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E-grāmata: Application of Big Data for National Security: A Practitioner's Guide to Emerging Technologies

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, (Professor of Informatics, Sheffield Hallam University, Sheffield, UK), (Associate Professor of Research, University of Virginia, USA), (Professor of Computer Science, University of Georgia, Athens, GA, USA), , (Department of Mathematics,)
  • Formāts: PDF+DRM
  • Izdošanas datums: 14-Feb-2015
  • Izdevniecība: Butterworth-Heinemann Inc
  • Valoda: eng
  • ISBN-13: 9780128019733
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Application of Big Data for National Security: A Practitioner's Guide to Emerging TechnologiesPalielināt
  • Formāts: PDF+DRM
  • Izdošanas datums: 14-Feb-2015
  • Izdevniecība: Butterworth-Heinemann Inc
  • Valoda: eng
  • ISBN-13: 9780128019733
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Application of Big Data for National Security introduces the reader to state-of-the-art concepts and technologies surrounding big data and presents a strategies framework for using Big Data to combat terrorism and improve safety. An international team of computer scientists and national security experts provide extensive knowledge on this increasingly global issue using a series of case studies and predictions for the future. The strategic frameworks and critical factors presented for success consider legal, ethical, and societal impacts, illustrating how data and security concerns intersect. Pointing to future technical and operational challenges, as well as possibilities for improving detection and prevention of crime and terrorism,Application of Big Data for National Security provides readers with the context they need to utilize big data in the most effective way.

  • Contextualizes the technology of Big Data in how it relates to national security
  • Presents a strategic approach for adopting Big Data technologies for preventing terrorism and reducing crime
  • Provides a series of case studies to demonstrate key concepts
  • Indicates future directions for Big Data as an enabler of more advanced detection and prevention techniques

Recenzijas

"Law enforcement and intelligence practitioners will especially be interested in the case studies on how big data and high performance analytics have been used to uncover terrorist plots" --Journal of Counterterrorism and Homeland Security International

Papildus informācija

This text introduces state-of-the-art concepts and technologies surrounding big data, providing users with a strategic framework that can be used to combat terrorism and reduce crime.
List of Contributors xv
About the Editors xvii
Foreword xix
Lord Carlile of Berriew
Preface xxi
Edwin Meese III
Acknowledgments xxiii
Section 1 Introduction To Big Data
Chapter 1 An Introduction to Big Data
3(11)
What Is Big Data?
3(1)
How Different Is Big Data?
4(1)
More on Big Data: Types and Sources
4(1)
Structured Data
5(1)
Unstructured Data
5(1)
Semi-Structured Data
5(1)
The Five V's of Big Data
5(2)
Volume
6(1)
Velocity
6(1)
Variety
6(1)
Veracity
7(1)
Value
7(1)
Big Data in the Big World
7(2)
Importance
7(1)
Advantages and Applications
7(2)
Analytical Capabilities of Big Data
9(1)
Data Visualization
9(1)
Greater Risk Intelligence
9(1)
Satisfying Business Needs
9(1)
Predictive Modeling and Optimization
10(1)
Streaming Analytics
10(1)
Identifying Business Use Cases
10(1)
Video and Voice Analytics
10(1)
Geospatial Analytics
10(1)
An Overview of Big Data Solutions
11(1)
Google BigQuery
11(1)
IBM InfoSphere BigInsights
11(1)
Big Data on Amazon Web Services
11(1)
Clouds for Big Data
11(1)
Conclusions
12(1)
References
12(2)
Chapter 2 Drilling into the Big Data Gold Mine: Data Fusion and High-Performance Analytics for Intelligence Professionals
14(9)
Introduction
14(1)
The Age of Big Data and High-Performance Analytics
14(1)
Technology Challenges
15(4)
Building the Complete Intelligence Picture
16(3)
Examples
19(1)
Scenario 1: Fusion and Michigan State Police
19(1)
Scenario 2: National Security and Intelligence Solution in the Middle East
19(1)
Conclusion
20(1)
Reference
20(3)
Section 2 Core Concepts And Application Scenarios
Chapter 3 Harnessing the Power of Big Data to Counter International Terrorism
23(16)
Introduction
23(1)
A New Terror
24(9)
Fertilizer Plot
24(1)
International Dimension
25(1)
Executive Action
26(1)
Vulnerabilities Emerge
27(1)
Assessing the Threat
27(2)
Suicide Terror
29(1)
Joining the Dots
30(1)
Held to Account
30(2)
Strategic Approach
32(1)
Changing Threat Landscape
33(1)
Embracing Big Data
34(2)
Conclusion
36(1)
References
37(2)
Chapter 4 Big Data and Law Enforcement: Advances, Implications, and Lessons from an Active Shooter Case Study
39(16)
The Intersection of Big Data and Law Enforcement
39(2)
Case Example and Workshop Overview
41(2)
Situational Awareness
43(2)
Looking into the Past
43(1)
Interacting with the Public
44(1)
Alerting and Prediction
44(1)
Twitter as a Social Media Source of Big Data
45(1)
Social Media Data Analyzed for the Workshop
45(1)
Tools and Capabilities Prototypes During the Workshop
46(5)
Word Cloud Visualization
46(1)
Dynamic Classification of Tweet Content
46(1)
Content-Based Image Retrieval
47(1)
Maximizing Geographic Information
48(1)
Detecting Anomalies
49(1)
Influence and Reach of Messaging
49(1)
Technology Integration
50(1)
Law Enforcement Feedback for the Sessions
51(1)
Discussion
51(1)
Acknowledgments
52(1)
References
52(3)
Chapter 5 Interpretation and Insider Threat: Rereading the Anthrax Mailings of 2001 Through a "Big Data" Lens
55(13)
Introduction
55(2)
Importance of the Case
57(1)
The Advancement of Big Data Analytics After 2001
58(1)
Relevant Evidence
59(2)
Potential for Stylometric and Sentiment Analysis
61(2)
Potential for Further Pattern Analysis and Visualization
63(1)
Final Words: Interpretation and Insider Threat
64(1)
References
65(3)
Chapter 6 Critical Infrastructure Protection by Harnessing Big Data
68(13)
Introduction
68(1)
What Is a CI System?
