An Intrusion Detection Model Based on Deep Belief Networks

This paper focuses on an important research problem of Big Data classification in intrusion detection system. Deep Belief Networks is introduced to the field of intrusion detection, and an intrusion detection model based on Deep Belief Networks is proposed to apply in intrusion recognition domain. T...

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Bibliographic Details
Published in2014 Second International Conference on Advanced Cloud and Big Data pp. 247 - 252
Main Authors Ni Gao, Ling Gao, Quanli Gao, Hai Wang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2014
Subjects
Artificial neural networks
Data models
Deep Belief Networks
Hidden Markov models
Intrusion detection
Machine learning
Restricted Boltzmann Machine
Support vector machines
Training
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DOI10.1109/CBD.2014.41

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Summary:This paper focuses on an important research problem of Big Data classification in intrusion detection system. Deep Belief Networks is introduced to the field of intrusion detection, and an intrusion detection model based on Deep Belief Networks is proposed to apply in intrusion recognition domain. The deep hierarchical model is a deep neural network classifier of a combination of multilayer unsupervised learning networks, which is called as Restricted Boltzmann Machine, and a supervised learning network, which is called as Back-propagation network. The experimental results on KDD CUP 1999 dataset demonstrate that the performance of Deep Belief Networks model is better than that of SVM and ANN.
DOI:10.1109/CBD.2014.41