Application of a FL Time Series Building Model in Mobile Network Interaction Anomaly Detection in the Internet of Things Environment

Abstract

With the continuous development of the social economy, mobile network is becoming more and more popular. However, it should be noted that it is vulnerable to different security risks, so it is extremely important to detect abnormal behaviors in mobile network interaction. This paper mainly introduces how to detect the characteristic data of mobile Internet interaction behavior based on IOT FL time series component model, set the corresponding threshold to screen the abnormal data, and then use K-means++ clustering algorithm to obtain the abnormal set of multiple interactive data, and conduct intersection operation on all abnormal sets, so as to obtain the final abnormal detection object set. The simulation results show that the FL time series component model of the Internet of Things is effective and can support abnormal detection of mobile network interaction behavior.