Skip to main content

Communication Network Anomaly Detection Based on Log File Analysis

  • Conference paper
Rough Sets and Knowledge Technology (RSKT 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8818))

Included in the following conference series:

Abstract

Communication network today are becoming larger and increasingly complex. Failure in communication systems will cause loss of critical data and even economic losses. Therefore, detecting failures and diagnosing their root-cause in a timely manner is essential. Fast and accurate detection of these failures can accelerate problem determination, and thereby improve system reliability. Today log files have been paid attention on system and network failure detection, but it is still a challenging task to build an efficient model to detect anomaly from log files. To this effect, we propose a novel approach, which aims to detect frequent patterns from log files to build the normal profile, and then to identify the anomalous behaviour in log files. The experimental results demonstrate that our approach is an efficient way for anomaly detection with high accuracy and few false positives.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Agrawal, R., Srikant, R., et al.: Fast algorithms for mining association rules. In: Proc. 20th Int. Con. Very Large Data Bases, VLDB, vol. 1215, pp. 487–499 (1994)

    Google Scholar 

  2. Dunia, R., Qin, S.J.: Multi-dimensional fault diagnosis using a subspace approach. In: American Control Conference. Citeseer (1997)

    Google Scholar 

  3. Han, J., Cheng, H., Xin, D., Yan, X.: Frequent pattern mining: current status and future directions. Data Mining and Knowledge Discovery 15(1), 55–86 (2007)

    Article  MathSciNet  Google Scholar 

  4. Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: ACM SIGMOD Record, vol. 29, pp. 1–12. ACM (2000)

    Google Scholar 

  5. Jackson, J.E., Mudholkar, G.S.: Control procedures for residuals associated with principal component analysis. Technometrics 21(3), 341–349 (1979)

    Article  MATH  Google Scholar 

  6. Lakhina, A., Crovella, M., Diot, C.: Diagnosing network-wide traffic anomalies. In: ACM SIGCOMM Computer Communication Review, vol. 34, pp. 219–230. ACM (2004)

    Google Scholar 

  7. Makanju, A.A., Zincir-Heywood, A.N., Milios, E.E.: Clustering event logs using iterative partitioning. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1255–1264. ACM (2009)

    Google Scholar 

  8. Vaarandi, R.: A breadth-first algorithm for mining frequent patterns from event logs. In: Aagesen, F.A., Anutariya, C., Wuwongse, V. (eds.) INTELLCOMM 2004. LNCS, vol. 3283, pp. 293–308. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  9. Vaarandi, R., et al.: A data clustering algorithm for mining patterns from event logs. In: Proceedings of the 2003 IEEE Workshop on IP Operations and Management (IPOM), pp. 119–126 (2003)

    Google Scholar 

  10. Xu, W., Huang, L., Fox, A., Patterson, D., Jordan, M.I.: Detecting large-scale system problems by mining console logs. In: Proceedings of the ACM SIGOPS 22nd Symposium on Operating Systems Principles, pp. 117–132. ACM (2009)

    Google Scholar 

  11. Xu, W., Huang, L., Fox, A., Patterson, D., Jordan, M.I.: Online system problem detection by mining patterns of console logs. In: Ninth IEEE International Conference on Data Mining, ICDM 2009, pp. 588–597. IEEE (2009)

    Google Scholar 

  12. Yamanishi, K., Maruyama, Y.: Dynamic syslog mining for network failure monitoring. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 499–508. ACM (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xin Cheng .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Cheng, X., Wang, R. (2014). Communication Network Anomaly Detection Based on Log File Analysis. In: Miao, D., Pedrycz, W., Ślȩzak, D., Peters, G., Hu, Q., Wang, R. (eds) Rough Sets and Knowledge Technology. RSKT 2014. Lecture Notes in Computer Science(), vol 8818. Springer, Cham. https://doi.org/10.1007/978-3-319-11740-9_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-11740-9_23

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11739-3

  • Online ISBN: 978-3-319-11740-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics