Output details
11 - Computer Science and Informatics
Imperial College London
Output title
Learning Stochastic Models of Information Flow
Type
E - Conference contribution
Name of conference/published proceedings
28th IEEE International Conference on Data Engineering (ICDE)
Volume number
-
Issue number
-
First page of article
570
ISSN of proceedings
1063-6382
Year of publication
2012
URL
-
Number of additional authors
4
Additional information
<12>This paper, presents for the first time, scalable and accurate computation of predictions of joint and conditional flows that is used to learn models of information flow in networks and predicting unseen flows of information. The work is the output of a collaborative project with IBM Watson within US Army/MoD funded ITA project, has formed the basis of an EPSRC Doctoral Prize Fellowship for Dr. Luke Dickens, a subsequent EPSRC project in the area of machine learning for cyber-security (Privacy Dynamics EP/K033425/1 £323K), and further collaborations with IBM. Acceptance 24.21% /413.
Interdisciplinary
-
Cross-referral requested
-
Research group
B - Distributed Software Engineering
Citation count
0
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
