Open AccessTest Case Generation Method based on Generative Adversarial Network
Author(s) -
Jiaojiao Nie,
Xianglin Fan,
Yushuang Wu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1883/1/012073
Subject(s) - fuzz testing , modbus , computer science , generative adversarial network , construct (python library) , protocol (science) , machine learning , artificial intelligence , test (biology) , vulnerability (computing) , generative grammar , test data , data mining , communications protocol , computer security , computer network , deep learning , software , software engineering , medicine , paleontology , alternative medicine , pathology , biology , programming language
Traditional fuzzing tools generate low diversity of test cases and low vulnerability detection efficiency. This paper uses a test case generation model based on a generative confrontation network. The model uses LSTM as the generation network to generate data. The model uses fully connected network to construct a discriminant network for classification, and the test cases with the same protocol format are automatically generated after training. Finally the model uses the generated test cases to attack the protocol system to detect vulnerabilities. This method is compared with the traditional tools AFL, Peach and Sulley on the Modbus protocol, and the validity of the method is verified.