Computational Red Teaming : (Record no. 1048837)

001 - CONTROL NUMBER
control field EBC1965237
006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS
additional material characteristics m o d |
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
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008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 191220s2014 xx o ||||0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783319082813
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Cancelled/invalid ISBN 9783319082806
035 ## - SYSTEM CONTROL NUMBER
System control number (MiAaPQ)EBC1965237
035 ## - SYSTEM CONTROL NUMBER
System control number (Au-PeEL)EBL1965237
035 ## - SYSTEM CONTROL NUMBER
System control number (CaPaEBR)ebr10966020
035 ## - SYSTEM CONTROL NUMBER
System control number (CaONFJC)MIL766593
035 ## - SYSTEM CONTROL NUMBER
System control number (OCoLC)894509287
040 ## - CATALOGING SOURCE
Original cataloging agency MiAaPQ
Language of cataloging eng
Description conventions rda
-- pn
Transcribing agency MiAaPQ
Modifying agency MiAaPQ
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number TA1-2040
082 0# - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.3
090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (OCLC)
Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) TA1-2040
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Abbass, Hussein A.
245 10 - TITLE STATEMENT
Title Computational Red Teaming :
Remainder of title Risk Analytics of Big-Data-to-Decisions Intelligent Systems.
264 #1 -
-- Cham :
-- Springer,
-- 2014.
264 #4 -
-- ©2015.
300 ## - PHYSICAL DESCRIPTION
Extent 1 online resource (239 pages)
336 ## - Content
Term text
Code txt
Content rdacontent
337 ## - Media
Term computer
Code c
Media rdamedia
338 ## - Carrier
Term online resource
Code cr
Carrier rdacarrier
490 0# - SERIES STATEMENT
Series statement eBooks on Demand
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Intro -- Preface -- Acknowledgments -- Contents -- Acronyms -- List of Figures -- List of Tables -- 1 The Art of Red Teaming -- 1.1 A Little Story -- 1.2 Red Teaming -- 1.2.1 Modelling -- 1.2.2 Executing Exercises -- 1.2.3 Deliberately Challenging -- 1.2.4 Risk Lens -- 1.2.5 Understanding the Space of Possibilities -- 1.2.6 Exploring Non-conventional Behaviors -- 1.2.7 Testing Strategies -- 1.2.8 Mitigating Risk -- 1.3 Success Factors of Red Teams -- 1.3.1 Understanding and Analyzing the Concept of a Conflict -- 1.3.2 Team Membership -- 1.3.3 Time for Learning, Embodiment and Situatedness -- 1.3.4 Seriousness and Commitment -- 1.3.5 Role Continuity -- 1.3.6 Reciprocal Interaction -- 1.4 Functions of Red Teaming -- 1.4.1 Discovering Vulnerabilities -- 1.4.2 Discovering Opportunities -- 1.4.3 Training -- 1.4.4 Thinking Tools -- 1.4.5 Bias Discovery -- 1.4.6 Creating Future Memories and Contingency Plans -- 1.4.7 Memory Washing -- 1.5 Steps for Setting Up RT Exercises -- 1.5.1 Setting the Purpose, Scope and Criteria of Success -- 1.5.2 Designing the Exercise -- 1.5.3 Conducting the Exercise -- 1.5.4 Monitoring and Real-Time Analysis of the Exercise -- 1.5.5 Post Analysis of the Exercise -- 1.5.6 Documenting the Exercise -- 1.5.7 Documenting Lessons Learnt on Red Teaming -- 1.6 Ethics and Legal Dimensions of RT -- 1.6.1 The RT Business Case -- 1.6.2 Responsible Accountability -- 1.6.2.1 Red-Teaming Stakeholder (Risk Level-Low) -- 1.6.2.2 Red-Teaming Communicator (Risk Level-Low) -- 1.6.2.3 Red-Teaming Legal Councilor (Risk Level-Low) -- 1.6.2.4 Red-Teaming Designer (Risk Level-Very High) -- 1.6.2.5 Red-Teaming Thinker (Risk Level-Very High) -- 1.6.2.6 Red-Teaming Technician (Risk Level-Medium) -- 1.6.2.7 Red-Teaming Documenter (Risk Level-Low) -- 1.6.2.8 Red-Teaming Auditor (Risk Level-Medium) -- 1.6.2.9 Red-Teaming Observer (Risk Level-Medium).
