Abstract: Multiclass Support Vector Machines (MSVM) have been applied to build classifiers, which can help Network Intrusion detection. Beside their high generalization accuracy, the learning time of MSVM classifiers is still a concern when applied into Network intrusion detection systems. This paper speeds up the learning time of MSVM classifiers by reducing the number of support vectors. In this study, we proposed KMSVM method combines the K-means clustering technique with custom kernel in MSVM. Experiments performed on KDD99 dataset using KMSVM method, and the results show that the KMSVM method can speed up the learning time of classifiers by both reducing support vectors and improve the detection rate on testing dataset.

Arvind Mewada, Shamila Khan and Prafful Gedam, “Network Anomaly Detection via Clustering and Custom Kernel in MSVM” International Journal of Advanced Computer Science and Applications(IJACSA), 1(1), 2010. http://dx.doi.org/10.14569/IJACSA.2010.010105

@article{Mewada2010,
title = {Network Anomaly Detection via Clustering and Custom Kernel in MSVM},
journal = {International Journal of Advanced Computer Science and Applications}
doi = {10.14569/IJACSA.2010.010105},
url = {http://dx.doi.org/10.14569/IJACSA.2010.010105},
year = {2010},
publisher = {The Science and Information Organization},
volume = {1},
number = {1},
author = {Arvind Mewada and Shamila Khan and Prafful Gedam},
}