Field classification-based novel fuzzing case generation for ICS protocols

Kim, Sung Jin; Shon, Taeshik

doi:10.1007/s11227-017-1980-3

Cited by 14 publications

(3 citation statements)

References 16 publications

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“…where c n max is the maximum value of the field in the population; c n min is the minimum value of the field in the population. e sum of each field is averaged to calculate the mean center test case, as shown in equation 10, and the calculation method of each field is as in equation (11).…”

Section: Average Similarity and Population Dispersion Of Test Casesmentioning

confidence: 99%

“…At present, the commonly used vulnerability mining techniques are divided into static analysis, dynamic analysis, binary comparison, fuzzy testing, and so on [6][7][8][9][10][11][12]. Fuzzy testing has the advantages of high automation, low system consumption, low false-alarm rate, and being independent of the source code of the object program [7].…”

Section: Introductionmentioning

confidence: 99%

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A Test Cases Generation Method for Industrial Control Protocol Test

Shang

Zhang

Wang

et al. 2021

Scientific Programming

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The coverage of test cases is an important indicator for the security and robustness test of industrial control protocols. It is an important research topic to complete the test with less use cases. Taking Modbus protocol as an example, a calculation method of case similarity and population dispersion based on weight division is proposed in this paper. The method can describe the similarity of use cases and the dispersion degree of individuals in the population more accurately. Genetic algorithm is used to generate and optimize test cases, and individual similarity and population dispersion are used as fitness functions of genetic algorithm. Experimental results show that the proposed method can increase the population dispersion by 3.45% compared with the conventional methods and effectively improve the coverage of test cases.

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Section: Average Similarity and Population Dispersion Of Test Casesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Test Cases Generation Method for Industrial Control Protocol Test

Shang

Zhang

Wang

et al. 2021

Scientific Programming

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show abstract

“…Shapiro et al [14] used the arpsk tool to perform address resolution protocol spoofing, achieving bidirectional master-slave fuzzing between SCADA clients and servers with the LZFuzz tool. Kim and Shon [15] used coding feature analysis to point out that the MMS protocol in smart grids has syntactic similarities with related ICPs, and designed a cross-domain fuzzing method based on field classification based on this feature. Liu et al [16] introduced a traffic-driven protocol fuzzing scheme for programmable controllers, which involves selecting seeds and bootstrapping states to execute proprietary protocol fuzzing on network daemons.…”

Section: Introductionmentioning

confidence: 99%

DiffusionFuzz: Fuzzing Framework of Industrial Control Protocols Based on Denoising Diffusion Probabilistic Model

Zong,

Luo,

Ning

et al. 2024

IEEE Access

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With the opening of industrial networks in the information age, the characteristic of Industrial Control Protocols (ICPs) to transmit plaintext without encryption exposes serious security risks, threatening the safe and stable operation of Industrial Control Systems (ICSs). Exploring the work of mining vulnerabilities in ICPs can use fuzzing to mine potential vulnerabilities in protocols to ensure the safe operation of ICS. However, traditional fuzzing methods require the construction of test cases based on expert experience and the format syntax specification of ICPs. This process is time-consuming, labor-intensive, inefficient, and limited when facing unknown ICPs. In response to these issues, this paper proposes an automated fuzzing method for ICPs based on the Denoising Diffusion Probabilistic Model (DDPM). Specifically, DDPM achieves the conversion from noise to data samples, which can easily and quickly generate test cases. On this basis, we designed a universal fuzzing framework, DiffusionFuzz, that can be applied to most ICPs. The experimental results obtained on ICPs such as Modbus/TCP in the Industrial Attack-Defense Range of the Key Laboratory of Information Security for Petrochemical Industry in Liaoning Province demonstrate that the test cases generated by this method are diverse, and outperform other fuzzing methods in terms of acceptance rate and ability to trigger exceptions. Certainly, DiffusionFuzz can enhance the effectiveness of fuzzing, identify vulnerabilities in ICPs, and thereby reduce potential economic risks and impacts.INDEX TERMS Vulnerability mining, Fuzzing, Industrial control protocols, Denoising diffusion probabilistic model.

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IPSpex: Enabling Efficient Fuzzing via Specification Extraction on ICS Protocol

Sun

You

et al. 2022

Lecture Notes in Computer Science

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Field classification-based novel fuzzing case generation for ICS protocols

Cited by 14 publications

References 16 publications

A Test Cases Generation Method for Industrial Control Protocol Test

A Test Cases Generation Method for Industrial Control Protocol Test

DiffusionFuzz: Fuzzing Framework of Industrial Control Protocols Based on Denoising Diffusion Probabilistic Model

IPSpex: Enabling Efficient Fuzzing via Specification Extraction on ICS Protocol

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