TY  - JOUR
N2  - Presently, Network security is the most concerned subject matter because with the rapid use of internet technology and further dependence on network for keeping our data secure, it's becoming impossible to protect from vulnerable attacks. Intrusion detection systems (IDS) are the key solution for detecting these attacks so that the network remains reliable. There are different classification approaches used to implement IDS in order to increase their efficiency in terms of detection rate. Support vector machine (SVM) is used for classification in IDS due to its good generalization ability and non linear classification using different kernel functions and performs well as compared to other classifiers. Different Kernels of SVM are used for different problems to enhance performance rate. In this paper, we provide a review of the SVM and its kernel approaches in IDS for future research and implementation towards the development of optimal approach in intrusion detection system with maximum detection rate and minimized false alarms. © 2011 Springer-Verlag.
SN  - 18650929
IS  - PART 3
KW  - Defense advanced research projects agencies; Intrusion Detection System (IDS); Kernel; Knowledge Discovery and Data Mining (KDD); RBF; SVM
KW  -  Computer crime; Information science; Network security; Research; Support vector machines
KW  -  Intrusion detection
TI  - A review of classification approaches using support vector machine in intrusion detection
ID  - scholars1455
EP  - 34
CY  - Kuala Lumpur
SP  - 24
AV  - none
Y1  - 2011///
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-82955206471&doi=10.1007%2f978-3-642-25462-8_3&partnerID=40&md5=8cd250bddcf354cf46240f3dc525f945
A1  - Kausar, N.
A1  - Belhaouari Samir, B.
A1  - Abdullah, A.
A1  - Ahmad, I.
A1  - Hussain, M.
N1  - cited By 20; Conference of International Conference on Informatics Engineering and Information Science, ICIEIS 2011 ; Conference Date: 14 November 2011 Through 16 November 2011; Conference Code:87535
JF  - Communications in Computer and Information Science
VL  - 253 CC
ER  -