With the rapid development of Internet of Things (IoT) technology, network security has become increasingly prominent, especially in the security issues of data transmission, storage, and other links. This article intends to use data mining methods to conduct in-depth analysis and mining of the transmission characteristics of data in the Internet of Things, in order to improve the level of information security management in the Internet of Things. This article establishes a data flow model for the Internet of Things, and uses methods such as association rule learning and abnormal behavior detection in data mining to identify security risks in the Internet of Things. By using machine learning and other methods, deep mining of data in the network can be carried out, and real-time monitoring of abnormal behavior in the network can be carried out to cope with various security attacks and threats. In addition, this article can also introduce technologies such as cryptography and access control to ensure the security and integrity of data in transmission, storage, and other aspects. The detection speed of various types of malware varies, but most of them are between 30 s and 70 s, indicating that the system has a fast response ability to different types of malware. The research results of this article have important theoretical significance and application value for ensuring the security of IoT information, maintaining network stability, and protecting the privacy of user information.