Skip to main content

Anomaly Based Intrusion Detection through Temporal Classification

  • Conference paper
Neural Information Processing (ICONIP 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8836))

Included in the following conference series:


Many machine learning techniques have been used to classify anomaly-based network intrusion data, encompassing from single classifier to hybrid or ensemble classifiers. A nonlinear temporal data classification is proposed in this work, namely Temporal-J48, where the historical connection records are used to classify the attack or predict the unseen attack. With its tree-based architecture, the implementation is relatively simple. The classification information is readable through the generated temporal rules. The proposed classifier is tested on 1999 KDD Cup Intrusion Detection dataset from UCI Machine Learning Repository. Promising results are reported for denial-of-service (DOS) and probing attack types.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others


  1. National Vulnerability Database [NVD]:

  2. Tsai, C.-F., Hsu, Y.-F., Lin, C.-Y., Lin, W.-Y.: Intrusion Detection by Machine Learning: A Review. Expert Systems with Application 36, 11994–12000 (2009)

    Article  Google Scholar 

  3. Joo, D., Hong, T., Han, I.: The Neural Network Models for IDS Based on the Asymmetric Costs of False Negative Errors and False Positive Errors. Expert Systems with Application 25, 69–75 (2003)

    Article  Google Scholar 

  4. Zhang, Z., Shen, H.: Application of Online-Training SVMs for Real-Time Intrusion Detection with Different Considerations. Computer Communications 28, 1428–1442 (2005)

    Article  Google Scholar 

  5. Stein, G., Chen, B., Wu, A.S., Hua, K.A.: Decision Tree Classifier for Network Intrusion Detection with GA-Based Feature Selection. In: Proceedings of the 43rd Annual Southeast Regional Conference, vol. 2, pp. 136–141 (2005)

    Google Scholar 

  6. Peddabachigari, S., Abraham, A., Grosan, C., Thomas, J.: Modeling Intrusion Detection System Using Hybrid Intelligent Systems. Journal of Network and Computer Applications 30, 114–132 (2007)

    Article  Google Scholar 

  7. Pfahringer, B.: Winning the KDD99 Classification Cup: Bagged Boosting. KDD 1999 1(2), 65–66 (2000)

    Article  Google Scholar 

  8. Levin, I.: KDD-99 Classifier Learning Contest LLSoft’s Results Overview. SIGKDD Explorations 1(2), 67–75 (2000)

    Article  Google Scholar 

  9. Xuren, W., Famei, H., Rongsheng, X.: Modeling Intrusion Detection System by Discovering Association Rule in Rough Set Theory Framework. In: Proceedings of the International Conference on Computational Intelligence for Modeling Control and Automation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC), p. 24 (2006)

    Google Scholar 

  10. Toosi, A.N., Kahani, M.: A New Approach to Intrusion Detection Based on an Evolutionary Soft Computing Model Using Neuro-Fuzzy Classifiers. Computer Communications 30, 2201–2212 (2007)

    Article  Google Scholar 

  11. Louvieris, P., Clewley, N., Liu, X.: Effects-Based Feature Identification for Network Intrusion Detection. Neurocomputing 121, 265–273 (2013)

    Article  Google Scholar 

  12. Horng, S.-J., Su, M.-Y., Chen, Y.-H., Kao, T.-W., Chen, R.-J., Lai, J.-L., Perkasa, C.D.: A Novel Intrusion Detection System Based On Hierarchical Clustering and Support Vector Machines. Expert Systems with Applications 38, 306–313 (2011)

    Article  Google Scholar 

  13. Feng, W., Zhang, Q., Hu, G., Huang, J.X.: Mining Network Data for Intrusion Detection through Combining SVMs with Ant Colony Networks. Future Generation Computer Systems 37, 127–140 (2014)

    Article  Google Scholar 

  14. Quinlan, J.R.: Induction of Decision Trees. Machine Learning 1(1), 81–106 (1986)

    Google Scholar 

  15. Karimi, K., Hamilton, H.J.: Temporal Rules and Temporal Decision Trees: A C4.5 Approach. Technical Report CS-2001-02, Department of Computer Science, University of Regina, Canada (2001)

    Google Scholar 

  16. Hall, M.A., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Ian, H.W.: The WEKA Data Mining Software: An Update. SIGKDD Explorations 11(1) (2009)

    Google Scholar 

  17. Quinlan, J.R.: Unknown Attribute Values in Induction. In: Segre, A. (ed.) Proceedings of the 6th International Machine Learning Workshop Cornell. Morgan Kaufmann (1989)

    Google Scholar 

  18. Bache, K., Lichman, M.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA (2013),

Download references

Author information

Authors and Affiliations


Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Ooi, S.Y., Tan, S.C., Cheah, W.P. (2014). Anomaly Based Intrusion Detection through Temporal Classification. In: Loo, C.K., Yap, K.S., Wong, K.W., Beng Jin, A.T., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8836. Springer, Cham.

Download citation

  • DOI:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12642-5

  • Online ISBN: 978-3-319-12643-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics