User security level identification method and device based on multi-stage time sequence and multiple tasks

Abstract

The invention relates to a user security level identification method and device based on multiple stages of time sequences and multiple tasks. The method comprises the following steps: generating a plurality of stage sets according to total users and user stages corresponding to the total users; sequentially arranging the plurality of stage sets according to a time sequence; performing multi-task training on the (n + 1) th group of initial models according to the (n + 1) th stage set and the nth group of model parameter vectors to generate the (n + 1) th group of model parameter vectors, n being a positive integer; generating a plurality of groups of stage scoring models based on a plurality of groups of model parameter vectors until the plurality of stage sets are trained; and performing security level identification on the current user through the multiple groups of stage scoring models. According to the method, the multi-task machine learning method can be integrally improved from the aspects of actual problems and application scenes, model samples and model parameters, so that user data security and transaction security of an application system are ensured.

Description

technical field [0001] The present application relates to the field of computer information processing, and in particular, to a method, apparatus, electronic device, and computer-readable medium for identifying user security levels based on multi-stage sequential multitasking. Background technique [0002] Machine learning, which uses useful information from historical data to help analyze future data, usually requires a large amount of labeled data to train a good learner. A deep learning model is a typical machine learning model, because this type of model is a neural network with many hidden layers and many parameters, so it usually requires millions of data samples to learn accurate parameters. However, some applications, including medical image analysis, cannot meet this data requirement because labeling the data requires a lot of human labor. In these cases, multi-task learning (MTL) can help alleviate this data sparsity problem by using useful information from other ...

AI Technical Summary

Problems solved by technology

However, in actual application scenarios, it is difficult for the data in the training data set to accurately reflect the real situation

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