Computer network big data detection based on internet of things technology

Abstract

In order to solve the problems of traditional network security intrusion detection system in detection accuracy and missed detection rate, this paper proposes a computer network data detection based on Internet of things technology. This paper designs an intrusion detection system based on data clustering analysis algorithm. The hardware part of the intrusion detection system is composed of six parts: detection module, adaptive module, control management module, risk early warning module, control access module and data acquisition module. Each module is closely connected and updated with the rule base and database in real time; In the software flow design of the system, based on the data clustering analysis algorithm, the collected original data are classified and processed, and the Mings distance is determined, revealing the difference between normal data and abnormal data, and finally realizing the accurate detection of cloud computing network intrusion behavior. The experimental results show that the proposed intrusion detection system has a higher detection accuracy for malicious data, and the average missed detection rate can be controlled below 0.3 %. Conclusionthe detection system designed in this paper has more advantages in stability performance.