servers. In: Proceedings of ACM IMC, London, UK, November 2017Google Scholar
- 14.van Rijswijk-Deij, R., Sperotto, A., Pras, A.: DNSSEC and its potential for DDoS attacks: a comprehensive measurement study. In: Proceedings of ACM IMC, Vancouver, BC, Canada, November 2014Google Scholar