Intrusion detection method based on improved ConvLSTM

Aiming at the weak generalization ability of network intrusion detection model, an intrusion detection method based on Weight-Dropped Convolutional LSTM(WD-ConvLSTM) and Gradient Penalty Wasserstein Generative Adversarial Network (WGAN-GP) is proposed. In terms of data processing, the Principal Component Analysis (PCA) algorithm is used to reduce the dimension of data after normalizing and digitizing the network traffic data. In feature extraction, the WD-ConvLSTM proposed will mine the deep spatial features of high-dimensional data. Finally, the extracted features are input into the softmax function to output the classification results. So as to solve the problem of overfitting caused by unbalanced dataset, WGAN-GP is used for generating attack samples to enhance the generalization ability of the model. The proposed intrusion detection method is verified on the NSL-KDD dataset, and the results show that the proposed method performs better in accuracy and F1 score, whether compared with traditional machine learning methods such as random forest, support vector machine, bayesian, or deep learning methods such as stacked denoising autoencoder and multi-scale convolution neural network.