A Novel Hybrid LE and SVM with CV in Intrusion Detection
2014 ◽
Vol 644-650
◽
pp. 2572-2576
Keyword(s):
A support vector machine (SVM) model combined Laplacian Eigenmaps (LE) with Cross Validation (CV) is proposed for intrusion detection. In the proposed model, a classifier is adopted to estimate whether an action is an attack or not. Maximum Likelihood Estimation (MLE) is used to estimate the intrinsic dimensions, and LE is used as a preprocessor of SVM to reduce the dimensions of feature vectors then training time is shortened. In order to improve the performance of SVM, CV is used to optimize the parameters of SVM in RBF kernel function. Compared with other detection algorithms, the experimental results show that the proposed model has the advantages: shorter training time, higher accuracy rate and lower false positive rate.
Keyword(s):
2011 ◽
Vol 121-126
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pp. 3745-3749
2021 ◽
2021 ◽
Vol 9
(8)
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pp. 2479-2483
2013 ◽
Vol 694-697
◽
pp. 1987-1992
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2013 ◽
Vol 655-657
◽
pp. 1787-1790