[1] Gao Ni, Gao Ling, He Yiyue, Gao Quanli, et al. Intrusion detection model based on deep belief nets [J]. Journal of Southeast University (English Edition), 2015, 31 (3): 339-346. [doi:10.3969/j.issn.1003-7985.2015.03.007]
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Intrusion detection model based on deep belief nets(
)
)Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]
- Volumn:
- 31
- Issue:
- 2015 3
- Page:
- 339-346
- Research Field:
- Computer Science and Engineering
- Publishing date:
- 2015-09-20
Info
- Title:
- Intrusion detection model based on deep belief nets
- Author(s):
- Gao Ni1; Gao Ling1; He Yiyue1; 2; Gao Quanli1; Ren Jie1
-
1School of Information Science and Technology, Northwest University, Xi’an 710127, China
2School of Economics and Management, Northwest University, Xi’an 710127, China
- PACS:
- TP393.08
- DOI:
- 10.3969/j.issn.1003-7985.2015.03.007
- Abstract:
- 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.
References:
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[6] Hinton G E, Osindero S, Teh Y W. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7):1527-1554.
[7] Bengio Y, Lamblin P, Popovici D, et al. Greedy layer-wise training of deep networks[C]//Advances in Neural Information Processing Systems. Vancouver, Canada, 2006:153-160.
[8] Stolfo S J, Fan W, Lee W K, et al. Cost-based modeling for fraud and intrusion detection: results from the JAM project [EB/OL].(1999-10-28)[2011-06-27]. http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html.
[9] Rumelhart D E, Hinton G E, Williams R J. Learning representations by back-propagating errors[J]. Nature, 1986, 323(6088):533-536.
[10] Hinton G E. Training products of experts by minimizing contrastive divergence[J]. Neural Computation, 2002, 14(8):1771-1800
[11] Larochelle H, Bengio Y, Louradour J, et al. Exploring strategies for training deep neural neural networks [J]. Journal of Machine Learning Research, 2009, 10(1):1-40.
Memo
- Memo:
-
Biographies: Gao Ni(1982—), female, graduate; Gao Ling(corresponding author), male, doctor, professor, gl@nwu.edu.cn.
Foundation items: The National Key Technology R&D Program during the 12th Five-Year Plan Period(No.2013BAK01B02), the National Natural Science Foundation of China(No.61373176), the Scientific Research Projects of Shaanxi Educational Committee(No.14JK1693).
Citation: Gao Ni, Gao Ling, He Yiyue, et al.Intrusion detection model based on deep belief nets[J].Journal of Southeast University(English Edition), 2015, 31(3):339-346.[doi:10.3969/j.issn.1003-7985.2015.03.007]