Abstract: At present,targets of network protocol fuzzing tend to be large protocol entities,and traditional testcase filtering me-thods are mainly based on the running status information of the test object.The larger the test object,the longer it takes to execute a single testcase.Therefore,in view of the problems of long invalid execution time and low efficiency in traditional testcase filtering methods for network protocol fuzzing,a testcase filtering method based on QRNN for network protocol fuzzing is proposed according to strong abilities of recurrent neural network models to process and predict sequence data.The method can automatically filter invalid testcases by learning structural characteristics of the network protocol,including the value range of fields and constraint relationships between fields,and reduce the number of testcases executed by the protocol entity.Experimental results show that,compared with traditional testcase filtering methods for network protocol fuzzing,the proposed method can effectively reduce the time cost of network protocol vulnerability discovery and dramatically improve the efficiency of network protocol fuzzing.

Key words: Testcase filtering, QRNN, Network protocol, Fuzzing test, Deep learning