2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541)
DOI: 10.1109/ijcnn.2004.1380189
|View full text |Cite
|
Sign up to set email alerts
|

Feature selection for intrusion detection: an evolutionary wrapper approach

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
18
0

Publication Types

Select...
5
1

Relationship

0
6

Authors

Journals

citations
Cited by 32 publications
(18 citation statements)
references
References 20 publications
0
18
0
Order By: Relevance
“…This data was originally discussed in ( [4,5,13]) and the actual data used in this paper was also discussed in ( [6][7][8]). The Sick data was originally examined in [11]. The parameter ν is set to .5 for every experiment.…”
Section: Resultsmentioning
confidence: 99%
“…This data was originally discussed in ( [4,5,13]) and the actual data used in this paper was also discussed in ( [6][7][8]). The Sick data was originally examined in [11]. The parameter ν is set to .5 for every experiment.…”
Section: Resultsmentioning
confidence: 99%
“…Feature selection based on rough set was introduced by [12]. (Stein et al, 2005) [13] utilized decision tree classifiers with genetic algorithm in intrusion detection approach while (Hofman et al,2004) [16] gave evolutionary wrapper approach (combining radial basis function with genetic algorithm). Genetic algorithm along with naïve bayes classification was used by (Lee et al, 2006) [11] to generate optimized results for classification on KDD99 dataset.…”
Section: Related Workmentioning
confidence: 99%
“…To ensure that for each rule, the corresponding class selected is correct, following formula is applied- (16) Hence the main objective is to maximize T and thus cover approximately all the samples of training dataset. Therefore the feasibility of obtaining the best rule set will be maximized which will improve the classification of attacks in testing dataset as more features will be available for classification.…”
Section: Fuzzy Rule Based Systemmentioning
confidence: 99%
“…For an analysis of the selected features for the present data set, in particular for Guest, see [75]. A lot of possible extensions or improvements of the EA are discussed in [70].…”
Section: Automated Feature Selection and Structure Optimizationmentioning
confidence: 99%