Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 778

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

A survey of deep learning-based network anomaly detection

Kwon, Donghwoon, Kim, Hyunjoo, Kim, Jinoh, Suh, Sang C., Kim, Ikkyun, Kim, Kuinam J.·2019·Cluster Computing

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1007/s10586-017-1117-8

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 2 references

Research works printed but not linked: 41

  1. Semente: 2016 Internet Security Threat Report (ISTR), vol. 21, p. 8, April 2016
  2. Li, Y., Ma, R., Jiao, R.: Hybrid malicious code detection method based on deep learning. Int. J. Secur. Appl. 9(5), 205–216 (2014)
  3. Salama, M.A., Eid, H.F., Ramadan, R.A., Darwish, A., Hassanien, A.E.: Hybrid intelligent intrusion detection scheme. Soft Comput. Ind. Appl. 96, 293–303 (2011) · DOI · Paper page
  4. Niyaz, Q., Sun, W., Javaid, A.Y., Alam, M.: A deep learning approach for network intrusion detection system. In: 9th EAI International Conference on Bio-Inspired Information and Communications Technologies, pp. 1–11, May 2016
  5. Ahmed, A.: Signature-based network inrusion detection system using JESS(SNIDJ). Graduate Project Technical Report, TAMUCC, pp. 2–6 (2004)
  6. Ning, P., Jajodia, S.: Intrusion detection techniques. The Internet Encyclopedia. doi: 10.1002/047148296X.tie097 · DOI
  7. Najafabadi, M.M., Villanustre, F., Khoshgoftaar, T.M., Seliya, N., Wald, R., Muharemagic, E.: Deep learning applications and challenges in big data analytics. J. Big Data 2(1), 1 (2015) · DOI · Paper page
  8. Deng, L., Yu, D.: Deep learning: methods and applications. Found. Trends Signal Process. 7(3–4), 197–387 (2014) · DOI · Paper page
  9. Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the KDD CUP 99 dataset. In: Proceedings of the 2009 IEEE Symposium on Computational Intelligence in Security and Defense Applications (CISDA 2009), pp. 53–58 (2009) · DOI · Paper page
  10. Revathi, S., Malathi, A.: A detailed analysis on NSL-KDD dataset using various machine learning techniques for intrusion detection. Int. J. Eng. Res. Technol. 2(12), 1848–1853 (2013)
  11. Vinchurkar, D.P., Reshamwala, A.: A review of intrusion detectiom system using neural network and machine learning technique. Int. J. Eng. Sci. Innov. Technol. 1(2), 54–63 (2012)
  12. Das, S., Kalita, H.K.: Advanced dimensionality reduction method for big data. In: Research advances in the integration of big data and smart computing, information science reference (an imprint of IGI global), p. 200 (2016) · doi:10.4018/978-1-4666-8737-0.ch011
  13. Panwar, S.S., Raiwani, Y.P.: Data reduction techniques to analyze NSL-KDD Dataset. Int. J. Comput. Eng. Technol. 5(10), 21–31 (2014)
  14. Jain, A.K.: Data clustering: 50 years beyond K-means. J. Pattern Recognit. Lett. 31(8), 651–666 (2010) · DOI · Paper page
  15. John, G.H., Langley, P.: Static versus dynamic sampling for data mining, KDD 96. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 367–370 (1996)
  16. Motoda, H., Liu, H.: Feature selection, extraction, and construction. Commun. Inst. Inf. Comput. Mach. Taiwan 5(2), 67–72 (2002)
  17. Elrawy, M.F., Abdelhamid, T.K., Mohamed, A.M.: IDS in telecommunication network using PCA. Int. J. Comput. Netw. Commun. 5(4), 147–157 (2013) · doi:10.5121/ijcnc.2013.5412
  18. Datti, R., Lakhina, S.: Performance comparison of features reduction techniques for intrusion detection system. Int. J. Comput. Sci. Technol. 3(1), 332–335 (2012)
  19. Bajaj, K., Arora, A.: Dimension reduction in intrusion detection features using discriminative machine learning approach. Int. J. Comput. Sci. Issues 10(4), 324–328 (2013)
  20. Ibraheem, N.B., Jawhar, M.M.T., Osman, H.M.: Principle components analysis and multi-layer perceptron based intrusion detection system. In: Fifth Scientific Conference Information Technology, vol. 10(1), pp. 127–135 (2013) · doi:10.33899/csmj.2013.163430
  21. Chae, H., Jo, B., Choi, S., Park, T.: Feature selection for intrusion detection using NSL-KDD. In: Proceedings of the 12th WSEAS International Conference on Information Security and Privacy, pp. 184–187, November 2013
  22. Namratha, M., Prajwala, T.R.: A comprehensive overview of clustering algorithms in pattern recognition. IOSR J. Comput. Eng. 4(6), 23–30 (2012) · doi:10.9790/0661-0462330
  23. Caruana, R., Niculescu-Mizil, A.: An empirical comparison of supervised learning algorithms. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 161–168, June 2006 · DOI · Paper page
