DNScope: Detecting DNS Misconfigurations through Graph Neural Static Reasoning
Year: 2025
Type: conference-paper
Abstract: The Domain Name System (DNS) underpins Internet reliability but remains vulnerable to subtle, multi-zone misconfigurations that can trigger widespread outages. We present DNScope, a purely static analysis framework that encodes DNS zone files as a Trace Configuration Graph (TCG), capturing cross-nameserver, intra-zone, and inter-zone dependencies. A Gated Graph Neural Network with an attention mechanism learns node representations that integrate local record features and global structural patterns, enabling unsupervised anomaly detection of malformed subgraphs without any runtime data or manually defined rules. On the DNS datasets, DNScope pinpoints misconfiguration root causes, paving the way for automated DNS reliability and self-healing.
Authors: Kaiqiang Hu, Haizhou Du, Ziyi Wang
Institutions: Shanghai University of Electric Power, Xiamen University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4413311726