68(1)
Understanding the Strategic Landscape into Which Big Data Must Be Applied
69(4)
What Is Meant by an Overarching Architecture?
73(3)
The SCR
73(3)
Underpinning the SCR
76(1)
Strategic Community Architecture Framework
77(3)
Conclusions
80(1)
References
80(1)
Chapter 7 Military and Big Data Revolution
81(27)
Risk of Collapse
81(1)
Into the Big Data Arena
82(1)
Simple to Complex Use Cases
83(4)
Canonic Use Cases
87(2)
Filtering
88(1)
Correlation of Data Over Space and Time
88(1)
More on the Digital Version of the Real World (See the World as Events)
89(2)
Quality of Data, Metadata, and Content
90(1)
Real-Time Big Data Systems
91(4)
Application Principles and Constraints
91(2)
Logical View
93(2)
Implementing the Real-Time Big Data System
95(7)
Batch Processing (into the "Batch Layer")
95(1)
Processing Layer (into the "Batch Layer")
95(1)
Spark
96(1)
Data Stream Processing (into the "Streaming Layer")
97(1)
Alerts and Notifications (into the "Publishing Layer")
98(1)
Filtering Processing Fitting in Memory (into the "Streaming Layer")
98(1)
Machine Learning and Filtering (into the "Batch and Streaming Layers")
99(1)
Online Clustering (into the "Streaming Layer")
100(1)
Results Publication
100(1)
Build the Layers
101(1)
Insight into Deep Data Analytics Tools and Real-Time Big Data Systems
102(2)
Add Fault Tolerance
103(1)
Security
103(1)
Adding Flexibility and Adaptation
104(1)
Very Short Loop and Battlefield Big Data Datacenters
104(1)
Conclusions
104(2)
Further Reading
106(2)
Chapter 8 Cybercrime: Attack Motivations and Implications for Big Data and National Security
108(23)
Introduction
108(2)
Defining Cybercrime and Cyberterrorism
110(1)
Attack Classification and Parameters
111(2)
Who Perpetrates These Attacks?
113(2)
Script Kiddies
113(1)
Web Defacers
114(1)
Hackers
114(1)
Pirates
114(1)
Phone Phreakers
115(1)
Tools Used to Facilitate Attacks
115(2)
Motivations
117(1)
Attack Motivations Taxonomy
118(4)
Political
118(2)
Ideological
120(1)
Commercial
120(1)
Emotional
120(1)
Informational/Promotional
121(1)
Financial
121(1)
Personal
121(1)
Exploitation
122(1)
Detecting Motivations in Open-Source Information
122(1)
Conclusion
123(1)
References
123(8)
Section 3 Methods And Technological Solutions
Chapter 9 Requirements and Challenges for Big Data Architectures
131(9)
What Are the Challenges Involved in Big Data Processing?
