Authors: Anirudh Narayan, Bindu Lakshmi, Haritha Gopal, Vivek Vardhan
Abstract: In the evolving landscape of IT infrastructure monitoring, the volume and velocity of log data generated by tools such as Nagios and Zabbix present significant challenges for timely and accurate anomaly detection. Traditional rule-based approaches, which rely on static thresholds and manual configurations, often fail to capture subtle or emerging issues, leading to alert fatigue or missed incidents. To address these limitations, the integration of artificial intelligence, particularly machine learning, into log-based monitoring has emerged as a transformative solution. By analyzing patterns in historical logs and adapting dynamically to changes in system behavior, AI models ranging from supervised classifiers to unsupervised clustering algorithms and deep learning architectures can enhance the detection of anomalies within Nagios and Zabbix environments. This review examines the application of AI to anomaly detection in logs generated by Nagios and Zabbix, focusing on key log types such as performance metrics, event logs, alert logs, and syslogs. It explores how AI improves detection precision, reduces false positives, and enables earlier incident prediction. The paper also compares data handling mechanisms in both tools and outlines common AI integration pipelines including log preprocessing, model training, and real-time inference. Furthermore, implementation case studies and evaluation metrics are discussed to highlight real-world benefits and performance trade-offs. Ultimately, this article positions AI-driven anomaly detection as a critical enabler for modern observability and proactive IT operations, especially in large-scale or mission-critical infrastructures.
