Managing 5G IOT Network Operations and Safety Using Deep Learning and Attention Methods

The abstract introduces an innovative method for overseeing 5G-connected Internet of Things (IoT) networks by combining deep learning and attention techniques. The approach use deep learning prediction algorithms to anticipate traffic patterns and allocate resources in order to optimise network operations and ensure security. Statistical analysis and exploratory research offer valuable insights into the dynamics of traffic, which in turn aid in the creation of precise prediction models. The attention-based LSTM learning model allows for the prediction of forthcoming traffic conditions by capturing the temporal connections in sequential data. Simultaneously, a security system that utilisesautoencoders efficiently identifies cyber-attacks by acquiring knowledge from network behaviour patterns. In addition, the utilisation of network modelling tools, such as network slicing, improves the efficiency of resource management and reduces security threats by dividing IoT networks into distinct logical subnetworks. The performance assessments clearly indicate the superiority of the suggested technique in traffic prediction and attack detection when compared to standard models. This framework provides a holistic solution for overseeing 5G IoT networks, guaranteeing utmost efficiency and safety in the present linked society.