Increasing the rate of intrusion detection based on a hybrid technique

Author

Ali Alheeti, Khattab M. ; Al-Jobouri, Laith ; McDonald-Maier, K.

Author_Institution

Coll. of Comput., Univ. of Anbar, Al-Anbar, Iraq

fYear

2013

fDate

17-18 Sept. 2013

Firstpage

179

Lastpage

182

Abstract

This paper presents techniques to increase intrusion detection rates. Theses techniques are based on specific features that are detected and it´s shown that a small number of features (9) can yield improved detection rates compared to higher numbers. These techniques utilize soft computing techniques such a Backpropagation based artificial neural networks and fuzzy sets. These techniques achieve a significant improvement over the state of the art for standard DARPA benchmark data.

Keywords

backpropagation; fuzzy set theory; neural nets; security of data; backpropagation based artificial neural networks; fuzzy sets; hybrid technique; intrusion detection rate; soft computing techniques; standard DARPA benchmark data; Accuracy; Artificial neural networks; Educational institutions; Feature extraction; Intrusion detection; Training; Fuzzy set; Intrusion Detection; Neural Networks; Soft Computing;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Electronic Engineering Conference (CEEC), 2013 5th

Conference_Location

Colchester

Type

conf

DOI

10.1109/CEEC.2013.6659468

Filename

6659468

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2174706