Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/11055
Title: Denial of service detection by support vector machines and radial-basis function neural network
Authors: Tsang, GCY
Chan, PPK
Yeung, DS
Tsang, ECC
Keywords: Internet
Learning (artificial intelligence)
Pattern classification
Radial basis function networks
Security of data
Support vector machines
Issue Date: 2004
Publisher: IEEE
Source: Proceedings of 2004 International Conference on Machine Learning and Cybernetics, 2004, 26-29 August 2004, v. 7, p. 4263-4268 How to cite?
Abstract: Denial of service (DoS) problem is one of serious attacks in the Internet. The attackers attempt to exhaust the resource of the service provider in order to prevent legitimate users from using the system. Most of the detecting DoS tools, such as rule-based and threshold detection approaches, rely on the objective opinion of the domain experts. This work aims to apply machine learning techniques, such as radial-basis function neural network (RBFNN) and support vector machines (SVM), to solve the DoS problem and compare which technique, is better to detect DoS. The main advantage of this detection method is that it has the ability to detect or predict new attacks when some patterns are similar to the attack patterns learnt in the past. Thus it can detect novel attacks for which signatures have not been defined.
URI: http://hdl.handle.net/10397/11055
ISBN: 0-7803-8403-2
DOI: 10.1109/ICMLC.2004.1384587
Appears in Collections:Conference Paper

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