Approved for Public Release: 10-9999. Distribution Unlimited© 2010 The MITRE Corporation. All rights reserved
Aggregating T&E Data for CCoD Trust in Operational Implementation
Suzanne M. Beers, Ph.D.The MITRE Corporation
Operations Research & Systems Analysis Department (E525)Colorado Springs, Colorado
Presented to: 27th International Symposium on Military Operational Research1 September 2010
© 2010 The MITRE Corporation. All rights reserved
Background
Composable Capability on Demand (CCoD)– MITRE research focus area– Provides means to develop agile information architectures– Rapidly meet warfighter mission needs
Implementation challenge: Trust in composed capability
Fuzzy Logic– Multi-valued logic using fuzzy sets– Successfully used in control and analysis applications– Allows humanistic reasoning, gradual transition between states
Decision support system: Aggregate component T&E data
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Overview
What’s Composable Capability on Demand (CCoD)?– Implementation problem statement– CCoD trust taxonomy
What’s fuzzy logic? Fuzzy logic-based CCoD Decision Support System (DSS)
– Structure– Example DSS and results
Conclusion
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What’s CCoD?
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Broker
Discover &
Subscribe
Loosely
Coupled
Services
Producers Consumers
Describe &
Publish
Composable Architectures for Net-centric OperationsRapidly Changeable to Meet Mission Information Needs
Composable Architectures for Net-centric OperationsRapidly Changeable to Meet Mission Information Needs
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One instantiation of CCOD: Mashups
Mashup is a web application that brings together several sources of data to form a unique new combination of information
EMML, Enterprise Mashup Markup Language– Language for creating
enterprise mashups– Software applications that
consume and mash data from variety of sources
– Output presented in graphical user interface, widgets, gadgets
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…for example
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Mashup: Chicago crimeCCoD: Satellite tracking
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Composed Capability Trust Problem
Take the components “off the shelf”
Compose a CCoD C2 capability
Tell the user WHATto give him confidence
to use the system
To know he’ll make theright moves based on
the information
Do I believe this info?Should I act on it?
What should I DO???
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CCoD Implementation Challenge
Lives could depend upon the answer… How to build trust without slowing down the process?
Use existing component T&E data– Software quality metrics
Aggregate using fuzzy logic– Provide trust measure at composed capability level
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Software Trust Metrics
Component Quality Models (CQM)– Evaluate the quality of reusable software modules– Typically start with ISO/IEC 9126 standards– Define characteristics, sub-characteristics, attributes/metrics
CCoD Trust Taxonomy developed by MITRE– Trust: to have confidence in; depend on
Formal acquisition: requirements development then T&E process CCoD: collect metrics during private to public state transition
– Categories CCoD environment Component Composition Component Developer & Composer: proficiency
– Conducted decision-maker survey – ID’ed important factors
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CCoD Trust Taxonomy – Environment
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CCoD Trust Taxonomy – Component
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CCoD Trust Taxonomy – Composition
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T&E Data – to – Composed Capability Trust
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CCoD Trust Taxonomy – Top Level Component Quality Characteristics
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Provide required services under specified conditions
Protect info and data; unauthorized cannot read/modify; authorized not denied access
Maintain a specified level of performance
Usable by someone other than developer
Provide appropriate performance relative to amount of resources used
Can be modified
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CCoD Trust Taxonomy - Functionality
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CCoD Trust Taxonomy – Security
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CCoD Trust Taxonomy - Reliability
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CCoD Trust Taxonomy - Usability
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CCoD Trust Taxonomy - Efficiency
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CCoD Trust Taxonomy - Maintainability
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Down-sized CCoD Decision Hierarchy
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Provide correct results w/ needed precision
Provide appropriate set of functions
Interact w/ other components
Prevent information disclosure
Only modifiable by authorized users
Re-establish performance & recover data
Understand if suitable / how used
Provide required services
Protect info & data
Maintain performance
Non-creator usable
© 2010 The MITRE Corporation. All rights reserved
Why fuzzy logic?
