Recognition of Network Security Situation Elements Based on Depth Stack Encoder and Back Propagation Algorithm

Guang KOU; Shuo WANG; Da ZHANG

doi:10.11999/JEIT181014

Volume 41 Issue 9

Sep. 2019

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(9): 2187-2193

Guang KOU, Shuo WANG, Da ZHANG. Recognition of Network Security Situation Elements Based on Depth Stack Encoder and Back Propagation Algorithm[J]. Journal of Electronics & Information Technology, 2019, 41(9): 2187-2193. doi: 10.11999/JEIT181014

Citation:

Guang KOU, Shuo WANG, Da ZHANG. Recognition of Network Security Situation Elements Based on Depth Stack Encoder and Back Propagation Algorithm[J]. Journal of Electronics & Information Technology, 2019, 41(9): 2187-2193. doi: 10.11999/JEIT181014

Citation:

PDF( 1890 KB)

Recognition of Network Security Situation Elements Based on Depth Stack Encoder and Back Propagation Algorithm

doi: 10.11999/JEIT181014

Guang KOU^{1
,
,},
Shuo WANG²,
Da ZHANG²

1.
Artificial Intelligence Research Center, National Innovation Institute of Defense Technology, Beijing 100072, China
2.
Information Engineering University, Zhengzhou 450001, China

Funds: The National Natural Science Foundation of China (61303074)

Received Date: 2018-11-05
Rev Recd Date: 2019-03-18

Available Online: 2019-04-16

Publish Date: 2019-09-10

Abstract

Abstract

The basis of the identification of network security situation element is to perform the feature extraction of situation data effectively. Considering the problem that the Back Propagation(BP) neural networks have excessive dependence on data labels when it has a learning of massive security situation information data, a network security situation element identification method is proposed, which combines deep stack encoder and BP algorithm. It trains the network layer by layer through unsupervised learning algorithm. On this basis the deep track encoder by stacking can be obtained. The unsupervised training of the network is realized when using the encoder to extract the characteristic of the data sets. It is verified by simulation experiments that the method can improve the performance and accuracy of situational awareness effectively.
- Network security situation,
- Back Propagation(BP) neural network,
- Stack encoder,
- Data analysis

FullText(HTML)

References(16)

References

国家计算机网络应急技术处理协调中心. 2017年我国互联网网络安全态势综述[EB/OL]. http://www.cert.org.cn/publish/main/upload/File/situation.pdf, 2018.

National Internet Emergency Center. Summary of China’s Internet security situation in 2018[EB/OL]. http://www.cert.org.cn/publish/main/upload/File/situation.pdf, 2018.

SRIHARI R K. Situation awareness through concept-based information extraction[EB/OL]. http://www.dawnbreaker.com/vas05, 2015.

ZHANG Songmei, YAO Shan, YE Xin'en, et al. A network security situation analysis framework based on information fusion[C]. The 6th IEEE Joint International Information Technology and Artificial Intelligence Conference, Chongqing, China, 2011: 326-332. doi: 10.1109/ITAIC.2011.6030216.

韦勇, 连一峰, 冯登国. 基于信息融合的网络安全态势评估模型[J]. 计算机研究与发展, 2009, 46(3): 353–362.

WEI Yong, LIAN Yifeng, and FENG Dengguo. A network security situational awareness model based on information fusion[J]. Journal of Computer Research and Development, 2009, 46(3): 353–362.

陈秀真, 郑庆华, 管晓宏, 等. 层次化网络安全威胁态势量化评估方法[J]. 软件学报, 2006, 17(4): 885–897.

CHEN Xiuzhen, ZHENG Qinghua, GUAN Xiaohong, et al. Quantitative hierarchical threat evaluation model for network security[J]. Journal of Software, 2006, 17(4): 885–897.

LIU Zhiming, LI Sheng, HE Jin, et al. Complex network security analysis based on attack graph model[C]. The 2nd International Conference on Instrumentation, Measurement, Computer, Communication and Control, Harbin, China, 2012: 183–186. doi: 10.1109/IMCCC.2012.50.

HINTON G E, OSINDERO S, and TEH Y W. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7): 1527–1554. doi: 10.1162/neco.2006.18.7.1527

ERHAN D, BENGIO Y, COURVILLE A, et al. Why does unsupervised pre-training help deep learning?[J]. The Journal of Machine Learning Research, 2010, 11: 625–660.

BENGIO Y. Learning deep architectures for AI[J]. Foundations and Trends in Machine Learning, 2009, 2(1): 1–127. doi: 10.1561/2200000006

VINCENT P, LAROCHELLE H, LAJOIE I, et al. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion[J]. The Journal of Machine Learning Research, 2010, 11: 3371–3408.

RIFAI S, VINCENT P, MULLER X, et al. Contractive auto-encoders: Explicit invariance during feature extraction[C]. The 28th International Conference on Machine Learning, New York, USA, 2011: 122-132.

EVANS R and GREFENSTETTE E. Learning explanatory rules from noisy data[J]. Journal of Artificial Intelligence Research, 2018, 61: 1–64. doi: 10.1613/jair.5714

BRONSTEIN M M, BRUNA J, LECUN Y, et al. Geometric deep learning: Going beyond Euclidean data[J]. IEEE Signal Processing Magazine, 2017, 34(4): 18–42. doi: 10.1109/MSP.2017.2693418

LIPPMANN R, HAINES J W, FRIED D J, et al. The 1999 DARPA off-line intrusion detection evaluation[J]. Computer Networks, 2000, 34(4): 579–595. doi: 10.1016/S1389-1286(00)00139-0

SHIRAVI A, SHIRAVI H, TAVALLAEE M, et al. Toward developing a systematic approach to generate benchmark datasets for intrusion detection[J]. Computers& Security, 2012, 31(3): 357–374. doi: 10.1016/j.cose.2011.12.012

KONIDARIS G, KAELBLING L P, and LOZANO-PEREZ T. From skills to symbols: Learning symbolic representations for abstract high-level planning[J]. Journal of Artificial Intelligence Research, 2018, 61: 215–289. doi: 10.1613/jair.5575

Relative Articles

Supplements(0)

Cited By

Proportional views