Intrusion Detection Model Based on CapsNet and SRU for Industrial Internet
With the popularization of the industrial Internet,a large number of infrastructure and equipment in industrial systems are connected to the Internet,making industrial systems more vulnerable to external attacks.Industrial Internet intrusion detection has become an important means to ensure the security and stable operation of industrial networks.Existing deep learning methods have problems with incomplete data feature extraction and low accuracy in detecting rare attacks in industrial Internet intrusion detection.Therefore,an industrial Internet intrusion detection model based on the fusion of Capsule Network(CapsNet)and Simple Recurrent Unit(SRU)is proposed.The SMOTE-ENN algorithm is used to complete the balanced processing of data,combining the simple recurrent unit neural network and the capsule that introduces the residual block.The network extracts the temporal and spatial features of the traffic data re-spectively,and weights the features through the self-attention mechanism,thereby improving the detection performance of the model.On the gas pipeline data set,comparing the prediction results before and after processing by the SMOTE-ENN algorithm,the proposed model's recognition accuracy for the MSCI and MFCI categories is increased by4.69 percentage points and4.41 percentage points re-spectively,indicating that the data balancing algorithm improves the classifier's prediction ability for a few categories of samples.Compared with other models,the accuracy of the proposed model reaches 99.36%and the false positive rate is 0.73%.
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