University of Adelaide Library

Adelaide Research and Scholarship : Schools and Disciplines : School of Mathematical Sciences : Mathematical Sciences publications

Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/73539

Type: Journal article
Title: Improving hidden Markov model inferences with private data from multiple observers
Author: Nguyen, H.
Roughan, M.
Citation: IEEE Signal Processing Letters, 2012; 19(10):696-699
Publisher: IEEE-Inst Electrical Electronics Engineers Inc
Issue Date: 2012
ISSN: 1070-9908
1558-2361
Statement of
Responsibility: 
Hung X. Nguyen and Matthew Roughan
Abstract: Most large attacks on the Internet are distributed. As a result, such attacks are only partially observed by any one Internet Service Provider (ISP). Detection would be significantly easier with pooled observations, but privacy concerns often limit the information that providers are willing to share. Multi-party secure distributed computation provides a means for combining observations without compromising privacy. In this letter, we show the benefits of this approach, the most notable of which is that combinations of observations solve identifiability problems in existing approaches for detecting network attacks.
Keywords: Hidden Markov models; identifiability; multiple observers; networks; security.
Rights: © 2012 IEEE
RMID: 0020121842
DOI: 10.1109/LSP.2012.2213811
Appears in Collections:Mathematical Sciences publications
View citing articles in: Web of Science
Google Scholar
Scopus

There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

 

© 2008 The University of Adelaide
library@adelaide.edu.au
CRICOS Provider Number 00123M
Service Charter | Copyright | Privacy | Disclaimer