Training method and device for anomaly detection model based on differential privacy

Abstract

The embodiment of the invention provides a training method for an anomaly detection model based on differential privacy. The method comprises the following steps: inputting a first vector of any sample in a training set into an auto-encoding network, outputting a dimension-reduced second vector through an encoder, and outputting a restored third vector through a decoder. Then, constructing an evaluation vector based on the second vector, inputting the evaluation vector into an evaluation network, and obtaining the sub-distribution probability that the sample output by the evaluation network belongs to K sub-Gaussian distributions in the Gaussian mixture distribution; then, according to the evaluation vector and the sub-distribution probability corresponding to each sample in the training set, obtaining a first probability of any sample in Gaussian mixture distribution; and determining the prediction loss which is negatively correlated with the first probability corresponding to each sample and is negatively correlated with the similarity between the first vector and the third vector. Furthermore, noise is added to an original gradient obtained on the basis of prediction loss in a differential privacy mode, and model parameters of the anomaly detection model are adjusted by using a gradient containing the noise.

Description

technical field [0001] One or more embodiments of this specification relate to the field of computer technology, and in particular, to a method and device for training an anomaly detection model based on differential privacy executed by a computer. Background technique [0002] With the development of computer technology, security has become a growing concern, such as computer data security, electronic payment transaction security, network access security, and so on. Therefore, in many scenarios, it is necessary to find abnormal samples that may affect security from a large number of samples, and take measures against these abnormal samples. [0003] For example, it is hoped to discover abnormal transaction operations from a large number of transaction operation samples, so as to prevent fraudulent transactions in advance; it is hoped to detect abnormal access from network access samples, so as to discover unsafe access, such as hacker attacks; it is hoped that users who per...

AI Technical Summary

Problems solved by technology

[0005] However, the existing unsupervised anomaly detection models often have the risk of leaking training samples,

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