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Research on Cost-Sensitive Learning in One-Class Anomaly Detection Algorithms

  • Jun Luo
  • Li Ding
  • Zhisong Pan
  • Guiqiang Ni
  • Guyu Hu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4610)

Abstract

According to the Cost-Sensitive Learning Method, two improved One-Class Anomaly Detection Models using Support Vector Data Description (SVDD) are put forward in this paper. Improved Algorithm is included in the Frequency-Based SVDD (F-SVDD) Model while Input data division method is used in the Write-Related SVDD (W-SVDD) Model. Experimental results show that both of the two new models have a low false positive rate compared with the traditional one. The true positives increased by 22% and 23% while the False Positives decreased by 58% and 94%, which reaches nearly 100% and 0% respectively. And hence, adjusting some parameters can make the false positive rate better. So using Cost-Sensitive method in One-Class Problems may be a future orientation in Trusted Computing area.

Keywords

Intrusion Detection Anomaly Detection System Call Support Vector Data Description Slide Window Size 
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 2007

Authors and Affiliations

  • Jun Luo
    • 1
  • Li Ding
    • 1
  • Zhisong Pan
    • 1
  • Guiqiang Ni
    • 1
  • Guyu Hu
    • 1
  1. 1.Institute of Command Automation, PLA University of Science and Technology, 210007, Nanjing, Email: zyqs1981@hotmail.comChina

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