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Online and Adaptive Signature Learning for Intrusion Detection

by Kamran Shafi
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Current price ₹7,306.00
Original price ₹8,295.00
Original price ₹8,295.00
Original price ₹8,295.00
(-12%)
₹7,306.00
Current price ₹7,306.00

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Book cover type: Paperback
  • ISBN13: 9783639136302
  • Binding: Paperback
  • Subject: N/A
  • Publisher: VDM Verlag
  • Publisher Imprint: VDM Verlag
  • Publication Date:
  • Pages: 284
  • Original Price: GBP 65.57
  • Language: English
  • Edition: N/A
  • Item Weight: 418 grams
  • BISAC Subject(s): Information Technology

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.

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