Authors: Nur Aisyah Karim
Abstract: Artificial intelligence (AI) has become a transformative technology in enhancing enterprise network monitoring systems by enabling intelligent, automated, and real-time analysis of network activities. Traditional monitoring approaches often struggle to manage the increasing complexity, scale, and dynamic nature of modern enterprise networks. AI-based monitoring systems address these limitations by leveraging machine learning, deep learning, and data analytics to detect anomalies, predict network failures, and optimize performance. These systems continuously analyze network traffic, system logs, and user behavior to identify security threats, performance bottlenecks, and operational inefficiencies. The study explores the architecture of AI-driven monitoring systems, including data collection, processing, analytics, and response layers integrated with cloud infrastructure. It also highlights applications in cybersecurity, network optimization, and predictive maintenance. Furthermore, the paper discusses key challenges such as data volume, false positives, model accuracy, and integration complexity. Emerging trends such as autonomous network management, edge AI, and real-time predictive analytics are also examined. The findings emphasize that AI-based monitoring significantly enhances network reliability, security, and operational efficiency in enterprise environments.
DOI: http://doi.org/
