Abstract
Method and model of machine learning have applied to many industry fields. Employing RNN to detect and recognize network events and intrusions is extensively studied. This paper divides KDD-99 dataset into 4 subsets according to data item’s ‘attack type’ field. And then, LSTM-RNN is trained and verified on each subset in order to optimize model parameters. Experiments show the strategy of training for LSTM-RNN could boost model accuracy.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge (2007). ISBN 978-0-521-71770-0
Siegelmann, H.T., Sontag, E.D.: Analog computation via neural networks. Theor. Comput. Sci. 131(2), 331–360 (1994). https://doi.org/10.1016/0304-3975(94)90178-3
Wasserman, P.D.: Advanced methods in neural computing. Van Nostrand Reinhold (1993). ISBN 0442004613. OCLC 27429729
Kruse, R., Borgelt, C., Klawonn, F., Moewes, C., Steinbrecher, M., Held, P.: Computational intelligence: a methodological introduction. Springer (2013). ISBN 9781447150121. OCLC 837524179
Borgelt, C.: Neuro-Fuzzy-Systeme: von den Grundlagen künstlicher Neuronaler Netze zur Kopplung mit Fuzzy-Systemen. Vieweg (2003). ISBN 9783528252656. OCLC 76538146
Kohonen, T., Honkela, T.: Kohonen network. Scholarpedia 2, 1568 (2007)
Kohonen, T.: Self-organized formation of topologically correct feature maps. Biol. Cybern. 43(1), 59–69 (1982). https://doi.org/10.1007/bf00337288
Von der Malsburg, C.: Self-organization of orientation sensitive cells in the striate cortex. Kybernetik 14, 85–100 (1973). https://doi.org/10.1007/bf00288907
Turing, A.: The chemical basis of morphogenesis. Phil. Trans. R. Soc. 237, 5–72 (1952)
Ultsch, A., Siemon, H.P.: Kohonen’s self organizing feature maps for exploratory data analysis. In: Widrow, B., Angeniol, B. (eds.) Proceedings of the International Neural Network Conference (INNC-90), Paris, France, 9–13 July 1990, pp. 305–308. Kluwer, Dordrecht (1990). ISBN 978-0-7923-0831-7
Arthur, D., Vassilvitskii, S.: K-means ++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2007, pp. 1027–1035 (2007)
Lloyd, S.P.: Least squares quantization in PCM. IEEE Trans. Inf. Theory 28, 129–137 (1982)
Seber, G.A.F.: Multivariate Observations. Wiley, Hoboken (1984)
Spath, H.: Cluster Dissection and Analysis: Theory, FORTRAN Programs, Examples. Translated by J. Goldschmidt. Halsted Press, New York (1985)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, Santiago, Chile, pp. 487–499, September 1994
Bayardo Jr., R.J.: Efficiently mining long patterns from databases. ACM SIGMOD Record 27(2), 85–93 (1998)
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. Prentice Hall, Upper Saddle River (2003). ISBN 978-0137903955
Rennie, J., Shih, L., Teevan, J., Karger, D.: Tackling the poor assumptions of Naive Bayes classifiers. In: ICML (2003)
Rish, I.: An empirical study of the naive Bayes classifier. In: IJCAI Workshop on Empirical Methods in AI (2001)
Hand, D.J., Yu, K.: Idiot’s Bayes—not so stupid after all? Int. Stat. Rev. 69(3), 385–399 (2001). https://doi.org/10.2307/1403452. ISSN 0306-7734
Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013). arXiv:1206.5538, https://doi.org/10.1109/tpami.2013.50
Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015). arXiv:1404.7828, https://doi.org/10.1016/j.neunet.2014.09.003. PMID 25462637
Bengio, Y., LeCun, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015). https://doi.org/10.1038/nature14539. PMID 26017442
Ghasemi, F., Mehridehnavi, A.R., Fassihi, A., Perez-Sanchez, H.: Deep neural network in biological activity prediction using deep belief network. Appl. Soft Comput. 62, 251 (2017). https://doi.org/10.1016/j.asoc.2017.09.040
Ciresan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3642–3649 (2012). arXiv:1202.2745, https://doi.org/10.1109/cvpr.2012.6248110. ISBN 978-1-4673-1228-8
The UCI KDD Archive Information and Computer Science University of California, Irvine, CA 92697-3425 (1999)
Javaid, A., Niyaz, Q., Sun, W., et al.: A deep learning approach for network intrusion detection system. In: EAI International Conference on Bio-Inspired Information and Communications Technologies. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), pp. 21–26 (2016)
Naoum, R.S., Abid, N.A., Al-Sultani, Z.N.: An enhanced resilient backpropagation artificial neural network for intrusion detection system. Int. J. Comput. Sci. Netw. Secur. 12(3), 11–16 (2012)
Chae, H.-S., Jo, B.-O., Choi, S.-H., Park, T.-K.: Feature Selection for Intrusion Detection using NSL-KDD. In: Recent Advances in Computer Science, pp. 184–187 (2013)
Thaseen, S., Kumar, C.A.: An analysis of supervised tree based classifiers for intrusion detection system. In: 2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering (PRIME), pp. 294–299. IEEE (2013)
Mikolov, T., et al.: Recurrent neural network based language model. In: Interspeech, vol. 2 (2010)
KDD Cup 99. http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html
Acknowledgment
This paper is supported by the National Natural Science Foundation of China under Grant No. 61572153 and the National Key research and Development Plan (Grant No. 2018YFB0803504).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Luo, C., Wang, L., Lu, H. (2018). Analysis of LSTM-RNN Based on Attack Type of KDD-99 Dataset. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11063. Springer, Cham. https://doi.org/10.1007/978-3-030-00006-6_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-00006-6_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00005-9
Online ISBN: 978-3-030-00006-6
eBook Packages: Computer ScienceComputer Science (R0)