Abstract:
Fuzzy Test has good applicability in the exploitation of vulnerabilities in industrial control protocols. However, the traditional fuzzy test has the disadvantages of large test workload and a high failure rate. In order to solve these problems, it design an industrial control protocol fuzzy tester GA-fuzzer which combines genetic algorithm and fuzzy test. and propose the concepts of dangerous points and case space model based on dimensional transformation. In GA-fuzzer, it constructed a more efficient dynamic fitness function, and design dynamic mutation and crossover operators to optimize test cases. In the same experimental environment, it used open source fuzzy test method Peach and GA-Fuzzer to test the target. The results show that GA-fuzzer can effectively improve the premature convergence problem of traditional genetic algorithm, and compared to Peach, the number of cases used to achieve the same test expectation was reduced by 27.20% and the test time was reduced by 34.82%.