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

Author

Listed:
  • Wanyou Lv

    (East China Normal University)

  • Jiawen Xiong

    (East China Normal University)

  • Jianqi Shi

    (East China Normal University)

  • Yanhong Huang

    (East China Normal University)

  • Shengchao Qin

    (University of Teesside
    Shenzhen University)

Abstract

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.

Suggested Citation

  • Wanyou Lv & Jiawen Xiong & Jianqi Shi & Yanhong Huang & Shengchao Qin, 2021. "A deep convolution generative adversarial networks based fuzzing framework for industry control protocols," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 441-457, February.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:2:d:10.1007_s10845-020-01584-z
    DOI: 10.1007/s10845-020-01584-z
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    References listed on IDEAS

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    1. Zhifen Zhang & Shanben Chen, 2017. "Real-time seam penetration identification in arc welding based on fusion of sound, voltage and spectrum signals," Journal of Intelligent Manufacturing, Springer, vol. 28(1), pages 207-218, January.
    2. Mahardhika Pratama & Eric Dimla & Chow Yin Lai & Edwin Lughofer, 2019. "Metacognitive learning approach for online tool condition monitoring," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1717-1737, April.
    3. Ehsan Pourjavad & Rene V. Mayorga, 2019. "A comparative study and measuring performance of manufacturing systems with Mamdani fuzzy inference system," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1085-1097, March.
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    Cited by:

    1. Han, Kunlun & Yang, Kai & Yin, Linfei, 2022. "Lightweight actor-critic generative adversarial networks for real-time smart generation control of microgrids," Applied Energy, Elsevier, vol. 317(C).

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