Out-of-distribution network flow data detection method based on calculated likelihood ratio

The invention discloses an out-of-distribution network flow data detection method based on a calculated likelihood ratio, and belongs to the field of network flow data detection. The objective of the invention is to improve the accuracy and confidence of network flow data identification. The method comprises the following steps: extracting network traffic characteristics: the original traffic is a pcap packet, is divided into different data streams according to a quintuple, and is set to extract a data packet length sequence, calculate a packet arrival time interval sequence, store the sequences and generate a CSV file as original training data of model training; the method comprises the following steps: training an original classification model by using original training data, training the original classification model by adopting a deep learning algorithm long-short-term memory network to obtain a model trained by the original training data, generating disturbance data, generating disturbance data by adopti