Research on Deep Learning-based Spatio-temporal Feature Fusion Network Intrusion Detection Model
As the number of network attacks increases,network intrusion detection systems are becoming increasingly important in maintaining network security.Most studies have used deep learning approaches for network intrusion detection but have not fully utilized the features of traffic from multiple perspectives.Additionally,these studies often suffer from the use of outdated experimental datasets.In this paper,a parallel-structured DSC-Inception-BiLSTM network is proposed to evaluate the designed network model using state-of-the-art datasets.The model consists of two branches,network traffic image,and text anomaly traffic detection.Spatial and temporal features of traffic are extracted by improved convolutional neural networks and recurrent neural networks,respectively.Finally,network intrusion detection is achieved by fusing spatio-temporal features.The experimental results show that our model achieves 99.96％,99.19％,and 99.95％accuracy on the three datasets of CIC-IDS 2017,CSE-CIC-IDS 2018 and CIC-DDoS 2019,respectively,effectively classifying the anomalous traffic with high precision and meeting the requirements of intrusion detection system.
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