Abstract
Many traditional algorithms use single metric generated by multi-events to detect intrusion by comparison with a certain threshold. In this paper we present a metric vector-based algorithm to detect intrusion while introducing the sample distance for both discrete and continuous data in order to improve the algorithm on heterogeneous dataset. Experiments on MIT lab Data show that the proposed algorithm is effective and efficient.
This work is supported by the National Natural Science Foundation of China (60573097), Natural Science Foundation of Guangdong Province (05200302,04300462), Research Foundation of National Science and Technology Plan Project (2004BA721A02), Research Foundation of Science and Technology Plan Project in Guangdong Province (2005B10101032) and Research Foundation of Disciplines Leading to Doctorate degree of Chinese Universities(20050558017).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yin, J., Mei, F., Zhang, G. (2006). Intrusion Detection Based on Data Mining. In: Huang, DS., Li, K., Irwin, G.W. (eds) Computational Intelligence. ICIC 2006. Lecture Notes in Computer Science(), vol 4114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-37275-2_90
Download citation
DOI: https://doi.org/10.1007/978-3-540-37275-2_90
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37274-5
Online ISBN: 978-3-540-37275-2
eBook Packages: Computer ScienceComputer Science (R0)