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)


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.


Mahalanobis Distance Anomaly Detection Attack Type Probe Attack Anomaly Detection Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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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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