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<p>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.</p>

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

  • 期刊论文

  • 发表日期：

    2026年4月13日

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  • Hanin Alyahya,
  • Tarek Sheltami

摘要

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.