File(s) under permanent embargo
Using feature selection for intrusion detection system
conference contribution
posted on 2012-01-01, 00:00 authored by A Alazab, Michael HobbsMichael Hobbs, Jemal AbawajyJemal Abawajy, Moutaz AlazabA good intrusion system gives an accurate and efficient classification results. This ability is an essential functionality to build an intrusion detection system. In this paper, we focused on using various training functions with feature selection to achieve high accurate results. The data we used in our experiments are NSL-KDD. However, the training and testing time to build the model is very high. To address this, we proposed feature selection based on information gain, which can detect several attack types with high accurate result and low false rate. Moreover, we executed experiments to category each of the five classes (probe, denial of service (DoS), user to super-user (U2R), and remote to local (R2L), normal). Our proposed outperform other state-of-art methods.
History
Event
International Symposium on Communications and Information Technologies (12th : 2012 : Gold Coast, Qld)Pagination
296 - 301Publisher
IEEELocation
Gold Coast, QldPlace of publication
[Piscataway, N. J.]Publisher DOI
Start date
2012-10-02End date
2012-10-05ISBN-13
9781467311571Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2012, IEEETitle of proceedings
ISCIT 2012 : Proceedings of the 12th IEEE International Symposium on Communications and Information TechnologiesUsage metrics
Categories
No categories selectedLicence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC