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Evaluating Host-Based Anomaly Detection Systems: Application of the Frequency-Based Algorithms to ADFA-LD

  • Miao Xie
  • Jiankun Hu
  • Xinghuo Yu
  • Elizabeth Chang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8792)

Abstract

ADFA Linux data set (ADFA-LD) is released recently for substituting the existing benchmark data sets in the area of host-based anomaly detection which have lost most of their relevance to modern computer systems. ADFA-LD is composed of thousands of system call traces collected from a contemporary Linux local server, with six types of up-to-date cyber attack involved. Previously, we have conducted a preliminary analysis of ADFA-LD, and shown that the frequency-based algorithms can be realised at a cheaper computational cost in contrast with the short sequence-based algorithms, while achieving an acceptable performance. In this paper, we further exploit the potential of the frequency-based algorithms, in attempts to reduce the dimension of the frequency vectors and identify the optimal distance functions. Two typical frequency-based algorithms, i.e., k-nearest neighbour (kNN) and k-means clustering (kMC), are applied to validate the effectiveness and efficiency.

Keywords

host-based intrusion detection system (HIDS) Unix system call 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Miao Xie
    • 1
  • Jiankun Hu
    • 1
  • Xinghuo Yu
    • 2
  • Elizabeth Chang
    • 1
  1. 1.UNSW CanberraCanberraAustralia
  2. 2.RMIT UniversityMelbourneAustralia

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