Supervision and early warning of abnormal data in Internet of Things based on unsupervised attention learning

Abstract

With the continuous development of artificial intelligence and big data, more and more communication protocols have appeared in the network connection. Because the network protocol content specification should not need to obtain the content of the common protocol by any means, it faces different attacks, which increases the complexity of network security detection to an unlimited extent. The absolute security analysis of the content of the relevant data protocol is not too limited. Based on the content of the unknown binary number protocol in the Internet of Things, this paper does the reverse research. To address the inefficiency, learning is focused on the inverse aspect of the content processing logic of the protocol for unsupervised execution attention. This paper proposes a method based on the name of the Dirichlet process mixture model normal field and the unknown protocol of the Internet of Things. It also establishes the behavioral criteria that process the logical inference least squares method to infer the content of the target protocol. Based on the original external characteristics of abnormal data in the Internet of Things, the neural network model of attention mechanism and the model checking of variational self-encoder are chosen. An innovative multi-dimensional spatial neural network model with a centralized control mechanism is taken to extract the spatial relevant information from the original external features, which are regarded as the intermediate external features. The intermediate extrinsic features are input to a variational self-encoder for computation and reconstruction. The experimental results verify that the time required by the proposed model is 45% less than that of the traditional structure. The flow processing improves by 36.7% compared with the optimization of the parameter control function. The specific method only relies on the original external characteristics of the Internet of Things traffic and can achieve better data anomaly detection and classification under the condition of not significantly improving the detection time. The abnormal data detection scheme of the Internet of Things designed in this paper not only fills the detection gap of the current mainstream Internet of Things system which lacks an autonomous supervision mechanism, but also effectively blocks external network intrusion and further improves the management authority verification system. It plays an indispensable role in the long-term stable operation of the Internet of Things platform.