Measuring the impact of a successful DDoS attack on the customer behaviour of managed DNS service providers Distributed DenialofService (DDoS) attacks continue to pose a serious threat to the availability of Internet services. The Domain Name System (DNS) is part of the core of the Internet and a crucial factor in the successful delivery of Internet services. Because of the importance of DNS, specialist service providers have sprung up in the market, that provide managed DNS services. One of their key selling points is that they protect DNS for a domain against DDoS attacks. But what if such a service becomes the target of a DDoS attack, and that attack succeeds? In this paper we analyse two such events, an attack on NS1 in May2016, and an attack on Dyn in October2016. We do this by analysing the change in the behaviour of the serviceu0027s customers. For our analysis we leverage data from the OpenINTEL active DNS measurement system, which covers large parts of the global DNS over time. Our results show an almost immediate and statistically significant change in the behaviour of domains that use NS1 or Dyn as a DNS service provider. We observe a decline in the number of domains that exclusively use NS1 or Dyn as a managed DNS service provider, and see a shift toward risk spreading by using multiple providers. While a large managed DNS provider may be better equipped to protect against attacks, these two case studies show they are not impervious to them. This calls into question the wisdom of using a single provider for managed DNS. Our results show that spreading risk by using multiple providers is an effective countermeasure, albeit probably at a higher cost.

IDNS: A HighPerformance Model for Identification of DNS Infrastructures on Largescale Traffic Domain Name System (DNS) is indispensable in a large number of network applications. Identifying DNS infrastructures into different roles hierarchically is highly desired for a variety of purposes such as network management and threat evaluation. However, traditional measurements almost all depend on active scanning without considering dynamic packetlevel features of different DNS infrastructures.In this paper, we propose a highperformance model IDNS (Identifying DNS) based on passive measurement. IDNS: (i) extracts singlepacket field features (SFF) and multipacket statistical features (MSF) from DNS traffic, (ii) utilizes an estimation algorithm to calculate MSF for satisfying online processing speed, and (iii) applies several classifiers in Ensemble Learning and Incremental Learning. We perform an extensive evaluation based on a large volume of DNS queries and responses collected from one ISP. The evaluation results demonstrate that the best classifier in Ensemble Learning can reach 90% accuracy rate while the classifier in Incremental Learning can reach 80% with the highest scalability.