In order to improve the stability of network transmission,an automatic identification algorithm for abnor-mal traffic in elastic optical networks based on isolated forest algorithm is proposed.Perform spectral density detection based on the abnormal distribution characteristics of traffic and the differences in normal data,construct a spectral fea-ture extraction model for elastic optical network traffic,implement spectral feature filtering for abnormal traffic through low-pass filter convolution vector reorganization,adopt isolated forest algorithm to achieve adaptive optimization control for network traffic anomaly detection,and combine multi-dimensional spatial structure reorganization method to a-chieve detection and recognition of abnormal traffic in elastic optical network.The results showed that the missed de-tection rate and the false detection rate were relatively low,3.16％and 1.03％,respectively.The detection takes less time,only 16 seconds.During detection,the external intrusion rate does not exceed 1％,and the immunity is strong.
维普
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