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Introduction

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Network Traffic Anomaly Detection and Prevention

Part of the book series: Computer Communications and Networks ((CCN))

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Abstract

With advances in network technologies, the variety and volume, Internet services that are provided by commercial, nonprofit or governmental organizations undergo constant growth, causing commensurate and often exposure expansion in network traffic.

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Notes

  1. 1.

    http://www.itu.int/en/Pages/default.aspx.

  2. 2.

    http://www.cisco.com.

  3. 3.

    http://www.verizon.com/enterprise/databreach.

References

  1. Aydin, M.A., Zaim, A.H., Ceylan, K.G.: A hybrid intrusion detection system design for computer network security. Comput. Electr. Eng. 35(3), 517–526 (2009)

    Article  MATH  Google Scholar 

  2. Bace, R., Mell, P.: Intrusion detection systems. Tech. Rep. SP800-31, NIST Special Publications, US Department of Defence, USA (2001)

    Google Scholar 

  3. Burbeck, K., Nadjm-Tehrani, S.: Adaptive real-time anomaly detection with incremental clustering. Information Security Technical Report 12(1), 56–67 (2007)

    Article  Google Scholar 

  4. Casas, P., Mazel, J., Owezarski, P.: Unsupervised network intrusion detection systems: detecting the unknown without knowledge. Comput. Commun. 35(7), 772–783 (2012)

    Article  Google Scholar 

  5. Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 1–58 (2009)

    Article  Google Scholar 

  6. David, S.T.: Network intrusion prevention systems: signature-based and anomaly detection. Ph.D. thesis, The Hebrew University of Jerusalem, April (2011)

    Google Scholar 

  7. Erman, J., Mahanti, A., Arlitt, M., Cohen, I., Williamson, C.: Semi-supervised network traffic classification. SIGMETRICS Perform. Eval. Rev. 35(1), 369–370 (2007)

    Article  Google Scholar 

  8. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: The KDD process for extracting useful knowledge from volumes of data. Commun. ACM 39(11), 27–34 (1996). doi:10.1145/240455.240464

    Article  Google Scholar 

  9. Jiang, S., Song, X., Wang, H., Han, J.J., Li, Q.H.: A clustering-based method for unsupervised intrusion detections. Pattern Recogn. Lett. 27(7), 802–810 (2006)

    Article  Google Scholar 

  10. Khreich, W., Granger, E., Miri, A., Sabourin, R.: Adaptive ROC-based ensembles of HMMs applied to anomaly detection. Pattern Recogn. 45(1), 208–230 (2012)

    Article  MATH  Google Scholar 

  11. Kumar, V.: Parallel and distributed computing for cybersecurity. IEEE Distrib. Syst. Online 6(10) (2005)

    Google Scholar 

  12. Laskov, P., Gehl, C., Krüger, S., Müller, K.R.: Incremental support vector learning: analysis, implementation and applications. J. Mach. Learn. Res. 7, 1909–1936 (2006)

    MathSciNet  MATH  Google Scholar 

  13. Noto, K., Brodley, C., Slonim, D.: FRaC: a feature-modeling approach for semi-supervised and unsupervised anomaly detection. Data Min. Knowl. Discov. 25(1), 109–133 (2012). doi:10.1007/s10618-011-0234-x

    Article  MathSciNet  Google Scholar 

  14. Panda, M., Abraham, A., Patra, M.R.: Hybrid intelligent systems for detecting network intrusions. Secur. Commun. Netw. 8(16), 2741–2749 (2015). http://dx.doi.org/10.1002/sec.592

    Article  Google Scholar 

  15. Patcha, A., Park, J.M.: An overview of anomaly detection techniques: existing solutions and latest technological trends. Comput. Netw. 51(12), 3448–3470 (2007)

    Article  Google Scholar 

  16. Shimonski, R.: The Wireshark field guide: analyzing and troubleshooting network traffic. Syngress Media, U.S. (2013)

    Google Scholar 

  17. Song, J., Takakura, H., Okabe, Y., Nakao, K.: Toward a more practical unsupervised anomaly detection system. Inf. Sci. 231 (2011). http://dx.doi.org/10.1016/j.ins.2011.08.011

  18. Su, M.Y.: Using clustering to improve the KNN-based classifiers for online anomaly network traffic identification. J. Netw. Comput. Appl. 34(2), 722–730 (2011)

    Article  Google Scholar 

  19. Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining, 4th edn. Addison-Wesley, Pearson Education, India (2009)

    Google Scholar 

  20. Tanenbaum, A.: Computer Networks. Prentice Hall Professional Technical Reference, 4th edn. Prentice Hall, Pearson Education, India (2002)

    Google Scholar 

  21. Thottan, M., Ji, C.: Anomaly detection in IP networks. IEEE Trans. Signal Process. 51(8), 2191–2204 (2003)

    Article  Google Scholar 

  22. Toosi, A.N., Kahani, M.: A new approach to intrusion detection based on an evolutionary soft computing model using Neuro-fuzzy classifiers. Comput. Commun. 30(10), 2201–2212 (2007)

    Article  Google Scholar 

  23. Wood, P., Egan, G., Haley, K., Tran, T., Cox, O.: Internet security threat report. Tech. Rep. 17, Symantec, USA (2012)

    Google Scholar 

  24. Wu, S.X., Banzhaf, W.: The use of computational intelligence in intrusion detection systems: a review. Appl. Soft Comput. 10(1), 1–35 (2010)

    Article  Google Scholar 

  25. Yi, Y., Wu, J., Xu, W.: Incremental SVM based on reserved set for network intrusion detection. Expert Syst. Appl. 38(6), 7698–7707 (2011)

    Article  Google Scholar 

  26. Zhang, J., Chen, C., Xiang, Y., Zhou, W.: Semi-supervised and compound classification of network traffic. In: Proceedings of the 32nd International Conference on Distributed Computing Systems Workshops, pp. 617–621 (2012). doi:10.1109/ICDCSW.2012.12

  27. Zhang, J., Zulkernine, M.: A hybrid network intrusion detection technique using random forests. In: Proceedings of the 1st International Conference on Availability, Reliability and Security, pp. 262–269. IEEE, CS, USA (2006). doi:10.1109/ARES.2006.7

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Authors and Affiliations

  1. Kaziranga University, Jorhat, India

    Monowar H. Bhuyan

  2. Tezpur University, Napaam, India

    Dhruba K. Bhattacharyya

  3. University of Colorado, Colorado Springs, Colorado, USA

    Jugal K. Kalita

Authors
  1. Monowar H. Bhuyan
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  2. Dhruba K. Bhattacharyya
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  3. Jugal K. Kalita
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Bhuyan, M.H., Bhattacharyya, D.K., Kalita, J.K. (2017). Introduction. In: Network Traffic Anomaly Detection and Prevention. Computer Communications and Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-65188-0_1

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  • DOI: https://doi.org/10.1007/978-3-319-65188-0_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65186-6

  • Online ISBN: 978-3-319-65188-0

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