Cover image for Homeland security techniques and technologies
Title:
Homeland security techniques and technologies
Author:
Mena, Jesus.
Personal Author:
Publication Information:
Hingham, Mass. : Charles River Media, [2004]

©2004
Physical Description:
xiii, 331 pages : illustrations, maps ; 24 cm + 1 CD-ROM (4 3/4 in.)
General Note:
Includes index.
Language:
English
ISBN:
9781584503286
Format :
Book

Available:*

Library
Call Number
Material Type
Home Location
Status
Item Holds
Searching...
QA76.9.D343 M439 2004 Adult Non-Fiction Central Closed Stacks
Searching...

On Order

Summary

Summary

The strategic use of network-centric software for data aggregation, integration, collaboration, categorization, and pattern-recognition by homeland security personnel at the local, state, and federal level is essential in combating terrorism in the 21st century. With the use of an assortment of software components, self-adaptive intelligent systems can be created for real-time use by a network of analysts. Homeland Security Techniques and Technologies provides important tips and tools necessary for achieving security that is both proper and functional. The book illustrates several key topics required in successful combating terrorism e.g., data warehousing techniques for behavioral profiling and entity validation to prevent identity theft; artificial intelligence and the Internet for the creation of virtual databases of images, html files, and e-mails; aggregating, preparing, and mining data remotely over networks anywhere in the world; and the visual mapping of connecting and tracking individuals on a global scale. Homeland Security Techniques and Technologies is an essential guide to understanding the different forms of terrorism and knowing the techniques of combating and preventing attacks.


Reviews 1

Choice Review

According to data mining consultant Mena, the data gathering, processing, and analysis requirements of organizations charged with homeland security consist of six key tasks: aggregation, integration, collaboration, categorization, intelligence systems, and data mining. The opening chapter presents an overview of homeland security's missions and requirements, along with an introduction to each of the tasks. The six succeeding chapters, respectively, delve into each of these tasks. Each chapter expands on the purpose, function, and possible implementation of the task and cites products, primarily software, applicable to its completion. Many of these products stem from the commercial business community's existing initiatives to spot credit card fraud, identify customer trends, assess credit risks, detect money laundering, etc. Illustrative examples are found in these chapters also. This book provides a roadmap for those responsible for integrating homeland security information technology at the federal, state, and local levels. It may also interest others seeking an understanding of the challenges and approaches to homeland security's data handing, including its important pattern-recognition requirement. The accompanying CD-ROM demonstrates several of the cited software products and contains all of the book's figures. ^BSumming Up: Recommended. General readers; professionals. E. M. Aupperle emeritus, University of Michigan


