Intrusion detection model based on deep belief nets

This paper focuses on the intrusion classification of huge amounts of data in a network intrusion detection system. An intrusion detection model based on deep belief nets (DBN) is proposed to conduct intrusion detection, and the principles regarding DBN are discussed. The DBN is composed of a multiple unsupervised restricted Boltzmann machine (RBM) and a supervised back propagation (BP) network. First, the DBN in the proposed model is pre-trained in a fast and greedy way, and each RBM is trained by the contrastive divergence algorithm. Secondly, the whole network is fine-tuned by the supervised BP algorithm, which is employed for classifying the low-dimensional features of the intrusion data generated by the last RBM layer simultaneously. The experimental results on the KDD CUP 1999 dataset demonstrate that the DBN using the RBM network with three or more layers outperforms the self-organizing maps (SOM) and neural network (NN) in intrusion classification. Therefore, the DBN is an efficient approach for intrusion detection in high-dimensional space. ©, 2015, Southeast University. All right reserved.