GANFuzz: a GAN-based industrial network protocol fuzzing framework

In this paper, we attempt to improve industrial safety from the perspective of communication security. We leverage the protocol fuzzing technology to reveal errors and vulnerabilities inside implementations of industrial network protocols(INPs). Traditionally, to effectively conduct protocol fuzzing, the test data has to be generated under the guidance of protocol grammar, which is built either by interpreting the protocol specifications or reverse engineering from network traces. In this study, we propose an automated test case generation method, in which the protocol grammar is learned by deep learning. Generative adversarial network(GAN) is employed to train a generative model over real-world protocol messages to enable us to learn the protocol grammar. Then we can use the trained generative model to produce fake but plausible messages, which are promising test cases. Based on this approach, we present an automatical and intelligent fuzzing framework(GANFuzz) for testing implementations of INPs. Compared to prior work, GANFuzz offers a new way for this problem. Moreover, GANFuzz does not rely on protocol specification, so that it can be applied to both public and proprietary protocols, which outperforms many previous frameworks. We use GANFuzz to test several simulators of the Modbus-TCP protocol and find some errors and vulnerabilities.