Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 39, Issue 1, Pages 424-430Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2011.07.032
Keywords
Intrusion detection; Support vector machine; Feature reduction
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The efficiency of the intrusion detection is mainly depended on the dimension of data features. By using the gradually feature removal method, 19 critical features are chosen to represent for the various network visit. With the combination of clustering method, ant colony algorithm and support vector machine (SVM), an efficient and reliable classifier is developed to judge a network visit to be normal or not. Moreover, the accuracy achieves 98.6249% in 10-fold cross validation and the average Matthews correlation coefficient (MCC) achieves 0.861161. (C) 2011 Elsevier Ltd. All rights reserved.
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