Embedded Gateway Security Detection Technology Based on the Deep Neural Network Rule Extraction

Abstract

Aiming at the network security problem of power system cable trench control industrial Internet system, we studied an intrusion detection method applied to the embedded industrial Internet of Things gateway. This method extracts rules from the DBN-DNN deep neural network to obtain intrusion detection models that are conducive to integration into embedded systems. We first use the DBN network to reduce the dimensionality of the data, then use the DNN to train the classification model, and extract the rules from the DNN’s neurons to form a rule tree for intrusion detection. The KDD CUP99 training database is used to verify the feasibility of the method, and the test is carried out in the embedded gateway. The results show that the detection method based on rule extraction used in this paper can ensure detection efficiency and accuracy compared to the traditional detection methods. At the same time, it saves more computing resources and is more conducive to integration in embedded gateway systems.