Abstract: The disclosure is directed towards the real-time detection and mitigation of security threats to a domain name system (DNS) for a communication network. A graph-theoretic method is applied to detect compromised DNS assets (e.g., DNS servers and web servers that DNS servers map domain names to). A graph is generated from domain name resolution (DNR) transactions. The nodes of the graph represent the DNS assets and edges between the nodes represent the DNR transactions. The graph is analyzed to detect features that signal compromised assets. The detection of such features serves to act as a binary classifier for the represented assets. The binary classifier acts to classify each node as non-compromised or compromised. The analysis is guided by supervised and/or unsupervised machine learning methods. Once the assets are classified, DNR transactions are analyzed in real-time. If the transaction involves a compromised asset, an intervention is performed that mitigates the threat.

Abstract: The disclosure is directed towards the real-time detection and mitigation of security threats to a domain name system (DNS) for a communication network. A graph-theoretic method is applied to detect compromised DNS assets (e.g., DNS servers and web servers that DNS servers map domain names to). A graph is generated from domain name resolution (DNR) transactions. The nodes of the graph represent the DNS assets and edges between the nodes represent the DNR transactions. The graph is analyzed to detect features that signal compromised assets. The detection of such features serves to act as a binary classifier for the represented assets. The binary classifier acts to classify each node as non-compromised or compromised. The analysis is guided by supervised and/or unsupervised machine learning methods. Once the assets are classified, DNR transactions are analyzed in real-time. If the transaction involves a compromised asset, an intervention is performed that mitigates the threat.