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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Evaluating Host-Based Anomaly Detection Systems: Application of the Frequency-Based Algorithms to ADFA-LD
verfasst von : Miao Xie, Jiankun Hu, Xinghuo Yu, Elizabeth Chang
Erschienen in: Network and System Security
Verlag: Springer International Publishing
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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.