Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm

10.1023/A:1018628609742

hsu, 0, A practical guide to support vector classification

10.1103/PhysRevE.69.066138

10.1016/j.chemolab.2005.06.010

10.1109/TNN.2004.841414

10.1109/TPAMI.2005.159

press, 1986, Numerical Recipes

10.1016/j.dss.2012.08.014

10.1109/TNN.2008.2005601

10.1109/72.977291

fukunaga, 2013, Introduction to statistical pattern recognition

s, 1999, Estimating the errors on measured entropy and mutual information, Physica D Nonlinear Phenomena, 125, 285, 10.1016/S0167-2789(98)00269-3

10.1103/PhysRevE.52.2318

10.1145/846183.846200

10.1109/TC.2012.105

10.1016/j.cose.2014.06.006

hoz, 0, Network anomaly classification by support vector classifiers ensemble and non-linear projection techniques, Proc 8th Int Conf Hybrid Artif Intell Syst, 8073, 103, 10.1007/978-3-642-40846-5_11

10.1007/978-3-642-27189-2_21

10.1109/CISDA.2009.5356528

m, 2014, A proposed http service based ids, Egyptian Informatics J, 15, 13, 10.1016/j.eij.2014.01.001

cover, 2012, Elements of Information Theory

10.1145/1978672.1978676

10.1007/978-3-642-40597-6_21

10.1137/1.9781611972719.29

bouzida, 0, Neural networks vs. decision trees for intrusion detection, Proc of IEEE/IST Workshop on Monitoring Attack Detection and Mitigation (MonAM 2006), 28

liu, 2014, Applying a new localized generalization error model to design neural networks trained with extreme learning machine, Neural Comput Appl, 1

aghamohammadi, 2012, A comparison of support vector machine and multi-level support vector machine on intrusion detection, World Comput Sci Inf Technol, 2, 215

10.1016/j.patcog.2006.12.009

10.1109/CIMCA.2006.148

10.1109/TrustCom.2014.15

10.1109/72.298224

10.1109/TSMCB.2011.2168604

10.1016/j.jnca.2011.01.002

10.1016/j.jnca.2005.06.001

10.1007/1-84628-284-5_11

10.1016/j.cose.2004.09.008

10.1016/j.neucom.2006.01.022

10.1016/j.eswa.2010.06.066

10.1016/j.eswa.2013.08.066

10.1007/978-3-642-32129-0_34

10.1007/978-3-540-45235-5_73

10.1145/846183.846201

10.1016/j.jnca.2004.01.003

10.1007/978-1-4614-6154-8_49

10.1109/TC.2014.2375218

10.1016/j.comcom.2007.05.002

10.1016/j.protcy.2012.05.017

10.1109/Trustcom.2015.387

10.1016/j.asoc.2007.10.012

10.1016/j.eswa.2007.08.088

10.1007/978-3-642-20505-7_26

panda, 0, Discriminative multinomial naive Bayes for network intrusion detection, Proc 6th Int Conf Inf Assurance Security, 5

rifkin, 2004, In defense of one-vs-all classification, Journal of Machine Learning Research, 5, 101

10.1109/DISCEX.2000.821515

croft, 2010, Search Engines Information Retrieval in Practice

10.1016/j.eswa.2009.05.029