Mitigating Negative Impacts on DNS Caches Caused by Disposable Domain Names DNS caches play an important role in DNS querying. However, the performance of DNS caches will be remarkably influenced by disposable domain names, which are generated by services of cloud storage, social networks, etc., and belong to a new class of misused case of DNS. In this paper, we proposed a novel solution named DC3 (Domain Classification and Cascade Cache) to mitigate the negative impact. Domain Classification adopts a classifier which is based on a long shortterm memory (LSTM) network to prevent disposable domains from being cached. Cascade Cache is a refined cascade LRU policy considering cache size allocation to process the remaining disposable domains. By querying the real DNS traces collected from a large ISP network, experiment results show that this solution can detect disposable domain names and mitigate their negative impacts on DNS caches effectively. Specifically, in our dataset, 67.4% of all distinct domain names are detected as disposable domain names. Correspondingly, when getting rid of them by using this solution, we can raise the cache hit rate more than double.

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.