Radio optical network security analysis with routing in quantum computing for 5G wireless communication using blockchain machine learning model

Rapid growth is also being seen in the deployment of optical network equipment and the development of new network services for next-generation networks beyond 5G and 6G. Sybil attacks, wormhole assaults, and single-point failure are just some of the security threats that may affect optical networks. With the introduction of new B5G applications, the conventional setup is no longer enough to meet the need of real-time automatic configuration. A new automated setup approach for the underlying optical transport network is desired by operators. This research proposes a novel method for analysing the security and routing of radio optical networks by bringing together blockchain machine learning and quantum computing. Here, the security of the radio optical network is improved by using cryptanalysis through a federated blockchain model, and the routing is handled by means of sparse Fourier Q-adversarial Boltzmann neural networks. This research tackles a number of issues plaguing optical networks and proposes a strategy for protecting networks from intruders by using quantum-secured blockchain in these environments. Quality of service, data integrity, throughput, latency, packet delivery ratio, and end-to-end delay are all measured experimentally across a range of network security datasets. The proposed method achieved a 72% Quality of Service, 68% Data Integrity, 98% Throughput, 56% Latency, a 96% Packet Delivery Ratio, and a 49% End-to-End Delay.