Abstract
This paper mainly discusses the application of rough-set theory in intrusion detection, establishes the rough-set intrusion detection system model by applying the attribute reduction algorithm of rough set to mine the intrusion detection data, and improves the reduction algorithm which is based on attribute frequency, enhancing the data mining efficiency, and helping obtain concise and efficient data.
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
References
Cai ZM, Guan XH (2003) New intrusion detection approach based on rough set theory. Chin J Comput 5(3):361–366
Zhang WX, Wu WZ, Liang JY, Li DY (2001) Rough set theory and approach, 1st edn, vol 4(45). Science Press, Beijing, pp 89–90
Zeng HG (1996) Rough set theory and application-on the new approach of data reasoning, 1st edn, vol 78(34). Chongqing University Press, Chongqing, pp 3–5
G. Y. Wang (2001) Rough set theory and knowledge acquisition, 1st edn, vol 34(6). Xi’an Jiaotong University Press, Xi’an, pp 13–15
Wang ZH, Hu KY, Hu XJ (1998) Knowledge discovery review base on rough set theory. Pattern Recognit Artif Intell 6(2):176–183
Yao MC (2002) Study and implementation on the attribute reduction algorithm based on rough set. Harbin Institute of Technology. Master Degree Thesis 16(4):56–57
Tang Z, Cao JY (2009) SVM abnormal intrusion detection approach based on the rough set attribute reduction. Commun Technol 65(2):261–263
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag London
About this paper
Cite this paper
Ma, S., Liao, H., Yuan, Y. (2013). Intrusion Detection Based on Rough-Set Attribute Reduction. In: Zhong, Z. (eds) Proceedings of the International Conference on Information Engineering and Applications (IEA) 2012. Lecture Notes in Electrical Engineering, vol 219. Springer, London. https://doi.org/10.1007/978-1-4471-4853-1_47
Download citation
DOI: https://doi.org/10.1007/978-1-4471-4853-1_47
Published:
Publisher Name: Springer, London
Print ISBN: 978-1-4471-4852-4
Online ISBN: 978-1-4471-4853-1
eBook Packages: EngineeringEngineering (R0)