Test case generation system based on neural style migration

Abstract

The invention discloses a network protocol test case generation system based on neural style migration, which comprises an original data collection module, an encoding module, a neural style migrationmodule and a reverse encoding module. The original data collection module collects the traffic data in the industrial control network system and classifies the collected data by clustering algorithm.The encoding module encodes the classified data into a picture form; The neural style transfer module takes the pictures and style pictures output by the encoding module as the input, and transformsthe neural style based on the neural style transfer method. In the process of transforming, the degree of the style transformation is controlled by continuous training iteration. The inverse coding module converts the two-dimensional image output by the neural style transformation module into one-dimensional sequential form as a test case. This test case can be directly injected into the target network for attack testing. This system can intelligently learn the protocol format, reduce the artificial learning process, and improve the efficiency of testing.

Description

technical field [0001] The invention relates to the field of industrial control communication protocols and deep learning, in particular to a test case generation system based on neural style transfer. Background technique [0002] In the field of traditional fuzz testing, the generation of test cases includes random generation and model-based generation. These two methods need to know the format of the protocol in advance or need manual reverse engineering to analyze the format of the protocol, which requires high professional knowledge of the participants. At the same time, this test case generation method appears to be less efficient in the whole process. When the target protocol to be tested is relatively complex, artificially designed test cases often lack diversity, and it is difficult to achieve good test depth and coverage, resulting in incomplete testing. As an important part of the industrial control system, the industrial control protocol is crucial to the safe o...

AI Technical Summary

Problems solved by technology

[0002] In the field of traditional fuzz testing, the generation of test cases includes random generation and model-based generation. These two methods need to know the format of the protocol in advance or need manual reverse engineering to analyze the format of the protocol, which requires high professional knowledge of the participants. At the same time, this test case generation method appears to be less efficient in the whole process

When the target protocol to be tested is relatively complex, artificially designed test cases often lack diversity, and it is difficult to achieve good test depth and coverage, resulting in incomplete testing

Images