A Deviation Based Outlier Intrusion Detection System

With the significant increase in use of networks, network security has become more important and challenging. An intrusion detection system plays a major role in providing security. This paper proposes a model in which Artificial Neural Network and Data Mining approaches are used together. In this m...

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Bibliographic Details
Published inRecent Trends in Network Security and Applications pp. 395 - 401
Main Authors Pareek, Vikas, Mishra, Aditi, Sharma, Arpana, Chauhan, Rashmi, Bansal, Shruti
Format Book Chapter
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2010
SeriesCommunications in Computer and Information Science
Subjects
Artificial Neural Network
Data mining
Internet
Intrusion Detection
Network Security
Online AccessGet full text
ISBN9783642144776
3642144772
ISSN1865-0929
1865-0937
DOI10.1007/978-3-642-14478-3_39

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Summary:With the significant increase in use of networks, network security has become more important and challenging. An intrusion detection system plays a major role in providing security. This paper proposes a model in which Artificial Neural Network and Data Mining approaches are used together. In this model “Self Organizing Map” approach is used for behavior learning and “Outlier Mining” approach is used for detecting an intruder. The scope of the proposed model is for internet. This model improves the capability of detecting intruders: both masqueraders and misfeasors.
ISBN:9783642144776
3642144772
ISSN:1865-0929
1865-0937
DOI:10.1007/978-3-642-14478-3_39