Abstract
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.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
National Vulnerability Database [NVD]: http://nvd.nist.gov
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)
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)
Zhang, Z., Shen, H.: Application of Online-Training SVMs for Real-Time Intrusion Detection with Different Considerations. Computer Communications 28, 1428–1442 (2005)
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)
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)
Pfahringer, B.: Winning the KDD99 Classification Cup: Bagged Boosting. KDD 1999 1(2), 65–66 (2000)
Levin, I.: KDD-99 Classifier Learning Contest LLSoft’s Results Overview. SIGKDD Explorations 1(2), 67–75 (2000)
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)
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)
Louvieris, P., Clewley, N., Liu, X.: Effects-Based Feature Identification for Network Intrusion Detection. Neurocomputing 121, 265–273 (2013)
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)
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)
Quinlan, J.R.: Induction of Decision Trees. Machine Learning 1(1), 81–106 (1986)
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)
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)
Quinlan, J.R.: Unknown Attribute Values in Induction. In: Segre, A. (ed.) Proceedings of the 6th International Machine Learning Workshop Cornell. Morgan Kaufmann (1989)
Bache, K., Lichman, M.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA (2013), http://archive.ics.uci.edu/ml
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights 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. https://doi.org/10.1007/978-3-319-12643-2_74
Download citation
DOI: https://doi.org/10.1007/978-3-319-12643-2_74
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12642-5
Online ISBN: 978-3-319-12643-2
eBook Packages: Computer ScienceComputer Science (R0)