TY  - CONF
VL  - 2
SP  - 569
AV  - none
ID  - scholars2830
A1  - Kausar, N.
A1  - Belhaouari Samir, B.
A1  - Sulaiman, S.B.
A1  - Ahmad, I.
A1  - Hussain, M.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84867888553&doi=10.1109%2fICCISci.2012.6297095&partnerID=40&md5=89c134a73502388d8fda9b47dea13479
N1  - cited By 21; Conference of 2012 International Conference on Computer and Information Science, ICCIS 2012 - A Conference of World Engineering, Science and Technology Congress, ESTCON 2012 ; Conference Date: 12 June 2012 Through 14 June 2012; Conference Code:93334
CY  - Kuala Lumpur
EP  - 574
N2  - Presently many intrusion detection approaches are available but have drawbacks like training overhead as well as their performance factor. Increased detection rate with less false alarms can enhanced the efficiency of an intrusion detection system. One of the main limitations is the processing of raw features for classification which increases the architecture complexity and decreases the accuracy of detecting intrusions. Because of the limitations in earlier approaches, this PCA based subsets has been proposed. An SVM based IDS mechanism with Principal Component Analysis (PCA) feature subsets has been presented. Support Vector Machines (SVM) used as classifier to test and train the subsets of extracted features with Radial Basis Function (RBF) kernel. © 2012 IEEE.
SN  - 9781467319386
KW  - Detection rates; False alarms; Feature subset; Intrusion detection approaches; Intrusion Detection Systems; Knowledge discovery and data minings; Performance factors; Radial basis functions; Training overhead
KW  -  Computer crime; Information science; Intrusion detection; Principal component analysis; Radial basis function networks; Set theory; Technology; Websites
KW  -  Support vector machines
TI  - An approach towards intrusion detection using PCA feature subsets and SVM
Y1  - 2012///
ER  -