Abstract
The Internet of Things (IoT) is vulnerable to network attacks due to the real-time, open and interactive characteristics, thus causing network security risks. This makes the intrusion detection technology face new challenges of high precision and low latency. In this paper, we propose a hybrid network intrusion detection model (ACNNBN-LSTM) based on CNN-LSTM and attention mechanism to classify network intrusions in real-time. The model consists of ACNNBN and LSTM modules. In the ACNNBN module, we introduce the convolution block attention module (CBAM) and batch normalization to recognize spatial features of two-dimensional images. The LSTM module learns the temporal features of feature vectors through time series to realize real-time detection. We use the CIRA-CIC-DoHBrw-2020 unbalanced dataset to evaluate model performance. Six machine learning algorithms are selected to compare in evaluation indexes of F1 score, recall ratio, precision ratio and accuracy. Experimental results demonstrate that the ACNNBN-LSTM model is preferable than the other six models in indicators of F1 score, recall rate and accuracy. And the accuracy of our model reached 99.41\(\%\).
This work was supported in part by National Natural Science Foundation of China under Grant 61902222, Grant 61702307, and the Taishan Scholars Program of Shandong Province under Grant tsqn201909109.
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Mu, J., He, H., Li, L., Pang, S., Liu, C. (2022). A Hybrid Network Intrusion Detection Model Based on CNN-LSTM and Attention Mechanism. In: Cao, C., Zhang, Y., Hong, Y., Wang, D. (eds) Frontiers in Cyber Security. FCS 2021. Communications in Computer and Information Science, vol 1558. Springer, Singapore. https://doi.org/10.1007/978-981-19-0523-0_14
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