Abstract/Details

On-line adaptive IDS scheme for detecting unknown network attacks using HMM models


2005 2005

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Abstract (summary)

An important problem in designing IDS schemes is an optimal trade-off between good detection and false alarm rate.

Specifically, in order to detect unknown network attacks, existing IDS schemes use anomaly detection which introduces a high false alarm rate. In this thesis we propose an IDS scheme based on overall behavior of the network. We capture the behavior with probabilistic models (HMM) and use only limited logic information about attacks. Once we set the detection rate to be high, we filter out false positives through stages. The key idea is to use probabilistic models so that even an unknown attack can be detected, as well as a variation of a previously known attack. The scheme is adaptive and real-time.

Simulation study showed that we can have a perfect detection of both known and unknown attacks while maintaining a very low false alarm rate.

Indexing (details)


Subject
Electrical engineering
Classification
0544: Electrical engineering
Identifier / keyword
Applied sciences
Title
On-line adaptive IDS scheme for detecting unknown network attacks using HMM models
Author
Bojanic, Irena
Number of pages
89
Publication year
2005
Degree date
2005
School code
0117
Source
MAI 43/06M, Masters Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9780542127434, 0542127431
Advisor
Baras, John S.
University/institution
University of Maryland, College Park
University location
United States -- Maryland
Degree
M.S.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
1426808
ProQuest document ID
304994440
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/304994440
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