Performance Evaluation of Data-Centric Networks Based on Parallel and Distributed Architectures for Machine Intelligence Research

Abstract

Maintaining computer network security has long been an essential component of computer administration. Network security has become essential to companies’ safety and steady development in real-time implementation for diverse physical domains employing computers. Establishing a specific physical domain has consistently included a priority job of improving the safety of computer systems management. Study results on the practical implementation of machine intelligence in network security maintenance. Specifically, this paper examines and offers security management techniques for computer network data protection to establish an all-encompassing security shield for networked computers, with the ultimate goal of improving the safety and reliability of the net-worked computers used in commercial settings. Furthermore, the comprehensive research analysis determines an architectural model for machine intelligence-based network security maintenance (MI-NSM), implying an intrusion detection scheme with a novel neural network system. Labelling important properties of objects or data points and searching for commonalities allows the auto-mobile’s AI to distinguish between a human, the street, another car, and the sky. The simulation evaluation is performed using the NS2 simulator and observes the security maintenance efficiency over the current security solutions.