Abstract
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.
Highlights
Scientific Programming cases, which will affect the coverage of the test, and it needs to be improved. erefore, we compared the advantages and disadvantages of the three methods, combined with the data packet structure characteristics of the test protocol, and propose a new method based on weight division to calculate the case similarity and the use case average similarity. e goal is to generate use cases with better coverage and improve test efficiency
(ii) Different weight and distance calculation methods are set according to different protocol fields, so the similarity can be determined more accurately according to the function and data content of the use case. e change of coding method solves the problem of inaccurate similarity judgment caused by data mutation
(iii) e genetic algorithm is used to generate the use case, and the similarity and the population dispersion of the case are used as the fitness function of the genetic algorithm
Summary
A test case generation technique based on mutation algorithm of precaptured IPC data is introduced in [28] in order to improve the fuzzing test efficiency. E model proposes a fuzzy security test algorithm, combined with the characteristics of the industrial control protocol, and elaborates on the analysis tree structure, test case generation, and mutation strategy. A fuzzing test data generation method was proposed in [32] based on dynamic construction of mutation strategy. E method was designed to use the feedback information of instrumentation to dynamically construct the control mutation strategy and the keyword mutation strategy and to guide the fuzzer to generate test data with high coverage. Industrial control protocol are analyzed, test cases are designed based on the construction of description model, and coding method of use cases is designed for genetic algorithm. In order to improve test coverage and vulnerability discovery rate, the concept of dangerous point is proposed, and, based on this, a composite fitness function is designed to monitor and adjust the state of use case population
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