Research on Intrusion Detection Algorithm Based on Deep Learning and Semi-Supervised Clustering

Abstract

With the development of new internet application technologies, cyberspace security is becoming more important. How to effectively identify network attacks is the core issue of cyberspace security. Deep learning is used in intrusion detection, which can find hidden attacks in intrusion data and then improve the accuracy of detection. Semi-supervised learning uses a small number of labeled data and a large number of unlabeled data to train. It reduces the requirements of the sample. In this paper, an intrusion detection algorithm based on semi-supervised learning and deep learning is proposed to solve the problem of low accuracy in intrusion detection systems. The algorithm uses sparse self-encoder and softmax classifier in deep learning to classify the data and improves the classification performance. Experimental verification is carried out using KDD CUP99 dataset. The experimental results verify the effectiveness of the algorithm.