NAS: AI-Driven Network Automation for Multi-vendor Environments

The limitations of manual network management in a complex and diverse organizational environment are addressed in this study. Manual configuration and management result in inefficiencies, misconfigurations, and security risks with the evolution of network infrastructure. This paper proposes a modular Network Automation System (NAS) that integrates centralized orchestration, AI-based anomaly detection, and standardized configuration management (NETCONF/YANG) using a microservices architecture to mitigate these challenges. Python was employed to develop the system, and it was validated in a practical simulated environment developed using GNS3 installed on Ubuntu and VirtualBox. The limitations of manual network management in a complex and diverse organizational environment are addressed in this study. Manual configuration and management result in inefficiencies, misconfigurations, and security risks with the evolution of network infrastructure. This paper proposes a modular Network Automation System (NAS) that integrates centralized orchestration, AI-based anomaly detection, and standardized configuration management (NETCONF/YANG) using a microservices architecture to mitigate these challenges. Python was employed to develop the system, and it was validated in a practical simulated environment developed using GNS3 installed on Ubuntu and VirtualBox.