131(1)
Deployment Concept
131(1)
Technological Underpinning
132(2)
The Core Technologies
132(2)
Planning for a Big Data Platform
134(5)
Infrastructure Requirements
134(3)
Capacity Planning Considerations
137(1)
Cloud Computing Considerations
137(2)
Conclusions
139(1)
References
139(1)
Chapter 10 Tools and Technologies for the Implementation of Big Data
140(15)
Introduction
140(1)
Techniques
141(1)
Representation, Storage, and Data Management
141(1)
Analysis
142(2)
AB Testing
142(1)
Association Rule Learning
143(1)
Classification
143(1)
Crowdsourcing
143(1)
Data Mining
143(1)
Natural Language Processing and Text Analysis
143(1)
Sentiment Analysis
144(1)
Signal Processing
144(1)
Visualization
144(1)
Computational Tools
144(1)
Hadoop
145(1)
MapReduce
145(1)
Apache Cassandra
145(1)
Implementation
145(1)
Implementation Issues
146(1)
Project Initiation and Launch
146(4)
Information Technology Project Reference Class
148(1)
Mitigating Factors
149(1)
User Factors and Change Management
149(1)
Data Sources and Analytics
150(1)
Cloud/Crowd sourcing
150(1)
Corporate Systems
150(1)
Analytics Philosophy: Analysis or Synthesis
151(1)
Governance and Compliance
152(1)
Data Protection Requirements and Privacy
152(1)
References
153(2)
Chapter 11 Mining Social Media: Architecture, Tools, and Approaches to Detecting Criminal Activity
155(18)
Introduction
155(2)
Mining of Social Networks for Crime
157(1)
Text Mining
158(1)
Natural Language Methods
158(1)
Symbolic Approach
158(1)
Statistical Approach
159(1)
Connectionist Approach
159(1)
General Architecture and Various Components of Text Mining
159(6)
Lexical Analysis
159(1)
POS Tagging
160(1)
Parsing
160(1)
Named Entity Recognition
161(1)
Co-reference Resolution
161(1)
Relation Extraction
161(1)
Concept Extraction
162(1)
Topic Recognition
163(1)
Sentiment Analysis
163(1)
Semantic Analysis
163(1)
Machine Translation
163(1)
Bayesian Networks
163(2)
Automatic Extraction of BNs from Text
165(1)
Dependence Relation Extraction from Text
165(1)
Variables Identification
166(1)
BN Structure Definition
166(3)
Probability Information Extraction
166(1)
Aggregation of Structural and Probabilistic Data
166(1)
BNs and Crime Detection
167(1)
General Architecture
167(2)
Example of BN Application to Crime Detection: Covert Networks
169(1)
Conclusions
169(1)
References
170(3)
Chapter 12 Making Sense of Unstructured Natural Language Information
173(11)
Introduction
173(1)
Big Data and Unstructured Data
174(1)
Aspects of Uncertainty in Sense Making
175(1)
Situation Awareness and Intelligence
176(1)
Situation Awareness: Short Timelines, Small Footprint
176(1)
Intelligence: Long(er) Timelines, Larger Footprint
176(1)
Processing Natural Language Data
177(1)
Structuring Natural Language Data
178(1)
Two Significant Weaknesses
179(1)
Ignoring Lexical Clues on Credibility and Reliability
179(1)
Out of Context, Out of Mind
180(1)
An Alternative Representation for Flexibility
180(2)
Conclusions
182(1)
References
182(2)
Chapter 13 Literature Mining and Ontology Mapping Applied to Big Data
184(25)
Introduction
184(1)
Background
185(2)
Parameter Optimized Latent Semantic Analysis
186(1)
Improving the Semantic Meaning of the POLSA Framework
186(1)
Web Services
186(1)
ARIANA: Adaptive Robust Integrative Analysis for Finding Novel Associations
187(1)
Conceptual Framework of ARIANA
187(6)
Ontology Mapping
189(1)
Data Stratification and POLSA
189(1)
Relevance Model
190(2)
Reverse Ontology Mapping
192(1)
Visualization and Interface
192(1)
Implementation of ARIANA for Biomedical Applications
193(8)
OM and MGD Creation
194(1)
Creation of the MGD
195(1)
Data Stratification and POLSA
195(3)
Parameter Optimized Latent Semantic Analysis
198(1)
Relevance Model
198(2)
Reverse Ontology Mapping and I&V
200(1)
Case Studies
201(1)
Case Study I: KD: Lethal Drug Interaction
201(1)
Case Study II: Data Repurposing: AD Study
202(1)
Discussion
202(2)
Conclusions
204(1)
Acknowledgment
205(1)
References
205(4)
Chapter 14 Big Data Concerns in Autonomous Al Systems
209(20)
Introduction
209(1)
Artificially Intelligent System Memory Management
210(2)
Sensory Memories
210(1)
Short-term Artificial Memories
211(1)
Long-term Artificial Memories
211(1)
Artificial Memory Processing and Encoding
212(6)
Short-term Artificial Memory Processing
212(4)
Long-term Artificial Memory Processing
216(1)
Implicit Biographical Memory Recall/Reconstruction Using Spectral Decomposition Mapping
217(1)
Constructivist Learning
218(3)
Adaptation of Constructivist Learning Concepts for Big Data in an AIS
220(1)
Practical Solutions for Secure Knowledge Development in Big Data Environments
221(3)
Practical Big Data Security Solutions
221(2)
Optimization of Sociopolitical-Economic Systems and Sentiment Analysis
223(1)
Conclusions
224(1)
References
225(4)
Section 4 Legal And Social Challenges
Chapter 15 The Legal Challenges of Big Data Application in Law Enforcement
229(9)
Introduction
229(1)
Attractions of Big Data
229(1)
Dilemmas of Big Data
230(1)
Legal Framework
230(6)
Human Rights
231(2)
Purpose Limitation and Further Processing
233(1)
Public Trust and Confidence
234(2)
Conclusions
236(1)
How Far Should Big Data Principles Such as "Do Not Track" and "Do Not Collect" Be Applicable to LEAs, Either in Qualified Format or at All?
