Computers & Internet Books:

Online and Adaptive Signature Learning for Intrusion Detection

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Paperback

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Online and Adaptive Signature Learning for Intrusion Detection by Kamran Shafi
$252.99
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Description

This thesis presents the case of dynamically and adaptively learning signatures for network intrusion detection using genetic based machine learning techniques. The two major criticisms of the signature based intrusion detection systems are their i) reliance on domain experts to handcraft intrusion signatures and ii) inability to detect previously unknown attacks or the attacks for which no signatures are available at the time. In this thesis, we present a biologically-inspired computational approach to address these two issues. This is done by adaptively learning maximally general rules, which are referred to as signatures, from network traffic through a supervised learning classifier system. The rules are learnt dynamically (i.e., using machine intelligence and without the requirement of a domain expert), and adaptively (i.e., as the data arrives without the need to relearn the complete model after presenting each data instance to the current model). Our approach is hybrid in that signatures for both intrusive and normal behaviours are learnt.
Release date NZ
March 25th, 2009
Author
Country of Publication
Germany
Illustrations
black & white illustrations
Imprint
VDM Verlag
Pages
284
Dimensions
152x229x16
ISBN-13
9783639136302
Product ID
2958015

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