Security situation prediction of artificial intelligence network based on wireless sensor

The security situational prediction aims to assess the present state of network security, forecast its future development trends, inform individuals to implement appropriate defensive measures and secure the network environment by analysing and extracting wireless sensor network (WSN) data. This study proposed a multilayer perceptron feed forward neural network model in WSN (MPFNN-WSN) that relies on accurate forecasting and mitigating security prediction of AI networks. The study offers multilayer perceptron networks for attack detection and network security prediction systems for hacker identification and threat level. This new AI-powered wireless measurement system has revolutionised data analysis by adding new computing and analysing capabilities to existing innovations. According to experiments that compared it to state-of-the-art evolutionary methods, the strategy considerably reduced root-mean-squared errors (RMSEs) and accuracy for different test sets. Results are better with the proposed method when contrasted with the industry standard.