Table of Contents

Dedicationp. v
Introductionp. vii
1 Overview: The Homeland Security (HS) Tasks, Technologies, and Processesp. 1
Preamble: The HS Requirementsp. 2
The HS Processes and Objectivesp. 4
The HS Missionsp. 6
Intelligence and Warningp. 6
Border and Transportation Securityp. 6
Domestic Counterterrorismp. 7
Protecting Critical Infrastructures and Key Assetsp. 7
Defending Against Catastrophic Threatsp. 7
Emergency Preparedness and Responsep. 8
Task 1 Aggregation: The Data Componentsp. 8
Task 2 Integration: Virtual Databasesp. 13
Web Services and XMLp. 16
Task 3 Collaboration in Real Timep. 19
Task 4 Categorization: Clustering Conceptsp. 22
Task 5 Intelligence Systems for Detecting Terrorist Crimesp. 26
Task 6 Data Mining: Embedded and Distributedp. 28
Conclusion: The Information Technology Terrainp. 33
The Perpetual Evolutionary Tasksp. 34
2 Aggregation: How to Leverage the Web, Robots, and Commercial Demographics for Entity Validationp. 37
Zero Latencyp. 38
Dynamic Data Warehouse (DDM)p. 39
The Web: Internet Mechanismsp. 40
IP Addressesp. 41
Log Files: Clickstream Datap. 42
Cookiesp. 45
Bugsp. 47
Formsp. 48
Robots: Data Collection Softwarep. 49
AISp. 49
Fetchp. 50
Mobularp. 50
Commercial Demographics: Entity Validationp. 52
Acxiomp. 52
ChoicePointp. 56
Claritasp. 58
DataQuickp. 73
Experianp. 73
Polkp. 74
SRCp. 76
TransUnionp. 77
Conclusion: From Aggregation to Integrationp. 78
3 Integration: The Components, Adapters, Middleware, and Web Services for Information Sharingp. 81
The Integrators: Framework Providersp. 82
Integrator: Ascentialp. 82
Integrator: BEAp. 83
The Cultural, Legal, and Technical Obstaclesp. 84
Integrator: IBMp. 85
Integrator: Informaticap. 86
The Benefits: Speed and Accuracyp. 87
Integrator: Information Builders (iWay)p. 88
Integrator: IONAp. 91
Integrator: MetaMatrixp. 92
The Architecture: The XML Gluep. 94
Integrator: Microsoftp. 99
Integrator: SeeBeyondp. 99
Integrator: TIBCOp. 101
Integrator: Vitriap. 104
Homeland Security (HS) Requirementsp. 105
Integrator: WebMethodsp. 106
The Future: From 22 to 1 in 2002p. 106
4 Collaboration: The Technologies for Communicating Content, Expertise, and Analyses in Real Timep. 109
Collaboration Components: Presence, Messaging, Discussion, Meeting, Sharing, and Virtualp. 111
Collective Knowledge: Locating, Extracting, Organizing, and Routing Tacit Knowledgep. 116
Benefits of Collaboration: One Mission, Different Culturesp. 117
The Collaborators: Acquire, Learn, and Discoverp. 119
Experts and Personalization: The Dynamic Organizationp. 136
Expertise Collaborators: Who Knows Whatp. 137
Mapping Collaboration: Search, Retrieval and Categorizationp. 139
Instant Collaborators: Presence Messagingp. 140
Integrating a Collaboration Process: A Communications Culturep. 144
Key Issues in Collaboration: Five Factorsp. 145
Threat Matrix Collaboration: Multi-Agency and Multimediap. 146
5 Categorization: The Techniques for the Clustering of Concepts from Unstructured Contentp. 153
Categorization Techniquesp. 155
Bayesian Networks: Categorizing Uncertaintyp. 155
Support Vector Machines: Categorization via Vectorsp. 157
Neural Networks: Mapping Meaningp. 157
Clustering Conceptsp. 160
Linguistic Analysis Techniquesp. 162
Mapping Contentp. 163
Personalized Contentp. 164
The Categorization Providersp. 165
The Evolution of Categorization of Unstructured Contentp. 181
Categorization for Homeland Securityp. 184
6 Intelligence: Systems for Detecting Terrorist Crimesp. 187
Identity Theftp. 188
A Crime of Our Timep. 190
Terrorist Theftp. 192
Visa Theftp. 196
Technologies for Detecting ID Crimesp. 197
National Report on Identity Fraudp. 200
ID Score by ID Analyticsp. 203
ID Fraud Intercept by Fair Isaacp. 204
The Fair and Accurate Credit Transactions Act of 2003p. 205
Name Recognitionp. 209
AeroText (Lockheed Martin)p. 210
IdentiFinder (BBN/Verizon)p. 210
Intelligent Miner for Text (IBM)p. 210
NetOwl (SRA)p. 211
Thing Finder (Inxight)p. 212
Government Use of Name Recognition Technologiesp. 213
Search Software America (SSA)p. 215
Language Analysis Systems (LAS)p. 216
Money Launderingp. 227
Anti-Money Laundering (AML) Technologiesp. 229
GIFTS Softwarep. 232
Mantasp. 233
NetEconomyp. 235
Searchspacep. 235
AML Technology for Homeland Securityp. 245
7 Mining: Pattern Recognition and Agent Technologies for Analyzing Text and Data Remotelyp. 247
What Is Data Mining? Discovery and Exploitationp. 248
Our Distributed Data World: Connecting the Dotsp. 249
Text Miningp. 251
The Challenges of Unstructured Data Analysis: Extracting Knowledgep. 251
The Text Mining Technologies: Software That Understandsp. 252
Text Mining Processes: Categorization and Information Extractionp. 253
The Text Mining Tools: From Unstructured to Structuredp. 254
Data Miningp. 257
The Data Mining Processes: Prediction and Descriptionp. 257
The Data Mining Technologies: Rules, Ratios, and Codep. 258
The Data Mining Suites: Toolboxes of Algorithmsp. 266
SPSSp. 275
Embedded Data Miningp. 284
Distributed Data Miningp. 287
The Future of Homeland Security: Hybrid Self-Evolving Systemsp. 314
About the CD-ROMp. 317
Indexp. 321