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.

Discovering Suspicious APT Behaviors by Analyzing DNS Activities As sensors become more prevalent in our lives, security issues have become a major concern. In the Advanced Persistent Threat (APT) attack, the sensor has also become an important role as a transmission medium. As a relatively weak link in the network transmission process, sensor networks often become the target of attackers. Due to the characteristics of low traffic, long attack time, diverse attack methods, and realtime evolution, existing detection methods have not been able to detect them comprehensively. Current research suggests that a suspicious domain name can be obtained by analyzing the domain name resolution (DNS) request to the target network in an APT attack. In past work based on DNS log analyses, most of the work would simply calculate the characteristics of the request message or the characteristics of the response message or the feature set of the request message plus the response message, and the relationship between the response message and the request message was not considered. This may leave out the detection of some APT attacks in which the DNS resolution process is incomplete. This paper proposes a new feature that represents the relationship between a DNS request and the response message, based on a deep learning method used to analyze the DNS request records. The algorithm performs threat assessment on the DNS behavior to be detected based on the calculated suspicious value. This paper uses the data of 4, 907, 147, 146 DNS request records (376, 605, 606 records after DNS Data Preprocessing) collected in a large campus network and uses simulation attack data to verify the validity and correctness of the system. The results of the experiments show that our method achieves an average accuracy of 97.6% in detecting suspicious DNS behavior, with the orange false positive (FP) at 2.3% and the recall at 96.8%. The proposed system can effectively detect the hidden and suspicious DNS behavior in APT.