Intrusion Detection based on Distance Combination
| Main Authors: | Joffroy Beauquier, Yongjie Hu |
|---|---|
| Format: | Article |
| Bahasa: | eng |
| Terbitan: |
, 2007
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/1081073 |
Daftar Isi:
- The intrusion detection problem has been frequently studied, but intrusion detection methods are often based on a single point of view, which always limits the results. In this paper, we introduce a new intrusion detection model based on the combination of different current methods. First we use a notion of distance to unify the different methods. Second we combine these methods using the Pearson correlation coefficients, which measure the relationship between two methods, and we obtain a combined distance. If the combined distance is greater than a predetermined threshold, an intrusion is detected. We have implemented and tested the combination model with two different public data sets: the data set of masquerade detection collected by Schonlau & al., and the data set of program behaviors from the University of New Mexico. The results of the experiments prove that the combination model has better performances.