Strengthening Network Security: Deep Learning Models for Intrusion Detection with Optimized Feature Subset and Effective Imbalance Handling

In recent years, frequent network attacks have highlighted the importance of efficient detection methods for ensuring cyberspace security. This paper presents a novel intrusion detection system consisting of a data preprocessing stage and a deep learning model for accurately identifying network attacks. We have proposed four deep neural network models, which are constructed using architectures such as Convolutional Neural Networks (CNN), Bi-directional Long Short-Term Memory (BiLSTM), Bidirectional Gate Recurrent Unit (BiGRU), and Attention mechanism. These models have been evaluated for their detection performance on the NSL-KDD dataset.To enhance the compatibility between the data and the models, we apply various preprocessing techniques and employ the particle swarm optimization algorithm to perform feature selection on the NSL-KDD dataset, resulting in an optimized feature subset. Moreover, we address class imbalance in the dataset using focal loss. Finally, we employ the BO-TPE algorithm to optimize the hyperparameters of the four models, maximizing their detection performance. The test results demonstrate that the proposed model is capable of extracting the spatiotemporal features of network traffic data effectively. In binary and multiclass experiments, it achieved accuracy rates of 0.999158 and 0.999091, respectively, surpassing other state-of-the-art methods.