Industrial Internet Intrusion Detection Method based on Cloud-Edge Collaboration
Jinhai Song, Zhiyong Zhang · Frontiers in Science and Engineering · 2023
Industrial Internet security incidents occur frequently, and the amount of industrial data is increasing exponentially. Efficient and correct detection of attacks is critical to industrial Internet security. The method is based on the concept of cloud-edge collaboration to detect malicious behaviors. Firstly, the data is normalized and preprocessed to reduce the differences caused by different feature scales, then the deep neural network(DNN) is used to extract the features of massive data, and finally the softmax function is used for classification. In order to verify the effectiveness of the model, it is evaluated on the NSL-KDD dataset and the GAS dataset, and compared with other traditional models, the model has higher precision and recall. This method integrates edge-cloud collaboration and deep learning models, which can effectively reduce edge load and improve model performance, and has a good application prospect.