A deep convolution generative adversarial networks based fuzzing framework for industry control protocols

• Published in: Journal of intelligent manufacturing Vol. 32; no. 2; pp. 441 - 457
• Main Authors: Lv, Wanyou, Xiong, Jiawen, Shi, Jianqi, Huang, Yanhong, Qin, Shengchao
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.02.2021; Springer Nature B.V
• Subjects: Automation; Business and Management; Control; Control systems; Convolution; Generative adversarial networks; Industrial electronics; Industrial safety; Machines; Manufacturing; Mechatronics; Network security; Performance measurement; Processes; Production; Protocol (computers); Robotics; Safety; Security; Specifications; Deep adversarial learning; Quality control; Long short-term memory; Industry 4.0; Industrial control protocol; Convolution neural networks; Fuzz testing
• ISSN: 0956-5515; 1572-8145
• DOI: 10.1007/s10845-020-01584-z

A growing awareness is brought that the safety and security of industrial control systems cannot be dealt with in isolation, and the safety and security of industrial control protocols (ICPs) should be considered jointly. Fuzz testing (fuzzing) for the ICP is a common way to discover whether the ICP itself is designed and implemented with flaws and network security vulnerability. Traditional fuzzing methods promote the safety and security testing of ICPs, and many of them have practical applications. However, most traditional fuzzing methods rely heavily on the specification of ICPs, which makes the test process a costly, time-consuming, troublesome and boring task. And the task is hard to repeat if the specification does not exist. In this study, we propose a smart and automated protocol fuzzing methodology based on improved deep convolution generative adversarial network and give a series of performance metrics. An automated and intelligent fuzzing framework BLSTM-DCNNFuzz for application is designed. Several typical ICPs, including Modbus and EtherCAT, are applied to test the effectiveness and efficiency of our framework. Experiment results show that our methodology outperforms the existing ones like General Purpose Fuzzer and other deep learning based fuzzing methods in convenience, effectiveness, and efficiency.