The Domain Name System (DNS) provides a translation between readable domain names and IP addresses. The DNS is a key infrastructure component of the Internet and a prime target for a variety of attacks. One of the most significant threat to the DNS's wellbeing is a DNS poisoning attack, in which the DNS responses are maliciously replaced, or poisoned, by an attacker. To identify this kind of attack, we start by an analysis of different kinds of response times. We present an analysis of typical and atypical response times, while differentiating between the different levels of DNS servers' response times, from root servers down to internal caching servers. We successfully identify empirical DNS poisoning attacks based on a novel method for DNS response timing analysis. We then present a system we developed to validate our technique that does not require any changes to the DNS protocol or any existing network equipment. Our validation system tested data from different architectures including LAN and cloud environments and real data from an Internet Service Provider (ISP). Our method and system differ from most other DNS poisoning detection methods and achieved high detection rates exceeding 99 . These findings suggest that when used in conjunction with other methods, they can considerably enhance the accuracy of these methods.
@cite_42: This paper seeks to quantitatively understand the nature of the current threat towards the common name servers. A new tracking technique based on statistical model is proposed to locate the anomalous name servers by analyzing the real-world DNS traffic. After summarizing the attacks towards DNS, the detection method based on associative feature analysis is presented. Experiments are conducted which highlighting both the payload anomaly and the data flow anomaly, and the experimental results reveal the efficiency of our method in detecting the anomalous behaviors of name servers.
@cite_42 proposed an associative feature analysis approach based on statistical models to track the anomalous behavior of DNS servers. In collaboration with a major commercial ISP in China, they captured and analyzed real DNS traffic in this large-scale network environment. They used an outlier function to map malicious responses. The parameters they used were queries responses per client server specific server. The authors detected various attacks in the real world, but did not determine the real volume of the attacks. In other words, they could not evaluate their accuracy.