A New Network Intrusion Detection Algorithm based on Radial Basis Function Neural Networks Classifier

Content Provider	Semantic Scholar
Author	Hong-Qiang, Jiao Limin, Jia Yan-Hua, Jin
Copyright Year	2012
Abstract	In this work we assumed that anomalies are not concentrated. This assumption can be specified by choosing a reference measure μ which determines a density and a level value ρ. The density then quantifies the degree of concentration and the density level ρ establishes a threshold on the degree that determines anomalies. Hence the reference measure μ and parameter ρ play key roles in the definition of anomalies. Unlike the intrusion detection methods based on supervised methods, the new method dose not needs any labeled data set. So this method is fit to solve the network intrusion detection problems while it difficult to obtain the label data. We proposed a new method to design RBF classifier apply this algorithm to estimate density level set for the data set
Starting Page	170
Ending Page	176
Page Count	7
File Format	PDF HTM / HTML
DOI	10.4156/aiss.vol4.issue1.22
Volume Number	4
Alternate Webpage(s)	http://www.aicit.org/AISS/ppl/AISSVOL4NO1P_P22.pdf
Alternate Webpage(s)	https://doi.org/10.4156/aiss.vol4.issue1.22
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article