TY  - JOUR
IS  - 4
KW  - Attack; Dataset; Detection rates; False negatives; False positive
KW  -  Genetic algorithms; Network security; Neural networks; Optimization; Principal component analysis; Soft computing; Support vector machines
KW  -  Intrusion detection
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84879897823&doi=10.1007%2fs11235-011-9541-1&partnerID=40&md5=0fcf74923fab937bf27d57eaa35575cd
ID  - scholars3684
AV  - none
N1  - cited By 26
Y1  - 2013///
N2  - Intrusion detection is an important technique in computer and network security. A variety of intrusion detection approaches be present to resolve this severe issue but the main problem is performance. It is important to increase the detection rates and reduce false alarm rates in the area of intrusion detection. Therefore, in this research, an optimized intrusion detection mechanism using soft computing techniques is proposed to overcome performance issues. The KDD-cup dataset is used that is a benchmark for evaluating the security detection mechanisms. The Principal Component Analysis (PCA) is applied to transform the input samples into a new feature space. The selecting of an appropriate number of principal components is a critical problem. So, Genetic Algorithm (GA) is used in the optimum selection of principal components instead of using traditional method. The Support Vector Machine (SVM) is used for classification purpose. The performance of this approach is addresses. Further, a comparative analysis is made with existing approaches. Consequently, this method provides optimal intrusion detection mechanism which is capable to minimize amount of features and maximize the detection rates. © 2011 Springer Science+Business Media, LLC.
A1  - Ahmad, I.
A1  - Abdullah, A.
A1  - Alghamdi, A.
A1  - Hussain, M.
SP  - 2187
EP  - 2195
VL  - 52
SN  - 10184864
TI  - Optimized intrusion detection mechanism using soft computing techniques
JF  - Telecommunication Systems
ER  -