Paper Info
Title
Host anomaly detection performance analysis based on system call of neuro-fuzzy using Soundex algorithm and N-gram technique
Abstract
To improve the anomaly intrusion detection system using system calls, this study focuses on neuro-fuzzy learning using the Soundex algorithm which is designed to change feature selection and variable length data into a fixed length learning pattern. That is, by changing variable length sequential system call data into a fixed length behavior pattern using the Soundex algorithm, this study conducted backpropagation neural networks with fuzzy membership function. The neuro-fuzzy and N-gram techniques are applied for anomaly intrusion detection of system calls using sendmail data of UNM to demonstrate its performance.
Year
DOI
Venue
2005
10.1109/ICW.2005.49
ICW/ICHSN/ICMCS/SENET
Keywords
Field
DocType
fixed length learning pattern,fixed length,system calls,call data,host anomaly detection performance analysis,system call,fixed length behavior pattern,sendmail data,soundex algorithm,backpropagation,variable length sequential system,neuro-fuzzy learning,n-gram techniques,variable length sequential system call data,fuzzy membership function,anomaly intrusion detection system,backpropagation neural networks,unm,variable length data,supervisor learning neural networks,anomaly intrusion detection,feature selection,n-gram technique,fuzzy neural nets,host anomaly detection performance,security of data,neuro fuzzy,anomaly detection
Data mining,Anomaly detection,Feature selection,Computer science,System call,Artificial intelligence,Artificial neural network,Intrusion detection system,Soundex,Neuro-fuzzy,Pattern recognition,Algorithm,Backpropagation
Conference
ISBN
Citations 
PageRank 
0-7695-2422-2
3
0.43
References 
Authors
4
1
Name
Order
Citations
PageRank
ByungRae Cha15114.59