Multi-valued logic based upon fuzzy set theory– Each element in a fuzzy set has membership value
A(u) [0,1] – Linguistic variables related in if – then rule bases
Ideal for representation and analysis of imprecise dependencies – Lowers the cost of products by simplifying programming
Define fuzzy sets, define rule bases relating the sets– Captures human-reasoning
Wide variety of successful control and analysis applications– Control: vacuum cleaners, video cameras, subway systems– Analysis: optimize manufacturing lines,
predict insurgent network behavior, aging
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Fuzzy Inference Structure
Crisp Output
Crisp Input
Fuzzy Input
Fuzzy OutputInference Engine
Defuzzification
Fuzzification
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Fuzzification
Turning a Crisp (Numerical) Value Into a
Degree of Activation of a Fuzzy Set
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Fuzzy Inference -- Rule Base
IF Height is MED AND Weight is LIGHT THEN Build is SMALL
IF Height is SHORT AND Weight is MED THEN Build is MEDSMALL
MEDmax
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Center of Area Defuzzification
yCOA = i=1 yi C (yi)
i=1 C (yi)q
q
1 5 6 7 8 9 10 112 3 4
1/3
2/3
yCOA = 1*0 + 2* 1/3 + 3* 2/3 + 4* 2/3 + 5* 2/3 + 6* 2/3 + 7* 2/3 + 8* 1/3 + 9* 1/3 + 10* 1/3 +11*0
0+ 1/3 + 2/3 + 2/3 + 2/3 + 2/3 + 2/3 + 1/3 + 1/3 + 1/3 +0
yCOA = 5.642 26
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Strategy-to-Task Operational Test AnalysisReduction
in Probability of Kill
Reduction in Guidance
Reduction in Hits
Increasein Break Lk
Track onJam
ResponseTime
Increase inTrack Error
%RIH => Pk
%RIG => Pk
%IBL => Pk
%TOJ => Pk
%ITE => Pk
Response Time =>Pk
^
RIH Fuzzy Set
RIG Fuzzy Set
IBL Fuzzy Set
TOJ Fuzzy Set
TE Fuzzy Set
Response Time Fuzzy Set
Pk Fuzzy Set
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Example Fuzzy-Logic CCoD Decision Hierarchy
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Provide correct results w/ needed precision
Provide appropriate set of functions
Interact w/ other components
Prevent information disclosure
Only modifiable by authorized users
Re-establish performance & recover data
Understand if suitable / how used
Provide required services
Protect info & data
Maintain performance
Non-creator usable
© 2010 The MITRE Corporation. All rights reserved
Fuzzy Logic DSS – Test Data to Component Trust: Sample Fuzzy Sets
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Fuzzy Logic DSS – Test Data to Component Trust: Sample Rule Base (Functionality)
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Fuzzy Logic DSS – Test Data to Component Trust: Sample Results
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Fuzzy Logic DSS – Test Data to Component Trust: Sample Results
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Reliability UsabilityAccuracy Suitability IntInterop Confidentiality Integrity Recoverability Understandability
All Zero 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.17All Bad 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20Two Bad 0.20 0.20 0.20 0.20 0.20 0.90 0.90 0.53Two Bad 0.90 0.90 0.90 0.20 0.20 0.20 0.90 0.53Two Bad 0.90 0.90 0.90 0.90 0.90 0.20 0.20 0.53All Medium 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50Mixture 0.67 0.75 0.25 0.35 0.85 0.67 0.90 0.75One Bad 0.90 0.90 0.90 0.90 0.90 0.90 0.20 0.70One Bad 0.20 0.20 0.20 0.90 0.90 0.90 0.90 0.70All Good 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.83All Perfect 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.83
Functionality SecurityComponentTrust
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Fuzzy Logic DSS – Component Trust to Composition Trust: Sample Fuzzy Sets
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Fuzzy Logic DSS – Component Trust to Composition Trust: Sample Rule Base
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Fuzzy Logic DSS – Component Trust to Composition Trust: Sample Results
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Description Component1 Component2 Component3 CompositionTrustAll Zero 0.00 0.00 0.00 0.17All Bad 0.25 0.25 0.25 0.22Two Bad 0.25 0.30 0.95 0.47All Medium 0.50 0.50 0.50 0.50Mixture 0.67 0.25 0.85 0.59One Bad 0.25 0.95 0.95 0.67All Good 0.90 0.90 0.90 0.83All Perfect 1.00 1.00 1.00 0.83
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Conclusion
CCoD provides a means to rapidly develop agile information architectures
Warfighter needs trust in composed capability
Fuzzy logic provides easily-built decision support
Future research/work focus areas– Incorporate real-time decision-maker risk preferences– Refine metrics, definitize data sources, construct automated data
collection/cataloging – Develop Web 2.0-like tool to operationalize
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