关键词: 受限玻尔兹曼机, 入侵检测, 深度信念网络, softmax分类器, 自适应学习速率, 特征学习

Abstract:

In order to solve the problem that the traditional intrusion detection methods are difficult to quickly and accurately extract feature information from massive unlabeled network data to identify abnormal intrusions, this paper proposes an intrusion detection method of Softmax Classification based on the Improved Deep Belief Network（IDBN-SC）, where the deep belief network is employed for conducting unsupervised feature learning on the original network data and the adaptive learning rate is used to reduce the time required to train the network model. Moreover, the softmax classifier is utilized to identify the types of network attack. Testing on the NSL-KDD dataset shows that the IDBN-SC method not only improves the recognition accuracy by an average of 3.02%, but also reduces the softmax classifier training time by 5.58 s.

Key words: restricted Boltzmann machine, intrusion detection, deep belief network, softmax classifier, adaptive learning rate, feature learning