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Title: Construction of low false alarm and high precision RBFNN for detecting flooding based denial of service attacks using stochastic sensitivity measure
Authors: Ng, WWY
Chan, APF
Yeung, DS
Tsang, ECC
Keywords: Learning (artificial intelligence)
Radial basis function networks
Security of data
Stochastic processes
DoS attacks
Artificial datasets
False alarm construction
Flooding-based denial of service
Generalization error
Intrusion detection system
Machine learning
Precision RBFNN
Stochastic sensitivity measure
Computer crime
Covariance matrix
Intrusion detection
Knowledge based systems
Machine learning
Neural networks
Issue Date: 2005
Publisher: IEEE
Source: Proceedings of 2005 International Conference on Machine Learning and Cybernetics, 2005, 18-21 August 2005, Guangzhou, China, v. 8, p. 4674-4679 How to cite?
Abstract: A good intrusion detection system (IDS) should have high precision on detecting attacks and low false alarm rates. Machine learning techniques for IDS usually yield high false alarm rate. In this work, we propose to construct host-based IDS for flooding-based denial of service (DoS) attacks by minimizing the generalization error bound of the IDS to reduce its false alarm rate and increase its precision. Experiments using artificial datasets support our claims.
ISBN: 0-7803-9091-1
DOI: 10.1109/ICMLC.2005.1527763
Appears in Collections:Conference Paper

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