Paper Info
Title
Intrusion Detection Using Neural Networks: A Grid Computing Based Data Mining Approach
Abstract
Scientific disciplines such as life sciences as well as security and business fields depend on Knowledge Discovery because of the increasing amount of data being collected and for the complex analyses that need to be performed on them. New techniques, such as parallel, distributed, and grid-based data mining, are often able to overcome some of the characteristics of current data sources such as their large scale, high dimensionality, heterogeneity, and distributed nature. In several of these data mining applications, neural networks can be successfully applied. Moreover, an approach using neural networks seems to be one of the most promising methods for intrusion detection in a computer system or network security today. In this paper we describe a grid computing data mining approach for an intrusion detection application based on neural networks. Detection is carried out through the analyses of internet traffic generated by users in a network computer system.
Year
DOI
Venue
2009
10.1007/978-3-642-10684-2_87
ICONIP
Keywords
Field
DocType
neural network,grid computing,data mining approach,grid-based data mining,network security,data mining application,intrusion detection,neural networks,computer system,current data source,grid computing data mining,intrusion detection application,network computer system,internet traffic,data mining,knowledge discovery
Data mining,Data stream mining,Grid computing,Computer science,Network security,Network Computer,Knowledge extraction,Artificial intelligence,Artificial neural network,Intrusion detection system,Machine learning,Internet traffic
Conference
Volume
ISSN
Citations 
5864
0302-9743
1
PageRank 
References 
Authors
0.34
6
3
Name
Order
Citations
PageRank
Marcello Castellano1405.28
G. Mastronardi216726.29
Gianfranco Tarricone3221.94