Skip to main content
SpringerLink
Log in

Usage of Modern Exponential-Smoothing Models in Network Traffic Modelling

  • Conference paper
Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 210))

Abstract

The article summarized current state of our works regarding usage of exponential smoothing Holt-Winters’ based models for analysis, modelling and forecasting Time Series with data of computer network traffic. Especially we use two models proposed by J. W. Taylor to deal with double and triple seasonal cycles for modelling network traffic in two local area networks and three campus networks. We use three time series with data of TCP, UDP and ICMP traffic (given by number of packets per interval) on each network.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Breunig, M.M., Kriegel, H.-P., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. In: ACM SIGMOD Conference Proceedings, pp. 93–104 (May 2000)

    Google Scholar 

  2. Brutlag, J.D.: Aberrant behavior detection in time series for network monitoring. In: Proceedings of the 14th System Administration Conference, New Orleans, Fla, USA, pp. 139–146 (2000)

    Google Scholar 

  3. Burkom, H.S., Murphy, S.P., Shmueli, G.: Automated time series forecasting for biosurveillance. Statist. Med. 26, 4202–4218, doi:10.1002/sim.2835

    Google Scholar 

  4. Chakhchoukh, Y., Panciatici, P., Bondon, P.: Robust estimation of SARIMA models: Application to short-term load forecasting. In: IEEE/SP 15th Workshop on Statistical Signal Processing, SSP 2009, August 31-September 3, pp. 77–80 (2009)

    Google Scholar 

  5. Chuah, M.C., Fu, F.: ECG Anomaly Detection via Time Series Analysis. In: Thulasiraman, P., He, X., Xu, T.L., Denko, M.K., Thulasiram, R.K., Yang, L.T. (eds.) ISPA 2007 Workshops. LNCS, vol. 4743, pp. 123–135. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  6. Cordeiro, C., Neves, M.M.: Forecasting with exponential smoothing methods and bootstrap, http://www.caos.inf.uc3m.es/~jperalta/ICTSF/images/DescriptionMethodCordeiroNeves.pdf

  7. Guzik, B., Appenzeller, D., Jurek, W.: Prognozowanie i symulacje. Wybrane zagadnienia, Wydawnictwo AE w Poznaniu, Poznań (2004)

    Google Scholar 

  8. Hanzák, T.: Holt-Winters method with general seasonality, http://ocipoez.dml.cz/bitstream/handle/10338.dmlcz/142058/Kybernetika_48-2012-1_1.pdf

  9. Hanzák, T., Cipra, T.: Exponential smoothing for time series with outliers, http://ww.dml.cz/bitstream/handle/10338.dmlcz/141565/Kybernetika_47-2011-2_1.pdf

  10. Hao, M.C., Keim, D.A., Dayal, U., Schneidewind, J.: Business Process Impact Visualization and Anomaly Detection, http://ivi.sagepub.com/content/5/1/15.short

  11. Hauskrecht, et. al.: Evidence-based anomaly detection in clinical domains. In: Proceedings of the Annual American Medical Informatics Association (AMIA) Symposium (2007), http://www.cs.pitt.edu/~milos/research/AMIA-07.pdf

  12. Knorr, E.M., Ng, R.T., Tucakov, V.: Distance-Based Outliers: Algorithms and Applications. VLDB Journal 8(3-4), 237–253 (2000)

    Article  Google Scholar 

  13. Kratz, L., Nishino, K.: Anomaly Detection in Extremely Crowded Scenes Using Spatio-Temporal Motion Pattern Models, Department of Computer Science. Drexel University, https://www.cs.drexel.edu/~kon/publication/LKratz_CVPR09_preprint.pdf

  14. Lawton, R.: On the Stability of the Double Seasonal Holt-Winters Method. University of the West of England, http://forecasters.org/submissions09/LawtonRichardISF2009.pdf

  15. Wang, L., ,Zhang, R.-Q., Sheng, W., Xu, Z.-G.: Regression Forecast and Abnormal Data Detection Based on Support Vector Regression, http://en.cnki.com.cn/Article_en/CJFDTOTAL-ZGDC200908020.htm

