A Test Cases Generation Method for Industrial Control Protocol Test

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.

An optimization technique for intrusion detection of industrial control network vulnerabilities based on BP neural network

“…According Here, the detection rate of the intrusion detection method in this paper is compared with the following three methods, as shown in Figure 2. The training time of the BP neural network optimized by Adaboost is 3.256s, while the training time of the traditional BP neural network is 3.912s [14][15] .The difference in training time is mainly due to the fact that Adaboost reduces the effect of a single BP neural network falling into the local optimum [16] .…”