A Data-Driven Approach for Network Intrusion Detection and Monitoring Based on Kernel Null Space-Reference-Cited by-同舟云学术

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A Data-Driven Approach for Network Intrusion Detection and Monitoring Based on Kernel Null Space

Published:2019 Issue: Volume: Page:130-140
ISSN:1867-8211
Container-title:Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
language:
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Author:

Truong Thu Huong,Ta Phuong Bac,Nguyen Quoc Thong,Du Nguyen Huu,Tran Kim Phuc

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-30149-1_11

Reference12 articles.

1. Bodesheim, P., Freytag, A., Rodner, E., Denzler, J.: Local novelty detection in multi-class recognition problems. In: 2015 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 813–820. IEEE (2015)

2. Bodesheim, P., Freytag, A., Rodner, E., Kemmler, M., Denzler, J.: Kernel null space methods for novelty detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3374–3381 (2013)

3. Borkar, A., Donode, A., Kumari, A.: A survey on intrusion detection system (IDS) and internal intrusion detection and protection system (IIDPS). In: International Conference on Inventive Computing and Informatics (ICICI), pp. 949–953. IEEE (2017)

4. Liu, J., Lian, Z., Wang, Y., Xiao, J.: Incremental kernel null space discriminant analysis for novelty detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 792–800 (2017)

5. Mercado, G.R., Conerly, M.D., Perry, M.B.: Phase i control chart based on a kernel estimator of the quantile function. Qual. Reliab. Eng. Int. 27(8), 1131–1144 (2011)

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