@article{(Open Science Index):https://publications.waset.org/pdf/2474,
	  title     = {Unsupervised Clustering Methods for Identifying Rare Events in Anomaly Detection},
	  author    = {Witcha Chimphlee and  Abdul Hanan Abdullah and  Mohd Noor Md Sap and  Siriporn Chimphlee and  Surat Srinoy},
	  country	= {},
	  institution	= {},
	  abstract     = {It is important problems to increase the detection rates
and reduce false positive rates in Intrusion Detection System (IDS).
Although preventative techniques such as access control and
authentication attempt to prevent intruders, these can fail, and as a
second line of defence, intrusion detection has been introduced. Rare
events are events that occur very infrequently, detection of rare
events is a common problem in many domains. In this paper we
propose an intrusion detection method that combines Rough set and
Fuzzy Clustering. Rough set has to decrease the amount of data and
get rid of redundancy. Fuzzy c-means clustering allow objects to
belong to several clusters simultaneously, with different degrees of
membership. Our approach allows us to recognize not only known
attacks but also to detect suspicious activity that may be the result of
a new, unknown attack. The experimental results on Knowledge
Discovery and Data Mining-(KDDCup 1999) Dataset show that the
method is efficient and practical for intrusion detection systems.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {1},
	  number    = {8},
	  year      = {2007},
	  pages     = {2568 - 2573},
	  ee        = {https://publications.waset.org/pdf/2474},
	  url   	= {https://publications.waset.org/vol/8},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 8, 2007},
	}