Cyber Intrusion Prevention for Large-Scale Semi-Supervised Deep Learning Based on Local and Non-Local Regularization

Abstract

The cyber intrusion prevention model represents a new means of cyber protection with intelligent defense capability. It can not only detect intrusion behavior but also respond to such behavior in a timely manner. This study applies deep learning theory and semi-supervised clustering to cyber intrusion prevention technology. Deep learning based on deep structures represents the current development trend of neural networks. Semi-supervised learning uses a large amount of unlabeled cyber traffic data and a small amount of labeled cyber traffic data to achieve cyber intrusion prevention with a low recognition error rate. Discriminative deep belief network (DDBN)-based cyber defense technology has emerged as a research hotspot in the field of cyber intrusion prevention owing to its low error rate. This paper proposes a cyber intrusion prevention technology using DDBN for large-scale semi-supervised deep learning based on local and non-local regularization to overcome the problem of high classification error rates of the cyber intrusion prevention model. Through comparisons with the cyber intrusion prevention results of the Hopfield, support vector machine (SVM), generative adversarial network (GAN), and deep belief network-random forest (DBN-RFS) classifiers, the proposed DDBN model is shown to have the lowest error rate. Thus, the proposed approach can improve the performance of the cyber intrusion prevention system. The training and testing error rates of the exponent loss function with local and non-local regularization (exponent with LNR) are lower than those of the exponent, square, and hinge loss functions. The experimental results show that the running time decreases as the number of hidden layers increases, especially with 6144 and 4096 hidden layer nodes.