Optical network modelling-based data analytics for network monitoring and security analysis using hybrid computing models

With the ever-increasing complexity and scale of optical networks, efficient network monitoring and robust security analysis have become paramount. In this study, we propose a novel approach that combines optical network modeling with data analytics, leveraging the power of hybrid computing models. Aim of this research is to propose novel technique in optical network modelling based on data analysis in network monitoring with security enhancement using hybrid computing model with deep learning techniques. Here the optical network monitoring and anomaly detection is carried out using fuzzy density clustering based markov K-means attention recurrent neural network (FDCM-KARNN). By continuously monitoring network performance and traffic patterns, our system can proactively detect and respond to network aberrations, minimizing downtime and mitigating potential risks. Then by hybrid computing based on edge and cloud network the vast amounts of network data and provide accurate predictions has been enabled. The experimental analysis is carried out for various monitored optical network dataset based on network security analysis and data analysis in terms of training accuracy, throughput, packet delivery ratio, data integrity, precision. The proposed technique attained training accuracy of 99%, precision of 98%, packet delivery ratio of 97%, data integrity of 85%, throughput of 96%.