Network Anomaly Traffic Detection Model Based on Spatio-Temporal Attention Feature Fusion

With the rapid proliferation of 5G, IoT devices are increasingly subjected to network attacks. Traditional Network Intrusion Detection Systems (NIDS) are becoming inadequate in the face of more complex network environments and massive data traffic. Deep learning-based intrusion detection algorithms have thus become a hot topic in cybersecurity research. However, existing NIDS have limitations in terms of accuracy, recall rates, false alarm rates, and generalization capabilities. The impact of data redundancy and data imbalance further degrades model performance. We have designed a new network that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, incorporating an attention mechanism to enhance learning capabilities. This allows the model to focus on both temporal and spatial features. Additionally, the introduction of the attention mechanism makes it easier to identify key anomalous data features amidst the redundant data. We also paid special attention to the issue of data imbalance. Our model and methods for balancing datasets were validated through binary and multi-class classification experiments on the two most commonly used datasets (UNSW_NB15, CICIDS2017). The results demonstrated good convergence and high accuracy. Compared to traditional models, our model shows significant improvements in detecting large-scale and multi-scenario network data attacks, making it suitable for network security detection in modern IoT environments.