Anomaly Detection Enhanced Classification in Computer Intrusion Detection

  • Mike Fugate
  • James R. Gattiker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2388)

Abstract

This paper describes experiences and results applying Support Vector Machine (SVM) to a Computer Intrusion Detection (CID) dataset. This is the second stage of work with this dataset, emphasizing incorporation of anomaly detection in the modeling and prediction of cyber-attacks. The SVM method for classification is used as a benchmark method (from previous study [1] ), and the anomaly detection approaches compare so-called “one class” SVMs with a thresholded Mahalanobis distance to define support regions. Results compare the performance of the methods, and investigate joint performance of classification and anomaly detection. The dataset used is the DARPA/KDD-99 publicly available dataset of features from network packets classified into non-attack and four attack categories.

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References

  1. 1.
    Mike Fugate, James R. Gattiker, “Detecting Attacks in Computer Networks”, Los Alamos National Laboratory Technical Report, LA-UR-02-1149.Google Scholar
  2. 2.
    Richard P. Lippmann et al., “Evaluating Intrusion Detection Systems: The 1998 DARPA Off-line Intrusion Detection Evaluation”, Proc of the DARPA Information Survivability Conf., vol. 2, pp. 12–26, 1999.CrossRefGoogle Scholar
  3. 3.
    Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer-Verlag, 2001.Google Scholar
  4. 4.
    Ronald Christensen (1996), Plane Answers to Complex Questions: The Theory of Linear Models, Second Edition. New York: Springer-Verlag.MATHGoogle Scholar
  5. 5.
    Ronald Christensen (2001), Advanced Linear Modeling, Second Edition. New York: Springer-Verlag.MATHGoogle Scholar
  6. 6.
    Bernhard Schölkopf, et al. (2000). “Estimating the Support of a High-Dimensional Distribution”, Technical report MSR-TR-99-87, Microsoft Research, Microsoft Corporation.Google Scholar
  7. 7.
    C. Chang, C. Lin, ”LIBSVM: a library for support vector machines”, http://www.csie.ntu.edu.tw/cjlin/papers/libsvm.ps.gz
  8. 8.
    T. Joachims, “Making large-Scale SVM Learning Practical”, Advances in Kernel Methods-Support Vector Learning, B. Schölkopf and C. Burges and A. Smola (ed.), MIT-Press, 1999.Google Scholar
  9. 9.
    M. Gokhale, D. Dubois, A. Dubois, M. Boorman, ”Gigabit Rate Network Intrusion Detection Technology”, Los Alamos National Laboratory Technical Report, LA-UR-01-6185.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Mike Fugate
    • 1
  • James R. Gattiker
    • 1
  1. 1.Los Alamos National LaboratoryUSA

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