Network Traffic Prediction And Abnormal Traffic Detection Based On Kafka Monitoring System

With the advancement of social science and technology and the rapid development of the Internet,people's demand for networ.k speed is constantly improving.This is followed by more secure network devices and more complex network structures.In the face of increasingly complex network environments,how to effectively monitor network flow,how to establish a generalized network flow model,and how to detect abnormal network flow in data streams.It is meaningful to study these questions for network management and resource allocation optimization.The network flow monitoring system is the basis of network flow analysis.A stable,robust network flow monitoring system can provide high-quality services for network management and network decision-making.And for network resource management,accurate network flow prediction can play a guiding role in the rational allocation of network resources,and accurate abnormal detection can enable relevant staff to find problems in time and avoid losses.In response to the above requirements,this thesis does the following work:Firstly,this thesis investigates the common system deployment environment and designs a Kafka-based flow monitoring and collection system,the hierarchical structure design in the system ensures the stability and robustness of the system.Then,this thesis analyzes the collected network flow,designs and implements an LSTM-based network model,which can predict network flow accurately and efficiently.Finally,this thesis proposes and implements a LOF-based outlier detection algorithm,then analyzes the detection results of the algorithm.In the real environment provided by the operator,the network flow monitoring and collecting system designed by this thesis can maintain stable and effective operation.The designed and implemented network flow model can accurately predict network flow,and the predicted results will play a key guiding role in the rational allocation of network resources.The proposed outlier detection algorithm achieves excellent results in accuracy through LOF analysis,and the algorithm greatly reduces the time consumption through Z-score pruning.