Hardening DGA Classifiers Utilizing IVAP Domain Generation Algorithms (DGAs) are used by malware to generate a deterministic set of domains, usually by utilizing a pseudorandom seed. A malicious botmaster can establish connections between their commandandcontrol center (Cu0026C) and any malwareinfected machines by registering domains that will be DGAgenerated given a specific seed, rendering traditional domain blacklisting ineffective. Given the nature of this threat, the realtime detection of  :62,based on incoming DNS traffic is highly important. The use of neural network machine learning (ML) models  :80,task has been wellstudied, but there is still substantial room for improvement. In this paper, we propose to  :99,Vennxe2x80x93Abers predictors (IVAPs) to calibrate the output of existing ML models for DGA classification. The  a :115,computationally efficient procedure which consistently improves the predictive accuracy of classifiers at the  :130,not offering predictions for a small subset of inputs and consuming an additional amount of training data.

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.