KEYWORDS: Information security, Control systems, Mathematical modeling, Optimization (mathematics), Network security, Composites, Computer programming, Computer security, Systems modeling, Binary data
A new mathematical model for the prediction of the security figure of merit of an assured
information system is proposed. The security effectiveness figure of merit is defined as a
multi-variate composite function of the strength of security mechanism, usability,
performance, and cost. The problem of determining the optimal set of security controls for a
given system is then formulated as mathematical optimization problem and the potential
methods of approach are addressed. The concept is illustrated with a simple example and the
conclusions bring out the benefits of the model.
KEYWORDS: Performance modeling, Systems modeling, Computer security, Solid state lighting, Information assurance, Network security, Information security, Symmetric-key encryption, Optical filters, Computing systems
An analytical performance model for a generic secure messaging system is formulated as a multi-class
queuing network. The model includes assessment of the impact of security features such as secret key
encryption/ decryption, signature generation/verification, and certificate validation, on overall performance.
Findings of sensitivity analysis with respect to message rate, WAN transmission link, SSL encryption,
message size, and distance between servers is also presented. Finally, the description of how the model can
be adopted for making performance based architectural design options is outlined.
The study presents a formal methodology to the problem of feature level fusion, that had been previously addressed in the literature mostly in an ad hoc manner on a case by case basis only. The input set of features extracted from multiple sensors (data sources) are optimally fused to derive a synthetic feature so as to enhance the effective discrimination potential among the defined set of decision classes. This `features in - feature out (FEI-FEO)' fusion process, unlike most other fusion schemes reported in the literature, is designed through a formal learning phase in which an optimal mapping from the multi-sensor derived feature space to a single unified feature is developed. This learning, accomplished through a new composite random and deterministic search based optimization tool, defines the transformation for the FEI-FEO process. This transformation is applied to the multi-sensor generated feature sets in the operational phase to derive the fused feature values corresponding to the objects under observation. The corresponding classification decisions are made on the basis of relative closeness of these feature values to the different class mean values in the transformed single dimensional feature space. The new methodology has been implemented in MATLAB which, being a vector/matrix oriented language, is an ideal candidate for solving problems in pattern recognition and learning. The method is applied to well-known data sets available on the web for testing pattern recognition algorithms to assess its effectiveness relative to the traditional classification methods from both conceptual as well as computational view points.
Conference Committee Involvement (10)
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2016
21 April 2016 | Baltimore, MD, United States
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2015
21 April 2015 | Baltimore, MD, United States
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2014
6 May 2014 | Baltimore, MD, United States
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2013
30 April 2013 | Baltimore, Maryland, United States
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2012
25 April 2012 | Baltimore, Maryland, United States
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2011
27 April 2011 | Orlando, Florida, United States
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2010
7 April 2010 | Orlando, Florida, United States
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2009
16 April 2009 | Orlando, Florida, United States
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2008
19 March 2008 | Orlando, Florida, United States
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2007
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