Paper Info
Title
Kernel Density Estimation for An Anomaly Based Intrusion Detection System
Abstract
This paper presents a new nonparametric method to simulate probability density functions of some random variables raised in characterizing an anomaly based intrusion detection system (ABIDS). A group of kernel density estimators is constructed and the criterions for bandwidth selection are discussed. In addition, statistical parameters of these distributions are computed, which can be used directly in ABIDS modeling. Statistical experiments and numerical simulations are used to test these estimators.
Year
Venue
Keywords
2006
MLMTA
anomaly based ids abids,intrusion detection system ids,index terms — kernel density estimation,self- organizing maps som.,kernel density estimate,intrusion detection system,probability density function,indexing terms,numerical simulation,random variable
Field
DocType
Citations 
Density estimation,Multivariate kernel density estimation,Pattern recognition,Kernel embedding of distributions,Anomaly-based intrusion detection system,Artificial intelligence,Variable kernel density estimation,Mathematics,Kernel density estimation,Kernel (statistics)
Conference
4
PageRank 
References 
Authors
0.43
3
2
Name
Order
Citations
PageRank
Xiaoping Shen1193.67
Sonali Agrawal240.43