Data mining based network intrusion detection method in the environment of IoT

With the rapid development of Internet of Things (IoT) and Internet technology, the network is becoming more and more important and strict. The IoT networks that access to a large number of devices make network intrusion more convenient. However, because the traditional network intrusion detection methods have the problems of low detection rate and high false alarm rate, this paper proposes a network intrusion detection method based on data mining. Firstly, a pre-processing model is established to process the initial data. Then, according to the characteristics of network behavior, a new feature subset selection process is designed. Based on the above data mining and analysis process, a dynamic detection generation rule matching with data mining is set to detect abnormal network intrusion traffic. Then, the network intrusion detection based on data mining is completed. The experimental results show that, compared with other methods, the network intrusion detection method based on data mining has higher network intrusion detection rate and lower false alarm rate, which is helpful to improve the detection accuracy.