DNS Infrastructure Monitoring Using Real-Time Syslog Analysis: A Pattern-Based Approach for Enterprise Grid Networks

Abstract

Enterprise services are becoming more reliant on the Domain Name System (DNS) infrastructure, and with that increased reliance comes an increased need for proactive monitoring solutions that can detect operational anomalies that can affect service availability. Traditional DNS monitoring methods are largely dependent on availability checks and manual log analysis, which can hinder timely insights into infrastructure health. This paper proposes a pattern-based architecture for real-time monitoring of the DNS infrastructure in enterprise grid networks, based on syslog analysis. The proposed framework starts by collecting the DNS operational logs using Python, parses the logs using structured log events, stores them in SQL Server, visualizes them using Grafana, and automatically alerts using the information gathered. A systematic event classification model is created to classify DNS events based on their operational significance, to monitor zone transfers, replication status, notification events, and configuration changes. The framework aims to be scalable for large enterprise deployments, improve observability of infrastructure, accelerate incident detection, and improve operational decision-making. The proposed approach guarantees efficient and cost-effective solutions to enhance DNS operational resilience through the use of structured log mining and real-time visualization. The insights have been compiled into a practical monitoring architecture that can enable the modern enterprise DDI environment and provide a starting point for future infrastructure monitoring with AI.