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A survey of deep learning-based network anomaly detection
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Efficient Processing of Deep Neural Networks: A Tutorial and Survey
Sze, V., Chen, Y.-H., Yang, T.-J., & Emer, J. S. (2017) · Proceedings of the IEEE · doi:10.1109/jproc.2017.2761740 · Cited by 4,215
A Survey of Classification Techniques in the Area of Big Data
Koturwar, P., Girase, S., & Mukhopadhyay, D. (2015) · arXiv · doi:10.48550/arxiv.1503.07477 · Cited by 35
Research works printed but not linked: 41
- Semente: 2016 Internet Security Threat Report (ISTR), vol. 21, p. 8, April 2016
- Li, Y., Ma, R., Jiao, R.: Hybrid malicious code detection method based on deep learning. Int. J. Secur. Appl. 9(5), 205–216 (2014)
- 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
- 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
- Ahmed, A.: Signature-based network inrusion detection system using JESS(SNIDJ). Graduate Project Technical Report, TAMUCC, pp. 2–6 (2004)
- Ning, P., Jajodia, S.: Intrusion detection techniques. The Internet Encyclopedia. doi: 10.1002/047148296X.tie097 · DOI
- 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
- Deng, L., Yu, D.: Deep learning: methods and applications. Found. Trends Signal Process. 7(3–4), 197–387 (2014) · DOI · Paper page
- 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
- 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)
- 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)
- 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
- Panwar, S.S., Raiwani, Y.P.: Data reduction techniques to analyze NSL-KDD Dataset. Int. J. Comput. Eng. Technol. 5(10), 21–31 (2014)
- Jain, A.K.: Data clustering: 50 years beyond K-means. J. Pattern Recognit. Lett. 31(8), 651–666 (2010) · DOI · Paper page
- 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)
- Motoda, H., Liu, H.: Feature selection, extraction, and construction. Commun. Inst. Inf. Comput. Mach. Taiwan 5(2), 67–72 (2002)
- 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
- Datti, R., Lakhina, S.: Performance comparison of features reduction techniques for intrusion detection system. Int. J. Comput. Sci. Technol. 3(1), 332–335 (2012)
- 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)
- 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
- 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
- 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
- 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
- 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
- Deng, L., Yu, D.: Deep learning methods and applications. Found. Trends Signal Process., 7(3–4), 199–201, 217 (2014) · DOI · Paper page
- Hinton, G.E.: Boltzmann machine. Scholarpedia 2(5), 1668 (2007) · DOI
- Fischer, A., Igel, C.: Training restricted Boltzmann machines: an introduction. Pattern Recognit. 47, 25–39 (2014) · DOI · Paper page
- 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)
- 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
- 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
- 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
- Hinton, G.E., Salakhutdinov, R.: Reducing the dimensionality of data with neural networks. Science 313, 504–507 (2006) · DOI · Paper page
- 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
- 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)
- 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
- 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
- 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
- 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
- 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
- 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
- Goodfellow, I.: Nips 2016 tutorial: generative adversarial networks. arXiv preprint, arXiv:1701.00160 (2016) · arXiv
Cited by
Deep Learning in Mobile and Wireless Networking: A Survey
Zhang, C., Patras, P., & Haddadi, H. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2904897 · Cited by 1,768
Deep Learning for Anomaly Detection: A Survey
Chalapathy, R., & Chawla, S. (2019) · arXiv · doi:10.48550/arxiv.1901.03407 · Cited by 1,240
Machine Learning and Deep Learning Methods for Cybersecurity
Xin, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., Zhu, H., Gao, M., Hou, H., & Wang, C. (2018) · IEEE Access · doi:10.1109/access.2018.2836950 · Cited by 1,228
Machine Learning for Anomaly Detection: A Systematic Review
Nassif, A. B., Talib, M. A., Nasir, Q., & Dakalbab, F. M. (2021) · IEEE Access · doi:10.1109/access.2021.3083060 · Cited by 748
Xie, J., Yu, F. R., Huang, T., Xie, R., Liu, J., Wang, C., & Liu, Y. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2866942 · Cited by 700
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)
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- 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
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.