  16. Liu, W., Lin, S., Piegorsch, W.W.: Construction of Exact Simultaneous Confidence Bands for a Simple Linear Regression Model. International Statistical Review 76(1), 39–57, doi:10.1111/j.1751-5823.2007.00027.x

    Google Scholar 

  17. Markou, M., Singh, S.: Novelty detection: a review part 1: statistical approaches. Signal Processing 83, 2481–2497 (2003)

    Article  MATH  Google Scholar 

  18. Maronna, R., Martin, R.D., Yohai, V.: Robust Statistics - Theory and Methods. Wiley (2006)

    Google Scholar 

  19. Rivlin, A.E., Shimshoni, I.: Ror: Rejection of outliers by rotations in stereo matching. In: Conference on Computer Vision and Pattern Recognition (CVPR 2000), pp. 1002–1009 (June 2000)

    Google Scholar 

  20. Rousseeuw, P.J., Leroy, A.M.: Robust Regression and Outlier Detection. Wiley (1987) (republished in paperback, 2003)

    Google Scholar 

  21. Szmit, M., Adamus, S., Bugała, S., Szmit, A.: Implementation of Brutlag’s algorithm in Anomaly Detection 3. In: Federated Conference on Computer Science and Information Systems, Proceedings of the Federated Conference on Computer Science and Information Systems, pp. 685–691. PTI, IEEE, Wrocław (2011)

    Google Scholar 

  22. Szmit, M., Szmit, A.: Use of holt-winters method in the analysis of network traffic: Case study. In: Kwiecień, A., Gaj, P., Stera, P. (eds.) CN 2011. CCIS, vol. 160, pp. 224–231. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  23. Szmit, M., Szmit, A.: Usage of Modified Holt-Winters Method in the Anomaly Detection of Network Traffic: Case Studies. Journal of Computer Networks and Communications 2012, doi:10.1155/2012

    Google Scholar 

  24. Szmit, M., Szmit, A.: Usage of Pseudo-estimator LAD and SARIMA Models for Network Traffic Prediction. Case Studies, Communications in Computer and Information Science 291, 229–236 (2012), doi:10.1007/978-3-642-31217-5_25

    Article  Google Scholar 

  25. Taylor, J.W.: Short-Term Electricity Demand Forecasting Using Double Seasonal Exponential Smoothing. Journal of Operational Research Society 54, 799–805 (2003), http://r.789695.n4.nabble.com/file/n888942/ExpSmDoubleSeasonal.pdf

    Article  MATH  Google Scholar 

  26. de la Torre, F., Black, M.J.: Robust principal component analysis for computer vision. In: Proceedings of the Eighth International Conference on Computer Vision (ICCV 2001), pp. 362–369 (2001)

    Google Scholar 

  27. Vala, R., Malaník, D., Jašek, R.: Usability of software intrusion-detection system in web applications. In: Herrero, Á., Snášel, V., Abraham, A., Zelinka, I., Baruque, B., Quintián, H., Calvo, J.L., Sedano, J., Corchado, E. (eds.) Int. JointConf. CISIS’12-ICEUTE’12-SOCO’12. AISC, vol. 189, pp. 159–166. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  28. Szmit, A., Szmit, M.: O wykorzystaniu modeli ekonometrycznych do prognozowania ruchu sieciowego, Zarządzanie rozwojem organizacji, Spała (accepted for publication, 2013)

    Google Scholar 

  29. ITU-T Recommendation E.490.1: Overview of Recommendations on traffic engineering, ITU (2003), http://www.itu.int/rec/T-REC-E.490.1-200301-I/en

  30. ITU-T E.507 Models for Forecasting International Traffic, ITU (1998), http://www.itu.int/rec/T-REC-E.507-198811-I/en

  31. Goodwin, P.: The Holt-Winters Approach to Exponential Smoothing: 50 Years Old and Going Strong, FORESIGHT Fall pp. 30–34 (2010), http://www.forecasters.org/pdfs/foresight/free/Issue19_goodwin.pdf

