Skip to main content
SpringerLink
Log in
Book cover

International Symposium on Neural Networks

ISNN 2006: Advances in Neural Networks - ISNN 2006 pp 184–189Cite as

A Resource Allocating Neural Network Based Approach for Detecting End-to-End Network Performance Anomaly

  • Conference paper

  • 1603 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3973))

Abstract

Automatic detection of end-to-end network performance anomalies is important to efficient network management and optimization. We present an end-to-end network performance anomalies detection method, based on characterizing of the dynamic statistical properties of RTT normality. The experiment on real Internet end-to-end path RTT data shows that, the proposed method is accurate in detecting performance anomalies, it can successfully detect about 96.25% anomalies in the experiment.

This work was supported by National Natural Science Foundation of China under grant 60273070, 60473031 and 60403031, the National High-Technology Program of China (863) under grant 2005AA121560, the Hunan Provincial Natural Science Foundation of China under grant 05JJ30116.

This is a preview of subscription content, 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   119.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Li, B.C., Yin, H.J.: Face Recognition Using RBF Neural Networks and Wavelet Transform. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3497, pp. 105–111. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  2. Ho, L., Cavuto, D., Papavassiliou, S.: Adaptive/Automated Detection of Service Anomalies in Transaction WANs: Network Analysis, Algorithms, Implementation, and Deployment. IEEE Journal on Selected Areas in Communications 18(5), 744–757 (2000)

    Article  Google Scholar 

  3. Pu, X.R., Yi, Z., Zheng, Z.M., Zhou, W., Ye, M.: Face Recognition Using Fisher Non-negative Matrix Factorization with Sparseness Constraints. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3497, pp. 112–117. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  4. Sun, C.Y., Feng, C.B.: Neural Networks for Nonconvex Nonlinear Programming Problems: A Switching Control Approach. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3496, pp. 694–699. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  5. Platt, J.: A Resource-Allocating Network for Function Interpolation. Neural Computation 3(2), 213–225 (1991)

    Article  MathSciNet  Google Scholar 

  6. Hao, Z.F., Liu, B., Yang, X.W., Liang, Y.C., Zhao, F.: Twi-Map Support Vector Machine for Multi-classification Problems. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3496, pp. 869–874. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  7. Guo, C.G., Anthony, K.: An Optimal Neural-Network Model for Learning Posterior Probability Functions from Observations. In: Yin, F.-L., Wang, J., Guo, C. (eds.) ISNN 2004. LNCS, vol. 3173, pp. 370–376. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  8. Lu, W., Lu, H.T., Chung, F.L.: Subsampling-Based Robust Watermarking Using Neural Network Detector. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3497, pp. 801–806. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  9. Manikopoulos, C., Papavassiliou, S.: Network Intrusion and Fault Detection: A Statistical Anomaly Approach. IEEE Communications Magazine 40(10), 76–82 (2002)

    Article  Google Scholar 

  10. Han, M., Fan, M.M., Xi, J.H.: Study of Nonlinear Multivariate Time Series Prediction Based on Neural Networks. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3497, pp. 618–623. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  11. Yang, Y.Q., Cao, J.D., Zhu, D.Q.: A Study of Portfolio Investment Decision Method Based on Neural Network. In: Yin, F.-L., Wang, J., Guo, C. (eds.) ISNN 2004. LNCS, vol. 3174, pp. 976–981. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  12. McGregor, A., Braun, H.-W.: The NLANR Network Analysis Infrastructure. IEEE Communications Magazine 38(5), 122–128 (2000)

    Article  Google Scholar 

  13. Sun, F.C., Zhang, H.: Neuro-Fuzzy Hybrid Position/Force Control for a Space Robot with Flexible Dual-Arms. In: Yin, F.-L., Wang, J., Guo, C. (eds.) ISNN 2004. LNCS, vol. 3174, pp. 13–18. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  14. Liu, J., Yuan, D.F., Ci, S., Zhong, Y.J.: A New QoS Routing Optimal Algorithm in Mobile Ad Hoc Networks Based on Hopfield Neural Network. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3498, pp. 343–348. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  15. Li, G., Patrick, W.: An Analysis of Network Performance Degradation Induced by Workload Fluctuations. IEEE/ACM Transaction on Network 3(4), 433–440 (1995)

    Article  Google Scholar 

  16. Wen, Y.M., Lu, B.L.: A Hierarchical and Parallel Method for Training Support Vector Machines. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3496, pp. 881–886. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  17. Li, S.W., Li, S.R.: Hopf Bifurcation in a Single Inertial Neuron Model with a Discrete Delay. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3496, pp. 327–332. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  18. Hajji, H., Far, B.H.: Continuous Network Monitoring for Fast Detection of Performance Problems. In: Proceedings of International Symposium on Performance Evaluation of Computer and Telecommunication Systems (2001)

    Google Scholar 

  19. Zhang, J., Liao, X.F., Li, C.D., Lu, A.W.: A Further Result for Exponential Stability of Neural Networks with Time-Varying Delays. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3496, pp. 120–125. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  20. Jiang, M.H., Shen, Y., Liu, M.Q.: Global Exponential Stability of Non-autonomous Neural Networks with Variable Delay. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3496, pp. 108–113. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  21. Juan, M., Garcia-Teodoro, P.: Anomaly Detection Methods in Wired Networks: A Survey and Taxonomy. Computer Communications 27(3), 1569–1584 (2004)

    Google Scholar 

  22. Zeng, Z.G., Chen, B.S., Wang, Z.F.: Globally Attractive Periodic State of Discrete-Time Cellular Neural Networks with Time-Varying Delays. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3496, pp. 282–287. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Computer and Communication, Hunan University, Changsha, 410082, China

    Wenwei Li

  2. School of Software, Hunan University, Changsha, 410082, China

    Dafang Zhang, Jinmin Yang & Lei Wang

  3. Institute of Computing Technology, Chinese Academy of Science, Beijing, 100080, China

    Gaogang Xie

Authors
  1. Wenwei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dafang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jinmin Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gaogang Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Li, W., Zhang, D., Yang, J., Xie, G., Wang, L. (2006). A Resource Allocating Neural Network Based Approach for Detecting End-to-End Network Performance Anomaly. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_27

Download citation

  • DOI: https://doi.org/10.1007/11760191_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34482-7

  • Online ISBN: 978-3-540-34483-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation