The Application of Machine Learning Models in Network Protocol Vulnerability Mining

Abstract

With the development of society, humans are becoming more and more dependent on the Internet. And there are exploitable vulnerabilities in network sharing protocol vulnerabilities that will cause great risks to individuals and society. Therefore, vulnerability mining technology has developed into an important research problem in the field of information security. To this end, this paper uses fuzzy testing method for vulnerability mining of network protocols. The fuzzy testing technique performs vulnerability mining by sending a large amount of abnormal data to the test target and monitoring whether the software system is working properly. The vulnerability mining approach in this paper prioritizes the need to analyze and model the protocol format and generate a large number of test cases by using fuzzy test values to vary the boundaries of different parts of the protocol. These test cases are then sent to the test target, and the network state and process state of the test target are monitored in real time. Finally, if the test cases trigger a vulnerability, the system automatically records the test case information as well as the vulnerability information. The test cases evaluated by machine learning as easy to trigger vulnerabilities are sent to the test H-target, which can save the time of vulnerability mining for everyone and improve the efficiency of vulnerability mining. The vulnerability mining technology researched in this paper is of great significance to network security, which can prevent problems before they occur, discover vulnerabilities in the network in time, take effective measures to prevent them, and possibly avoid the spread of some major network vulnerability viruses.