236(1)
References
237(1)
Chapter 16 Big Data and the Italian Legal Framework: Opportunities for Police Forces
238(12)
Introduction
238(1)
European Legal Framework
239(3)
Directive 95/46/EC and Revision Process Started in 2012
239(2)
Data Retention Directive
241(1)
The Italian Legal Framework
242(3)
Authority for Personal Data Protection
242(1)
The Italian Privacy Code
242(1)
Focus on Italian Police Forces
243(1)
Police Data Processing and Privacy
244(1)
Opportunities and Constraints for Police Forces and Intelligence
245(3)
References
248(2)
Chapter 17 Accounting for Cultural Influences in Big Data Analytics
250(11)
Introduction
250(1)
Considerations from Cross-Cultural Psychology for Big Data Analytics
251(1)
Cultural Dependence in the Supply and Demand Sides of Big Data Analytics
252(4)
Cultural Dependence on the Supply Side (Data Creation)
253(1)
Cultural Dependence on the Demand Side (Data Interpretation)
254(2)
(Mis)Matches among Producer, Production, Interpreter, and Interpretation Contexts
256(1)
Integrating Cultural Intelligence into Big Data Analytics: Some Recommendations
257(1)
Conclusions
258(1)
References
259(2)
Chapter 18 Making Sense of the Noise: An ABC Approach to Big Data and Security
261(14)
How Humans Naturally Deal with Big Data
261(1)
The Three Stages of Data Processing Explained
262(3)
Stage 1: Reflexive
263(1)
Stage 2: Pre-attentive
263(1)
Stage 3: Attentive
264(1)
The Public Order Policing Model and the Common Operational Picture
265(2)
Applications to Big Data and Security
267(3)
Level 1: Reflexive Response
268(1)
Level 2: Pre-attentive Response
268(1)
Level 3: Attentive Response and the Focused, Intellectual Management of Data
269(1)
Application to Big Data and National Security
270(2)
A Final Caveat from the FBI Bulletin
272(1)
References
272(3)
Glossary 275(4)
Index 279
Babak Akhgar is Professor of Informatics and Director of CENTRIC (Center of Excellence in Terrorism, Resilience, Intelligence and Organized Crime Research) at Sheffield Hallam University (UK) and Fellow of the British Computer Society. He has more than 100 refereed publications in international journals and conferences on information systems with specific focus on knowledge management (KM). He is member of editorial boards of several international journals and has acted as Chair and Program Committee Member for numerous international conferences. He has extensive and hands-on experience in the development, management and execution of KM projects and large international security initiatives (e.g., the application of social media in crisis management, intelligence-based combating of terrorism and organized crime, gun crime, cyber-crime and cyber terrorism and cross cultural ideology polarization). In addition to this he is the technical lead of two EU Security projects: Courage” on Cyber-Crime and Cyber-Terrorism and Athena” onthe Application of Social Media and Mobile Devices in Crisis Management. He has co-edited several books on Intelligence Management.. His recent books are titled Strategic Intelligence Management (National Security Imperatives and Information and Communications Technologies)”, Knowledge Driven Frameworks for Combating Terrorism and Organised Crime” and Emerging Trends in ICT Security”. Prof Akhgar is member of the academic advisory board of SAS UK. Hamid R. Arabnia is currently a Full Professor of Computer Science at University of Georgia where he has been since October 1987. His research interests include Parallel and distributed processing techniques and algorithms, interconnection networks, and applications in Computational Science and Computational Intelligence (in particular, in image processing, medical imaging, bioinformatics, and other computational intensive problems). Dr. Arabnia is Editor-in-Chief of The Journal of is Associate Editor of IEEE Transactions on Information Technology in Biomedicine . He has over 300 publications (journals, proceedings, editorship) in his area of research in addition he has edited two titles Emerging Trends in ICT Security (Elsevier 2013), and Advances in Computational Biology (Springer 2012). Andrew Staniforth, Detective Inspector and Advisory Board Member and Senior Research Fellow, Centre of Excellence in Terrorism, Resilience, Intelligence and Organised Crime Research (CENTRIC).
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