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note 1.6.3 The Ethics of Budget Estimation -- 1.7 From Red Teaming to Computational Red Teaming -- 1.7.1 Military Decision Sciences and Red Teaming -- 1.7.2 Smoothing the Way Toward ComputationalRed Teaming -- 1.7.3 Automating the Red-Teaming Exercise -- 1.7.4 Blue-Red Simulation -- 1.8 Philosophical Reflection on Assessing Intelligence -- 1.8.1 The Imitation Game (Turing Test) for AssessingIntelligence -- 1.8.2 Computational Red Teaming for Assessing Intelligence -- References -- 2 Analytics of Risk and Challenge -- 2.1 Precautions -- 2.2 Risk Analytics -- 2.2.1 Intentional Actions -- 2.2.2 Objectives and Goals -- 2.2.3 Systems -- 2.2.4 Uncertainty and Risk -- 2.2.5 Deliberate Actions -- 2.3 Performance -- 2.3.1 Behavior -- 2.3.2 Skills -- 2.3.3 Competency -- 2.3.3.1 Need for a Standard -- 2.3.3.2 Comfort vs Efficiency -- 2.3.3.3 Revisiting Behavior -- 2.3.4 From Gilbert's Model of Performance to a General Theory of Performance -- 2.4 Challenge Analytics -- 2.4.1 A Challenge is Not a Challenge -- 2.4.2 Motivation and Stimulation -- 2.4.3 Towards Simple Understanding of a Challenge -- 2.4.4 Challenging Technologies, Concepts and Plans -- 2.5 From the Analytics of Risk and Challenge to Computational Red Teaming -- 2.5.1 From Sensors to Effectors -- 2.5.2 The Cornerstones of Computational-Red-Teaming -- 2.5.3 Risk Analytics -- 2.5.4 Challenge Analytics Using the Observe-Project-Counteract Architecture -- References -- 3 Big-Data-to-Decisions Red Teaming Systems -- 3.1 Basic Ingredients of Computations in Red Teaming -- 3.1.1 From Classical Problem Solving to Computational-Red-Teaming -- 3.1.2 Run Through a CRT Example -- 3.2 Experimentation -- 3.2.1 Purpose, Questions, and Hypotheses -- 3.2.2 Experiments -- 3.2.2.1 Unwanted and Wanted Factors -- 3.2.2.2 Cause-Effect Relationship -- 3.3 Search and Optimization -- 3.3.1 Blind vs Knowledge-Based Optimization.
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note 3.3.2 System vs Negotiation-Based Optimization -- 3.4 Simulation -- 3.4.1 Resolution, Abstraction and Fidelity -- 3.5 Data Analysis and Mining -- 3.5.1 C4.5 -- 3.6 Big Data -- 3.6.1 The 6 V's Big Data Characteristics -- 3.6.2 Architectures for Big Data Storage -- 3.6.3 Real-Time Operations: What It Is All About -- 3.6.4 GDL Data Fusion Architecture -- 3.7 Big-Data-to-Decisions Computational-Red-Teaming-Systems -- 3.7.1 Preliminary Forms of Computational-Red-Teaming-Systems -- 3.7.2 Progressive Development of Sophisticated Computational-Red-Teaming-Systems -- 3.7.3 Advanced Forms of Computational-Red-Teaming-Systems -- 3.7.4 The Shadow CRT Machine -- References -- 4 Thinking Tools for Computational Red Teaming -- 4.1 Scenarios -- 4.1.1 Possibility vs Plausibility -- 4.1.2 Classical Scenario Design -- 4.1.3 Scenario Design in CRT -- 4.2 A Model to Deconstruct Complex Systems -- 4.2.1 Connecting the Organization -- 4.2.2 Resources -- 4.2.3 Fundamental Inputs to Capabilities -- 4.2.4 Capabilities -- 4.2.5 Vision, Mission and Values -- 4.2.6 Strategy -- 4.3 Network-Based Strategies for Social and Cyber-SecurityOperations -- 4.3.1 Socio-Cognitive-Cyber-Physical Effect Space -- 4.3.2 Cyber Security -- 4.3.3 Operations on Networks -- 4.3.3.1 Detect: Detection of Hidden Networks -- 4.3.3.2 Identify: Identification of Detected Networks -- 4.3.3.3 Track: Tracking Networks as They Maneuver in the Environment -- 4.3.3.4 Deny: Denying Access and Capabilities to a Network -- 4.3.3.5 Prevent: Preventing a Network from Achieving an Effect -- 4.3.3.6 Isolate: Isolating a Network from Other Networks -- 4.3.3.7 Neutralize: Neutralizing a Network's Effect -- 4.3.3.8 Destroy: Destroying an Existing Network -- 4.3.3.9 Reshape: Reshaping the Network's Environment -- 4.3.3.10 Hide: Hiding a Network Within Other Networks -- References.
