Research on Machine Learning Algorithm for Internet of Things Information Security Management System Research and Implementation

Abstract

To study in the Internet information security and privacy problem, a method based on IoT card monitoring technology based on machine learning, the technology can use fuzzy $c$ -means algorithm for online business audit, using the Naive Bayes algorithm to classify Internet content and text messages, on the analysis of the comparison of the measurement method based on distance and based on the similarity. The concept of divergence in information theory is used to measure the difference between probability distributions. Finally, the feasibility of the proposed method is validated using the data from the machine learning database. The experimental results show that under the same privacy gain, the data availability relationship between the three anonymous protection methods is roughly satisfied as follows: when the privacy gain is less than 0.85, $U_{\text{dverity}}<U_{\text{emonymity}}<U_{\text{eclorenes}}$ ; when the privacy gain is greater than 0.85, $U_{\text{uxanaymiy}}<U_{\text{uiversriy}}<U_{\text{closencs}}$ . And it proves that on the premise of ensuring efficiency and accuracy, the system can find a large number of illegal IoT cards and effectively guarantee the security of IoT.