  24. Lin, F., Cohen, W.W.: Semi-supervised classification of network data using very few labels. In: Proceedings of the 2010 International Conference on Advances in Social Networks and Mining, pp. 192–198, August 2010 · DOI · Paper page
  25. Deng, L., Yu, D.: Deep learning methods and applications. Found. Trends Signal Process., 7(3–4), 199–201, 217 (2014) · DOI · Paper page
  26. Hinton, G.E.: Boltzmann machine. Scholarpedia 2(5), 1668 (2007) · DOI
  27. Fischer, A., Igel, C.: Training restricted Boltzmann machines: an introduction. Pattern Recognit. 47, 25–39 (2014) · DOI · Paper page
  28. Alom, M.Z., Bontupalli, V., Taha, T.M.: Intrusion detection using deep belief networks. Int. J. Monit. Surveill. Technol. Res. 3(2), 35–56 (2015)
  29. Kim, S.K., McMahon, P.L., Olulotun, K.: A large-scale architecture for restricted Boltzmann machines. In: Proceedings of the 2010 18th IEEE Annual International Symposium on Field-Programmable Custom Computing Machines, pp. 201–208, May 2010 · DOI
  30. Kang, M., Kang, J.: Intrusion detection system using deep neural network for in-vehicle network security. PLoS ONE 11(6), e0155781 (2016). doi: 10.1371/journal.pone.0155781e0155781 · DOI · Paper page
  31. Yamashita, T., Tanaka, M., Yoshida, E., Yamauchi, Y., Fujiyoshii, H.: To be Bernoulli or to be Gaussian, for a restricted boltzmann machine. In: 2014 22nd International Conference on Pattern Recognition (ICPR), pp. 1520–1525. IEEE (2014) · DOI
  32. Hinton, G.E., Salakhutdinov, R.: Reducing the dimensionality of data with neural networks. Science 313, 504–507 (2006) · DOI · Paper page
  33. Kayack, H.G., Zincir-Heywood, A.N., Heywood, M.I.: Selecting features for intrusion detection: a feature relevance analysis on KDD 99 intrusion detection datasets. In: Proceedings of the 3rd Annual Conference on Privacy Security and Trust, October 2005
  34. Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the kdd cup 99 data set. In: CISDA 2009. IEEE Symposium on Computational Intelligence for Security and Defense Applications, 2009, pp. 1–6. IEEE (2009)
  35. Tao, X., Kong, D., Wei, Y., Wang, Y.: A big network traffic data fusion approach based on fisher and deep auto-encoder. Information 7(2), 20 (2016) · DOI
  36. Kim, J., Kim, J., Thu, H.L.T., Kim, H.: Long short term memory recurrent neural network classifier for intrusion detection. In: 2016 International Conference on Platform Technology and Service (PlatCon), pp. 1–5, Feb 2016 · DOI · Paper page
  37. Tang, T.A., Mhamdi, L., McLernon, D., Zaidi, S.A.R., Ghogho, M.: Deep learning approach for network intrusion detection in software defined networking. In: 2016 International Conference on Wireless Networks and Mobile Communications (WINCOM), pp. 258–263. IEEE (2016) · DOI · Paper page
  38. Baek, S., Kwon, D., Kim, J., Suh, S., Kim, H., Kim, I.: Unsupervised labeling for supervised anomaly detection in enterprise and cloud networks. In: The 4th IEEE International Conference on Cyber Security and Cloud Computing (IEEE CSCloud 2017), July 2017 · DOI
  39. Schlegl, T., Seeböck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G.: Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. arXiv preprint, arXiv:1703.05921 (2017) · DOI · Paper page
  40. Xue, Y., Xu, T., Zhang, H., Long, R., Huang, X.: Segan: adversarial network with multi-scale $$ l_1 $$ l 1 loss for medical image segmentation. arXiv preprint, arXiv:1706.01805 (2017) · DOI · Paper page
  41. Goodfellow, I.: Nips 2016 tutorial: generative adversarial networks. arXiv preprint, arXiv:1701.00160 (2016) · arXiv

Cited by

See all 778

Metadata

Authors
Kwon, D., Kim, H., Kim, J., Suh, S. C., Kim, I., & Kim, K. J.
Venue
Cluster Computing
Published
2017-09-27
Volume
22
Issue
S1
Pages
949-961
ISSN
  • 1386-7857 (ISSN-L)
  • 1573-7543
OpenAlex
W2756489700
Cited by
778
References
2
FWCI
38.89
Type
journal article
Language
en
Open access
closed
Licence
www.springer.com/tdm (tdm)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: A survey of deep learning-based network anomaly detection. https://app.cochat.ai/papers/10.1007/s10586-017-1117-8
The paper
Kwon, D., Kim, H., Kim, J., Suh, S. C., Kim, I., & Kim, K. J. (2019). A survey of deep learning-based network anomaly detection. Cluster Computing. https://doi.org/10.1007/s10586-017-1117-8

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.