NIDS-CNNLSTM: Network Intrusion Detection Classification Model Based on Deep Learning

Abstract

Intrusion detection is the core topic of network security, and the intrusion detection algorithm based on deep learning has become a research hotspot in the field of network security. In this paper, a network intrusion detection classification model (NIDS-CNNLSTM) based on deep learning is constructed for the wireless sensing scenario of the Industrial Internet of Things (IIoT) to effectively distinguish and identify network traffic data and ensure the security of the equipment and operation of the IIoT. NIDS-CNNLSTM combines the powerful learning ability of long short-term memory neural networks in time series data, learns and classifies the features selected by the convolutional neural network, and verifies the applicability based on binary classification and multi-classification scenarios. The model is trained using KDD CUP99, NSL_KDD and UNSW_NB15 classic datasets. The verification accuracy and training loss on the three datasets all show good convergence and level, and the accuracy rate is high when classifying various types of traffic. The overall performance of NIDS-CNNLSTM has been significantly improved compared with the models proposed in previous studies. The effectiveness shows a high detection rate and classification accuracy and a low false alarm rate through experimental results. It is more suitable for large-scale and multi-scenario network data in the IIoT.