ACGANs-CNN: A Novel Intrusion Detection Method

Abstract In this paper, an intrusion detection model (ACGANs-CNN) method based on GAN and CNN fusion is proposed for the reasons that unknown attack sample data cannot be provided in training samples, the number of training samples is limited, and known attack sample types account for less such small sample data. The model converts network traffic data into grayscale images, generates the same proportion of attack samples by generating the counter network, ensures the uniform distribution of attack samples in the training set, and introduces the gradient penalty function to improve the stability of the training model. Secondly, CNN is used to better extract sample features. In order to prevent overfitting, the nonlinear activation function Relu and Dropout method are introduced. At the same time, the convergence speed of the model is accelerated, and the detection efficiency of the model is improved. Attention is introduced to highlight the key features and to classify samples based on these key features. In this paper, the KDDCUP99 data set is used for model evaluation. Experimental results show that this algorithm (ACGANs-CNN) has stronger model training stability, higher quality of generated fake samples, and better feature extraction effect in small sample data. Its detection rate and accuracy of attack types are significantly higher than that of traditional machine learning algorithms such as SVM, KNN, RF, and other CNN models.