Mutual Information on Tensors for Measuring the Nonlinear Correlations in Network Security

Lu, L; Ren, X; Cui, C; Luo, Y; Jia, Y; Xu, Y

Mutual Information on Tensors for Measuring the Nonlinear Correlations in Network Security

Lu, L Ren, X Cui, C Luo, Y Jia, Y Xu, Y

Permalink

Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE), 2019
Issue Date:: 2019-08

Closed Access

	Filename	Description	Size
	08887304.pdf	Published version	390.87 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Lu, L
dc.contributor.author	Ren, X
dc.contributor.author	Cui, C
dc.contributor.author	Luo, Y
dc.contributor.author	Jia, Y
dc.contributor.author	Xu, Y
dc.date	2019-08-05
dc.date.accessioned	2020-06-18T22:23:16Z
dc.date.available	2020-06-18T22:23:16Z
dc.date.issued	2019-08
dc.identifier.citation	2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE), 2019
dc.identifier.isbn	978-1-7281-2777-4
dc.identifier.issn	2324-898X
dc.identifier.issn	2324-9013
dc.identifier.uri	http://hdl.handle.net/10453/141544
dc.description.abstract	Correlation analysis has been proposed to measure the relationship among different variables, with application in multi-view dimension reduction. However, the existing methods usually are used by covariance in a linear way rather than the nonlinear effects being considered among multiple variables and only few works on nonlinear interaction of two variables have been considered. In this paper we propose a nonlinear analysis of multiple (more than two) variables based on mutual information for tensors analysis (MITA) firstly. In addition, we extend the mutual information matrix analysis directly to mutual information tensor analysis and show the mutual information formula for multiple variables theoretically. Experiment on multi-view dimension reduction, including attacking internet traffic detection, has been done to illustrate the effectiveness of the proposed method, especially in the case of low dimensional subspace.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE)
dc.relation.ispartof	IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ IEEE International Conference On Big Data Science And Engineering
dc.relation.isbasedon	10.1109/trustcom/bigdatase.2019.00107
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	Mutual Information on Tensors for Measuring the Nonlinear Correlations in Network Security
dc.type	Conference Proceeding
utslib.location.activity	Rotorua, New Zealand
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-06-18T22:23:11Z
pubs.finish-date	2019-08-08
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2019-08-05
dc.location	Piscataway, USA

Abstract:

Correlation analysis has been proposed to measure the relationship among different variables, with application in multi-view dimension reduction. However, the existing methods usually are used by covariance in a linear way rather than the nonlinear effects being considered among multiple variables and only few works on nonlinear interaction of two variables have been considered. In this paper we propose a nonlinear analysis of multiple (more than two) variables based on mutual information for tensors analysis (MITA) firstly. In addition, we extend the mutual information matrix analysis directly to mutual information tensor analysis and show the mutual information formula for multiple variables theoretically. Experiment on multi-view dimension reduction, including attacking internet traffic detection, has been done to illustrate the effectiveness of the proposed method, especially in the case of low dimensional subspace.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/141544