Data augmentation in network intrusion detection based on S-DCGAN

Aiming at the low detection accuracy problem of small samples in network intrusion detection, this paper researches the data expansion method based on the generative adversarial network algorithm. The data sample is generated to supplement the small data set sample, which is helpful to improve the accuracy of network intrusion detection. Firstly, the non-numerical data in the original data set is converted into numerical data by one-hot coding, and reconstructed them into two-dimensional data. Secondly, this paper proposes an S-DCGAN algorithm to generate data. Initially, we improve the network structure of deep convolutional generative adversarial networks (DCGAN). S_ReLU function is used instead of the ReLU function as the activation function after each convolutional layer of the adversarial network, which makes the quality of the generated data better and the effect of the generated data more stable. Furthermore, we improve the loss function of generation model and discriminant model of DCGAN. A simplified total variation loss function is added to the loss function of the generated model to reduce the noise of the generated data. The gradient penalty term is added to the loss function of the discriminant model to alleviate the problem of gradient explosion or gradient disappearance and improve the stability of network training. In order to verify its effectiveness, various classifiers are used to evaluate the performance of the proposed data augmentation method. The results show the accuracies for classifiers are increased after employing the S-DCGAN-based data augmentation techniques.