Single classifier anomaly detection method based on multilayer random neural network

Abstract

The invention discloses a single classifier anomaly detection method based on a multilayer random neural network. The the method comprises: only inputting a training data set of a normal class; through multilayer ELM-AE autoencoder and decoding processing, input sample data obtaining a reconstructed characteristic value; inputting the reconstructed characteristic value into the last layer of ELM to obtain actual output; sorting the obtained distance error vectors of the actual output and output tags from large to small, and determining a threshold for separating a normal class from an abnormalclass according to a set threshold parameter; and finally, inputting the test data into the multi-layer random neural network single classification abnormity detection model, and testing the recognition effect of the model. According to the method, main information is extracted more quickly and efficiently, dimensionality reduction is carried out, and then recognition and classification are carried out. And the speed is higher, the accuracy is higher, and the generalization performance is better. The method is not only suitable for small data sets, but also suitable for high-dimensional largedata sets, and has universality. And the method has important significance for practical application in future.

Description

technical field [0001] The invention belongs to the field of machine learning and data mining, and relates to a single classifier anomaly detection method based on a multi-layer random neural network. Background technique [0002] Anomaly detection is an important branch of machine learning and data mining, and is widely used in various fields, such as credit card fraud detection in commercial and financial fields, disease detection and chemical substance toxicity detection in biomedicine, and analysis in the field of computer images detection etc. The existence of abnormal data will bring certain harm and loss, which seriously threatens the safety of people's life and property. Therefore, how to detect the anomalies in the data is of great significance. [0003] Anomaly detection is to detect data and behaviors that do not meet expectations by analyzing the input data, but in the actual detection process, it faces many challenges. 1. It is difficult to obtain accurate an...

AI Technical Summary

Problems solved by technology

Traditional neural network learning algorithms (such as BP algorithm) need to artificially set a large number of network training parameters, so it is very easy to generate local optimal solutions

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