An Intrusion Detection Method based on Fusion Neural Network

Abstract

Aiming at the problems of class imbalance, insufficient feature learning, weak generalization ability, and representation capability in existing intrusion detection models, we propose a multi-scale feature fusion Intrusion Detection Model (MSFF). This model combines multi-scale one-dimensional convolution and bidirectional long short-term memory (LSTM) networks, and incorporates residual connections with identity mappings to address the problem of network degradation. The multi-scale convolution captures feature representations at different levels, thereby improving the expressive power of the model. The WGAN-GP algorithm is employed to augment the minority samples and balance the dataset. By performing convolution operations and extracting local window features and global features using bidirectional LSTM units, the model effectively captures temporal information and long-term dependencies. Experimental results demonstrate significant performance improvement compared to a single model. The MSFF model achieves an accuracy of 99.50% and 94.73% in binary classification experiments on the NSL-KDD and UNSW-NB15 datasets, respectively, and an accuracy of 99.50% and 83.78% in multi-class classification experiments.