Integration of Computer Networks and Artificial Neural Networks for an AI-based Network Operator

TL;DR

This paper presents an integrated AI-based network operator that uses neural networks to interpret network state information, achieve perfect accuracy, and automate network management tasks.

Contribution

It introduces a novel approach combining network data embedding, a new training loss algorithm, and a custom network simulator for efficient AI network management.

Findings

Achieved 100% accuracy in network operation tasks

Developed a new loss emphasis algorithm for training

Created a network simulator for automated testing

Abstract

This paper proposes an integrated approach combining computer networks and artificial neural networks to construct an intelligent network operator, functioning as an AI model. State information from computer networks is transformed into embedded vectors, enabling the operator to efficiently recognize different pieces of information and accurately output appropriate operations for the computer network at each step. The operator has undergone comprehensive testing, achieving a 100% accuracy rate, thus eliminating operational risks. Furthermore, a novel algorithm is proposed to emphasize crucial training losses, aiming to enhance the efficiency of operator training. Additionally, a simple computer network simulator is created and encapsulated into training and testing environment components, enabling automation of the data collection, training, and testing processes. This abstract outlines the core contributions of the paper while highlighting the innovative methodology employed in the development and validation of the AI-based network operator.