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An Alarm Correlation Algorithm Based on Similarity Distance and Deep Network

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Intelligent Computing Methodologies (ICIC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9773))

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Abstract

Currently, a few alarm correlation algorithms are based on a framework involving frequency and support-confidence. These algorithms often fail to address text data in alarm records and cannot handle high-dimensional data. This paper proposes an algorithm based on the similarity distance and deep networks. The algorithm first translates text data in alarms to real number vectors; second, it reconstructs the input, obtains the alarm features through a deep network system and performs dimension reduction; and finally, it presents the alarm distribution visually and helps the administrator determine the new fault. Experimental results demonstrate that it cannot only mine the correlation among alarms but also determine the new fault quickly by comparing the graphs of the alarm distribution.

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References

  1. Cronk, R.N., Callahan, P.H., Bernstein, L.: Rule-based expert systems for network management and operations: an introduction. Network 2(5), 7–21 (1988)

    Google Scholar 

  2. Kliger, S., Yemini, S., Yemini, Y., Ohsie, D., Stolfo, S.J.: A coding approach to event correlation. Integr. Netw. Manage. 95, 266–277 (1995)

    Article  Google Scholar 

  3. Meira, D.M., Nogueira, J.M.S.: Modelling a telecommunication network for fault management applications. In: Network Operations and Management Symposium, NOMS 1998, vol. 3, pp. 723–732. IEEE (1998)

    Google Scholar 

  4. Slade, S.: Case-based reasoning: a research paradigm. AI Mag. 12(1), 42 (1991)

    Google Scholar 

  5. Zadeh, L.A.: Fuzzy logic. Computer 4, 83–93 (1988)

    Article  MathSciNet  Google Scholar 

  6. Heckerman, D., Mamdani, A., Wellman, M.P.: Real-world applications of Bayesian networks. Commun. ACM 38(3), 24–26 (1995)

    Article  Google Scholar 

  7. Gurer, D.W., Khan, I., Ogier, R., Keffer, R.: An artificial intelligence approach to network fault management. SRI International, 86 (1996)

    Google Scholar 

  8. Mannila, H., Toivonen, H., Verkamo, A.I.: Discovery of frequent episodes in event sequences. Data Min. Knowl. Disc. 1(3), 259–289 (1997)

    Article  Google Scholar 

  9. Hätönen, K., Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H.: Knowledge discovery from telecommunication network alarm databases. In: Proceedings of the Twelfth International Conference on Data Engineering, pp. 115–122 (1996)

    Google Scholar 

  10. Gardner, R.D., Harle, D.A.: Fault resolution and alarm correlation in high-speed networks using database mining techniques. In: Proceedings of 1997 International Conference on Information, Communications and Signal Processing, ICICS 1997, vol. 3, pp. 1423–1427 (1997)

    Google Scholar 

  11. Cuppens, F., Miege, A.: Alert correlation in a cooperative intrusion detection framework. In: 2002 IEEE Symposium on Security and Privacy. Proceedings, pp. 202–215 (2002)

    Google Scholar 

  12. Shin, M.S., Ryu, K.H.: Data mining methods for alert correlation analysis. Int. J. Comput. Inf. Sci. (IJCIS) (2003)

    Google Scholar 

  13. Xu, Q., Guo, J.: Alarm association algorithms based on spectral graph theory. In: International Joint Conference on Artificial Intelligence, JCAI 2009, pp. 320–323 (2009)

    Google Scholar 

  14. Hinton, G.E.: Learning distributed representations of concepts. In: Proceedings of the Eighth Annual Conference of the Cognitive Science Society, vol. 1, p. 12 (1986)

    Google Scholar 

  15. Xu, W., Rudnicky, A.I.: Can artificial neural networks learn language models? (2000)

    Google Scholar 

  16. Bengio, Y., Schwenk, H., Senécal, J.S., Morin, F., Gauvain, J.L.: Neural probabilistic language models. In: Holmes, D.E., Jain, L.C. (eds.) Innovations in Machine Learning, pp. 137–186. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  17. Mikolov, T., Le, Q.V., Sutskever, I.: Exploiting similarities among languages for machine translation. arXiv preprint arXiv:1309.4168 (2013)

  18. Huang, C., Zhao, H.: Chinese word segmentation: a decade review. J. Chin. Inf. Process. 21(3), 8–20 (2007)

    Google Scholar 

  19. Guthrie, D., Allison, B., Liu, W., Guthrie, L., Wilks, Y.: A closer look at skip-gram modelling. In: Proceedings of the 5th International Conference on Language Resources and Evaluation (LREC-2006), pp. 1–4 (2006)

    Google Scholar 

  20. Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)

    MATH  Google Scholar 

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Acknowledgment

This work is supported by the Funds NSFC61572279 and the Science Foundation of Chinese Ministry of Education—China Mobile 2012.

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Authors and Affiliations

  1. School of Software, Tsinghua University, Beijing, China

    Boxu Zhao & Guiming Luo

Authors
  1. Boxu Zhao
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  2. Guiming Luo
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Corresponding author

Correspondence to Boxu Zhao .

Editor information

Editors and Affiliations

  1. Tongji University , Shanghai, China

    De-Shuang Huang

  2. Inha University , Incheon, Korea (Republic of)

    Kyungsook Han

  3. Liverpool John Moores University , Liverpool, United Kingdom

    Abir Hussain

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© 2016 Springer International Publishing Switzerland

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Zhao, B., Luo, G. (2016). An Alarm Correlation Algorithm Based on Similarity Distance and Deep Network. In: Huang, DS., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2016. Lecture Notes in Computer Science(), vol 9773. Springer, Cham. https://doi.org/10.1007/978-3-319-42297-8_34

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  • DOI: https://doi.org/10.1007/978-3-319-42297-8_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42296-1

  • Online ISBN: 978-3-319-42297-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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