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note 5 Case Studies on Computational Red Teaming -- 5.1 Breaking Up Air Traffic Conflict Detection Algorithms -- 5.1.1 Motivation and Problem Definition -- 5.1.2 The Purpose -- 5.1.3 The Simulator -- 5.1.4 The Challenger -- 5.1.5 Context Optimizer -- 5.1.6 Context Miner -- 5.1.7 The Response -- 5.2 Human Behaviors and Strategies in Blue-Red Simulations -- 5.2.1 Motivation and Problem Definition -- 5.2.2 The Purpose -- 5.2.3 The Simulator -- 5.2.4 The Challenger -- 5.2.5 Behavioral Miner -- 5.2.6 The Response -- 5.3 Cognitive-Cyber Symbiosis (CoCyS): Dancingwith Air Traffic Complexity -- 5.3.1 Motivation and Problem Definition -- 5.3.2 The Purpose -- 5.3.3 Experimental Logic -- 5.3.4 The Simulator -- 5.3.5 The Miner -- 5.3.6 The Optimizer -- 5.3.7 The Challenger -- 5.3.8 Experimental Protocol -- 5.3.9 The Response -- References -- 6 The Way Forward -- 6.1 Where Can We Go from Here? -- 6.1.1 Future Work on Cognitive-Cyber-Symbiosis -- 6.1.2 Future Work on the Shadow CRT Machine -- 6.1.3 Computational Intelligence Techniques for Computational Red Teaming -- 6.1.4 Applications of Computational Red Teaming -- References -- Index.
520 ## - SUMMARY, ETC.
Summary, etc Written to bridge the information needs of management and computational scientists, this book presents the first comprehensive treatment of Computational Red Teaming (CRT).  The author describes an analytics environment that blends human reasoning and computational modeling to design risk-aware and evidence-based smart decision making systems. He presents the Shadow CRT Machine, which shadows the operations of an actual system to think with decision makers, challenge threats, and design remedies. This is the first book to generalize red teaming (RT) outside the military and security domains and it offers coverage of RT principles, practical and ethical guidelines. The author utilizes Gilbert's principles for introducing a science. Simplicity: where the book follows a special style to make it accessible to a wide range of  readers. Coherence:  where only necessary elements from experimentation, optimization, simulation, data mining, big data, cognitive information processing, and system thinking are blended together systematically to present CRT as the science of Risk Analytics and Challenge Analytics. Utility: where the author draws on a wide range of examples, ranging from job interviews to Cyber operations, before presenting three case studies from air traffic control technologies, human behavior, and complex socio-technical systems involving real-time mining and integration of human brain data in the decision making environment..
588 ## -
-- Description based on publisher supplied metadata and other sources.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computational intelligence.;Big data.;Decision making -- Data processing.
655 #4 - INDEX TERM--GENRE/FORM
Genre/form data or focus term Electronic books.
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Print version:
Main entry heading Abbass, Hussein A.
Title Computational Red Teaming : Risk Analytics of Big-Data-to-Decisions Intelligent Systems
Place, publisher, and date of publication Cham : Springer,c2014
International Standard Book Number 9783319082806
797 2# - LOCAL ADDED ENTRY--CORPORATE NAME (RLIN)
Corporate name or jurisdiction name as entry element ProQuest (Firm)
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://ebookcentral.proquest.com/lib/uttyler/detail.action?docID=1965237">https://ebookcentral.proquest.com/lib/uttyler/detail.action?docID=1965237</a>
Link text Click here to view this ebook.
901 ## - LOCAL DATA ELEMENT A, LDA (RLIN)
Platform EBC
901 ## - LOCAL DATA ELEMENT A, LDA (RLIN)
Platform EBL
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Electronic Book
Source of classification or shelving scheme
Holdings
Withdrawn status Lost item Source of classification or shelving scheme Damaged status Not for loan Permanent Location Current Location Shelving location Date acquired Full call number Barcode Date last seen Uniform Resource Identifier Price effective from Koha item type
          UT Tyler Online UT Tyler Online Online 2020-01-08 TA1-2040 EBC1965237 2020-01-08 https://ebookcentral.proquest.com/lib/uttyler/detail.action?docID=1965237 2020-01-08 Electronic Book