TY  - JOUR
SN  - 19928645
PB  - Asian Research Publishing Network (ARPN)
EP  - 63
AV  - none
N1  - cited By 3
TI  - Efficient intrusion detection system based on support vector machines using optimized kernel function
SP  - 55
Y1  - 2014///
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893732306&partnerID=40&md5=1858620823dc495e2e84a03b8513fbf0
JF  - Journal of Theoretical and Applied Information Technology
A1  - Kausar, N.
A1  - Samir, B.B.
A1  - Ahmad, I.
A1  - Hussain, M.
VL  - 60
N2  - An efficient intrusion detection system requires fast processing and optimized performance. Architectural complexity of the classifier increases by the processing of the raw features in the datasets which causes heavy load and needs proper transformation and representation. PCA is a traditional approach for dimension reduction by finding linear combinations of original features into lesser number. Support vector machine performs well with different kernel functions that classifies in higher dimensional at optimized parameters. The performance of these kernels can be examined by using variant feature subsets at respective parametric values. In this paper SVM based intrusion detection is proposed by using PCA transformed features with different kernel functions. This results in optimal kernel of SVM for feature subset with fewer false alarms and increased detection rate. © 2005 - 2014 JATIT & LLS. All rights reserved.
IS  - 1
ID  - scholars5479
ER  -