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.

Research on DUAL-ADGAN Model for Anomaly Detection Method in Time-Series Data

“…Ripan et al [ 17 ] proposed an unsupervised K-means clustering anomaly detection algorithm to detect anomalies by determining the optimal K-value through the contour method, calculating the distance between different samples, and clustering normal and abnormal samples into two clusters. Chen et al [ 18 ] used a K-means++ clustering algorithm to cluster and detect time-series feature data to obtain anomaly data clusters and performed intersection operation on all anomaly clusters to obtain the final set of anomaly detection objects. Liu et al [ 19 ] proposed the isolated forest algorithm, which constructs an isolated tree by continuously selecting random subsamples from data samples to form an isolated forest and determines whether the sample is an outlier based on the size of its path length in the isolated forest.…”

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

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”