Affiliations: [a] School of Higher Vocational Technical, Jilin Provincial Institute of Education, Changchun 130022, China | [b] School of Information, Changchun Vocational and Technical College, Changchun 130031, China | [c] School of Economics and Management, Changchun University of Technology, Changchun 130012, China
Abstract: In order to overcome the problems of poor data classification accuracy and effectiveness of traditional data monitoring methods, this paper designs a data security monitoring method based on narrow-band Internet of things. Firstly, the model of network data acquisition and sensor node’s optimal configuration is established to collect intranet data. Based on the analysis of data characteristics, dynamic intranet data analysis indexes are designed from three aspects: establishing security incident quantity index, establishing address entropy index and data diversion. According to the above-mentioned narrow-band data aggregation rate, the security index of the Internet of things is calculated to realize the security of monitoring data. The experimental results show that: whether the network attack exists or not, the accuracy rate of the method is always higher than 90%, the classification time is less than 4 s, and the energy consumption of monitoring process is always less than 150 J, which fully proves that the method achieves the design expectation.
Keywords: Narrow-band Internet of things, intranet data, security monitoring, analysis index, address entropy, abnormal data screening
