Strategic Real-Time Monitoring and Detection of Anomalous Behavior for Enhanced Security, Performance, and Reliability in Complex IT Network Infrastructures

Abstract

This paper explores the critical role of strategic monitoring in IT network security, emphasizing the detection of anomalous behavior to safeguard sensitive data and system integrity. As IT networks have evolved from simple academic tools to complex infrastructures integrating cloud computing and IoT devices, the sophistication of cyber threats has similarly advanced, necessitating robust security measures. The paper examines various methodologies for anomaly detection, including signature-based, heuristic-based, and machine learning approaches, highlighting their strengths and limitations. Signature-based detection excels in identifying known threats but struggles with new anomalies, while heuristic-based methods offer flexibility but require intensive rule creation. Machine learning and AI approaches, despite their high computational demands, present promising capabilities for detecting complex and unknown anomalies. Through a comprehensive review of current methodologies, empirical case studies, and the challenges of existing approaches, the paper aims to provide insights into effective anomaly detection strategies and future research directions. Addressing these challenges is essential for enhancing IT network security and mitigating the impact of emerging cyber threats.