Prevention and Control Technology of Optical Transmission Network Management being Illegally Taken over with Wireless Sensor Technology

Abstract

Abstract The transmission network represented by optical fiber communication constitutes the most important basic network of modern communication. It provides transmission channels for various telecommunication service networks, dispatches and protects the transmission channels, manages the transmission network, and is an important part of the entire network management system. The efficient and centralized management of the resource objects in the network management is the basis for realizing the optical transmission integrated network management system, and it is also an important component of the entire integrated network management system. In this context, this paper studies the prevention and control technology of optical transmission network management being illegally taken over, based on wireless sensing technology. We use deep learning to monitor traffic anomalies that may occur when the network management is illegally taken over and then feed the data back to the security maintenance system to clear the anomalies. We introduce the development status of wireless sensor technology and optical transmission network management technology, which provided a theoretical basis for building network models. The related technologies and principles of DNNs are introduced, the traffic model and anomaly detection are studied, and a self-encoding and decoding anomaly detection model based on an attention mechanism is proposed. An anomaly scoring mechanism is designed on the basis of the traffic model, and an attention-based anomaly detection model is proposed. Experimental results show that the accuracy of our model reaches 95% while the recall is more than 96%, outperforming many of the existing models. The training efficacy of the model is significantly improved.