Feature selection, learning metrics and dimension reduction in training and classification processes in intrusion detection systems
Artículo de revista
2015-12-20
Journal of Theoretical and Applied Information Technology
This research presents an IDS prototype in Matlab that assess network traffic connections contained in the
NSL-KDD dataset, comparing feature selection techniques available in FEAST toolbox, refining prior
results applying dimension reduction technique ISOMAP. The classification process used a supervised
learning technique called Support Vector Machines (SVM). The comparative analysis related to detection
rates by attack category are conclusive that MRMR+PCA+SVM (selection, reduction and classification
techniques) combined obtained more promising results, just using 5 of 41 available features in the dataset.
The results obtained were: 85.42% normal traffic, 80.77% DoS, 90.41% Probe, 91.78% U2R and 83.25%
R2L.
- Artículos científicos [3154]
Descripción:
Feature selection, learning metrics and dimension reduction in training and classification processes in intrusion detection systems.pdf
Título: Feature selection, learning metrics and dimension reduction in training and classification processes in intrusion detection systems.pdf
Tamaño: 577.0Kb
PDFLEER EN FLIP
Título: Feature selection, learning metrics and dimension reduction in training and classification processes in intrusion detection systems.pdf
Tamaño: 577.0Kb
PDFLEER EN FLIP