Speech network analysis and anomaly detection based on FSS model

摘要

Speech network analysis and anomaly detection based on the FSS model is analyzed in this research. Aiming at the problem of parallel detection and processing of massive data in distributed intrusion detection, a data segmentation algorithm based on capability and load is proposed. The algorithm evaluates the capabilities and actual load of the nodes, weighs the actual state of each node and the data distribution relationship in the cluster, and allocates more data to be processed to nodes with strong data processing capabilities and light loads. High-order statistics are used to describe the intrusion characteristics of persistent attacks in the link layer of the speech sensor networks, and vector quantitative decomposition is used to analyze the fusion characteristics of advanced persistent intrusion symbols in mobile terminals. Different terminals are estimated based on the machine learning algorithms, and the FSS model is integrated to achieve the comprehensive analysis of the speech analytic models. The experiment compared with the state-of-the-art methods have proven the efficiency of the framework. The detection accuracy is higher than the latest methodologies.