Abnormal object identification method and device, medium and electronic equipment

Abstract

The invention relates to the field of neural networks, and discloses an abnormal object recognition method and device, a medium and electronic equipment. The method comprises the steps of obtaining object data and a label corresponding to the object data and representing whether the object data is abnormal or not; dividing the object data into a training set and a test set; inputting the object data in the training set and the corresponding labels into a plurality of to-be-trained deep neural network models for training to obtain a plurality of models; inputting the object data of the test setinto a deep neural network model to obtain an abnormal probability output by the model; determining a target deep neural network model according to the abnormal probability output by each model; cascading the target deep neural network model with the extreme gradient boosting model to obtain a cascading model, and training the cascading model by using the training set to obtain a trained cascading model; and inputting the to-be-identified object data into the trained cascade model for prediction. According to the method, the accuracy of identifying the abnormal object is improved, and the missing identification rate of the abnormal object is reduced.

Description

technical field [0001] The present disclosure relates to the technical field of neural networks, and in particular to an abnormal object recognition method, device, medium and electronic equipment. Background technique [0002] When applying computer-related technologies to actual business fields, it is often necessary to identify entities that do not meet certain requirements, and then deal with these identified entities according to certain strategies. For example, in the field of network traffic monitoring, in order to monitor abnormal illegal traffic or large traffic, it is generally necessary to set corresponding rules to restrict it. However, this method of using fixed rules to identify specific entities has defects such as low recognition accuracy and high missed recognition rate. Contents of the invention [0003] In the field of neural network technology, in order to solve the above technical problems, the purpose of the present disclosure is to provide an abnorm...

AI Technical Summary

Problems solved by technology

However, this method of using fixed rules to identify specific entities has defects such as low recognition accuracy and high missed recognition rate.

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