Enhancing SVM performance in intrusion detection using optimal feature subset selection based on genetic principal components

Title
Enhancing SVM performance in intrusion detection using optimal feature subset selection based on genetic principal components
Authors
Keywords
Intrusion detection system (IDS), Support vector machines (SVMs), Principal component analysis (PCA), Genetic algorithm (GA), Genetic principal component (GPC), Detection rate (DR) and dataset
Journal
NEURAL COMPUTING & APPLICATIONS
Volume 24, Issue 7-8, Pages 1671-1682
Publisher
Springer Nature
Online
2013-04-11
DOI
10.1007/s00521-013-1370-6

Ask authors/readers for more resources

Reprint

Contact the author

Find the ideal target journal for your manuscript

Explore over 38,000 international journals covering a vast array of academic fields.

Search

Create your own webinar

Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.

Create Now