Learning-based intrusion detection for high-dimensional imbalanced traffic

With the rapid development of Industry 4.0, industrial big data has become a hot topic in the field of smart manufacturing. However, the large-scale data flow generated by industrial IoT also has serious security challenges. This paper proposes a new multi-module intrusion detection system: DWGF-IDS, which consists of three modules: feature extraction, imbalance processing and traffic anomaly detection. Firstly, a deep denoising autoencoder is used to extract the deep feature representation of the data and improve the generalization performance of the detection model by adding noise to the autoencoder. Secondly, a Wasserstein Generative Adversarial Network -Gradient Penalty optimized based on the self-attention mechanism is used to generate a few classes in the anomalous traffic. Finally, the weights and bias values in the deep denoising autoencoder are transferred to the deep neural network structure, and a DNN improved based on focal loss is used to implement multi-classification detection on the reduced dimensional balanced traffic data. The system performance was evaluated using two datasets, namely NSL-KDD and CSE-CIC-IDS-2018. The multi-classification accuracy achieved on these datasets were 85.05% and 99.57%, respectively. The experimental results show that DWGF-IDS effectively copes with the high dimensionality and imbalance of IoT data, improves the detection rate of unknown attacks, and improves the misclassification of rare classes of attack traffic.

Automated summary

This paper proposes a new multi-module intrusion detection system, which effectively deals with the high dimensionality and imbalance of IoT data and improves the detection rate of unknown attacks and the misclassification of rare classes of attack traffic.