  32. Taylor, J.W.: Exponentially Weighted Methods for Forecasting Intraday Time Series with Multiple Seasonal Cycles. International Journal of Forecasting 26, 627–646 (2010), http://users.ox.ac.uk/~mast0315/MultSeasExpWtdMethods.pdf

    Article  Google Scholar 

  33. Szmit, M.: Využití nula-jedničkových modelů pro behaviorální analýzu síťového provozu, Internet, competitiveness and organizational security, TBU, Zlín (2011)

    Google Scholar 

  34. Gelper, S., Fried, R., Croux, C.: Robust forecasting with exponential and Holt-Winters smoothing, Leuven (2007), https://lirias.kuleuven.be/bitstream/123456789/120456/1/kbi_0718.pdf

  35. Szmit, M., Szmit, A., Adamus, S., Bugała, S.: Usage of Holt-Winters Model and Multilayer Perceptron in Network Traffic Modelling and Anomaly Detection. Informatica 36(4), 359–368

    Google Scholar 

  36. Münz, G.: Traffic Anomaly Detection and Cause Identification Using Flow-Level Measurements, TUM, Müchen (2010), http://www.net.in.tum.de/fileadmin/TUM/NET/NET-2010-06-1.pdf

  37. Wang, Y.: Statistical Techniques for Network Security: Modern Statistically-Based Intrusion Detection and Protection. IGI Global (2009)

    Google Scholar 

  38. Palmieri, F., Fiore, U.: Network anomalny detection though nonlinear analysis. Computers and Security 29, 737–755 (2010)

    Article  Google Scholar 

  39. Grzenda, M., Macukow, B.: Heat Consumption Prediction with Multiple Hybrid Models. In: Omatu, S., Rocha, M.P., Bravo, J., Fernández, F., Corchado, E., Bustillo, A., Corchado, J.M. (eds.) IWANN 2009, Part II. LNCS, vol. 5518, pp. 1213–1221. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  40. Villén-Altamirano, M.: Overview of ITU Recommendations on Traffic Engineering. Paper presented in the ITU/ITC workshop within 17th ITC

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlin, Zlin, Czech Republic

    Roman Jašek

  2. Faculty of Management, Lodz University of Technology, Lodz, Poland

    Anna Szmit

  3. Orange Labs, Corporate IT Security Agency, Poland, Lodz

    Maciej Szmit

Authors
  1. Roman Jašek
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anna Szmit
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maciej Szmit
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Roman Jašek .

Editor information

Editors and Affiliations

  1. Faculty of Electrical Eng. & Comp. Sci, Department of Computer Science, VŠB-TUO, 17. listopadu 15, Ostrava-Poruba, 708 33, Czech Republic

    Ivan Zelinka

  2. , Department of Electronic Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, China, People's Republic

    Guanrong Chen

  3. , Institute of Physical, University of Tuebingen, Auf der Morgenstelle 8, Tuebingen, 72076, Germany

    Otto E. Rössler

  4. Faculty of Electrical Eng. & Comp. Sci., Department of Computer Science, VŠB-TUO, 17. listopadu 15, Ostrava-Poruba, 708 33, Czech Republic

    Vaclav Snasel

  5. (MIR Labs), Scientific Network for Innovation, Machine Intelligence Research Labs, Auburn, 98071, Washington, USA

    Ajith Abraham

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer International Publishing Switzerland

About this paper

Cite this paper

Jašek, R., Szmit, A., Szmit, M. (2013). Usage of Modern Exponential-Smoothing Models in Network Traffic Modelling. In: Zelinka, I., Chen, G., Rössler, O., Snasel, V., Abraham, A. (eds) Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems. Advances in Intelligent Systems and Computing, vol 210. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00542-3_43

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-00542-3_43

  • Publisher Name: Springer, Heidelberg

  • Print ISBN: 978-3-319-00541-6

  • Online ISBN: 978-3-319-00542-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation