A sample generation method and device

Abstract

The invention discloses a method and device for generating a sample. The data in the pre-collected training set is input into a neural network model to obtain a sample generation model. The neural network model is a neural network model using an attention mechanism; the pre-collected test set is The data input to the sample generation model generates samples. The present invention adopts the neural network model of the attention mechanism, so that the neural network model can assign different weights to the characters in the input data, thereby more selectively learning the data information in the input data, and finding the characters in the input data that are related to this output. Data with high data correlation will eventually obtain a high-precision deep learning model, and the training loss is small, and the samples generated by the high-precision deep learning model must have higher legitimacy and diversity.

Description

technical field [0001] The invention relates to the field of data processing, in particular to a sample generation method and device. Background technique [0002] Fuzz testing is a common vulnerability mining technique to discover software vulnerabilities by providing unexpected inputs to the target program and monitoring abnormal results. Among them, samples with high legality and diversity need to be generated in fuzz testing. [0003] The existing sample generation method is to use a neural network with a two-layer Long Short-Term Memory (LSTM) model to train a deep learning model, and then input the test data set provided by the Windows official test team into the trained deep learning model. In the model, samples are obtained. [0004] In the existing sample generation method, the source of test data is provided by the government, which is relatively single, which makes the diversity of the final generated samples poor. Moreover, the LSTM model does not have the abi...

AI Technical Summary

Problems solved by technology

[0005] The present invention provides a sample generation method and device, which can solve the problem of the low accuracy of the deep learning model and the large training loss caused by the neural network training with the two-layer LSTM model in the prior art, which leads to the legality and safety of the final generated samples. The problem of poor diversity

Images