Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/73539
Citations
Scopus Web of Science®
?
?
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

Files in This Item:
There are no files associated with this item.


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