Detecting Hostile Accesses through Incremental Subspace Clustering

W
WI
2003
2003 IEEE/WIC International Conference on Web Intelligence (WI'03)
Abstract - Detecting Hostile Accesses through Incremental Subspace Clustering

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	Similar Articles Articles by Masaki Narahashi Articles by Einoshin Suzuki

2003 IEEE/WIC International Conference on Web Intelligence (WI'03)

Halifax, Canada

October 13-October 17

ISBN: 0-7695-1932-6

	ASCII Text	x
	Masaki Narahashi, Einoshin Suzuki, "Detecting Hostile Accesses through Incremental Subspace Clustering," Web Intelligence, IEEE / WIC / ACM International Conference on, pp. 337, 2003 IEEE/WIC International Conference on Web Intelligence (WI'03), 2003.

	BibTex	x
	@article{ 10.1109/WI.2003.1241213, author = {Masaki Narahashi and Einoshin Suzuki}, title = {Detecting Hostile Accesses through Incremental Subspace Clustering}, journal ={Web Intelligence, IEEE / WIC / ACM International Conference on}, volume = {0}, year = {2003}, isbn = {0-7695-1932-6}, pages = {337}, doi = {http://doi.ieeecomputersociety.org/10.1109/WI.2003.1241213}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Web Intelligence, IEEE / WIC / ACM International Conference on TI - Detecting Hostile Accesses through Incremental Subspace Clustering SN - 0-7695-1932-6 SP EP A1 - Masaki Narahashi, A1 - Einoshin Suzuki, PY - 2003 KW - null VL - 0 JA - Web Intelligence, IEEE / WIC / ACM International Conference on ER -

Masaki Narahashi, Yokohama National University

Einoshin Suzuki, Yokohama National University

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/WI.2003.1241213

In this paper, we propose an incremental subspace clustering method for flexibly detecting hostile accesses to a Web site. Typical log data for Web accesses are huge, contain irrelevant information, and exhibit dynamic characteristics. We overcome these difficulties through data squashing, subspace clustering, and an incremental algorithm. We have improved, by modifying its data squashing functionality, our subspace clustering method SUBCCOM so that it can exploit previous results. Experimental evaluation confirms superiority of our I-SUBCCOM in terms of precision, recall, and computation time.

Citation:

Masaki Narahashi, Einoshin Suzuki, "Detecting Hostile Accesses through Incremental Subspace Clustering," wi, pp.337, 2003 IEEE/WIC International Conference on Web Intelligence (WI'03), 2003

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