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Vulnerability Mining Method for the Modbus TCP Using an Anti-Sample Fuzzer

College of Computer Science, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
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Sensors 2020, 20(7), 2040; https://doi.org/10.3390/s20072040
Received: 5 March 2020 / Revised: 1 April 2020 / Accepted: 3 April 2020 / Published: 5 April 2020
(This article belongs to the Section Internet of Things)
Vulnerability mining technology is used for protecting the security of industrial control systems and their network protocols. Traditionally, vulnerability mining methods have the shortcomings of poor vulnerability mining ability and low reception rate. In this study, a test case generation model for vulnerability mining of the Modbus TCP based on an anti-sample algorithm is proposed. Firstly, a recurrent neural network is trained to learn the semantics of the protocol data unit. The softmax function is used to express the probability distribution of data values. Next, the random variable threshold and the maximum probability are compared in the algorithm to determine whether to replace the current data value with the minimum probability data value. Finally, the Modbus application protocol (MBAP) header is completed according to the protocol specification. Experiments using the anti-sample fuzzer show that it not only improves the reception rate of test cases and the ability to exploit vulnerabilities, but also detects vulnerabilities of industrial control protocols more quickly. View Full-Text
Keywords: industrial control system; Modbus TCP; probability distribution; recurrent neural network; vulnerability mining industrial control system; Modbus TCP; probability distribution; recurrent neural network; vulnerability mining
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Lai, Y.; Gao, H.; Liu, J. Vulnerability Mining Method for the Modbus TCP Using an Anti-Sample Fuzzer. Sensors 2020, 20, 2040.

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