An Adaptive IoT Network Security Situation Prediction Model

With the rapid development of the Internet of things (IoT) technology, how to effectively predict the network security situation of the IoT has become particularly important. It is difficult to quantify the IoT network situation due to a large number of historical data dimensions, and there are also has the problem of low accuracy for IoT network security situation prediction with multi-peak changes. To solve the above problems, this paper proposed an adaptive IoT network security situation prediction model, which makes the IoT network security situation prediction accuracy higher. Firstly, the paper used the entropy correlation method to calculate the network security situation value sequence in each quantization period according to Alarm Frequency (AF), Alarm Criticality (AC), and Alarm Severity (AS). Then, the security situation values arranged in time series are fragmented through the sliding window mechanism, and then the adaptive cubic exponential smoothing method is used to initially generate the IoT network security situation prediction results. Finally, the paper built the time-varying weighted Markov chain to predict the error value and modify the initial predicted value based on the error state. The experimental results show that the model has a better fitting effect and higher prediction accuracy than other models, and this model's determination coefficient is 0.811. Compared with the other two models, the sum of squared errors in this model is reduced by 78 %-82 %. The model can better reflect the changes in the IoT network security situation over a while.