Prediction of the domain name system (DNS) quality attributes

Abstract

The Domain Name System (DNS) has a direct impact on the performance and dependability of nearly all aspects of interactions on the Internet. DNS relies on a delegation-based architecture, where resolution of a name to its IP address requires resolving the names of the servers responsible for that name. The graphs of the inter-dependencies that exist between name servers associated with each zone are called Dependency Graphs. We constructed a DNS Dependency Model as a unified representation of these Dependency Graphs. We utilize a set of Structural Metrics defined over this model as indicators of external quality attributes of the domain name system. We explore the inter-metric and inter-quality relations further in order to quantify the indicative power of each metric. We apply some machine learning algorithms in order to construct Prediction Models of the perceived quality attributes of the operational system out of the structural metrics of the model. Assessing these quality attributes at an early stage of the design/deployment enables us to avoid the implications of defective and low-quality designs and deployment choices and identify configuration changes that might improve the availability, security, stability and resiliency postures of the DNS.

Domain Name System Security and Privacy: A Contemporary Survey

“…For example, the application of machine learning algorithms are widely used [80,17,58]. This section introduces some of the prevalent data analysis techniques that have been used in the cybersecurity community, such as machine learning algorithms [80,17,46,53,22,54,55,56,87,88,89,76] and association analysis [59,90,54,91,51].…”

“…First, we describe two main DNS data collection methods utilized in the literature and the associated works, including passive DNS data (PDNS) [80,47,81,82,83,12,11,84,48,41,67,22,51] and Active DNS data (ADNS) [67,83,85,54,41,86]. Next, we categorize the research works based on the common DNS data analysis techniques that have been used in the literature, such as machine learning algorithms [80,17,46,53,22,54,55,56,87,88,89,76] and association analysis [59,82,90,54,91]. Finally, we categorize the research works based on their scope of analysis and from security point of view into vanilla DNS [35,40,29,92,93,94,18] and DNSSEC [95,96,97,86,…”