DocumentCode :
2174706
Title :
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 :
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