Akhgar / Saathoff / Arabnia Application of Big Data for National Security

A Practitioner's Guide to Emerging Technologies
1. Auflage 2015
ISBN: 978-0-12-801973-3
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: 6 - ePub Watermark

E-Book, Englisch, 316 Seiten

Application of Big Data for National Security provides users with state-of-the-art concepts, methods, and technologies for Big Data analytics in the fight against terrorism and crime, including a wide range of case studies and application scenarios. This book combines expertise from an international team of experts in law enforcement, national security, and law, as well as computer sciences, criminology, linguistics, and psychology, creating a unique cross-disciplinary collection of knowledge and insights into this increasingly global issue. The strategic frameworks and critical factors presented in Application of Big Data for National Security consider technical, legal, ethical, and societal impacts, but also practical considerations of Big Data system design and deployment, illustrating how data and security concerns intersect. In identifying current and future technical and operational challenges it supports law enforcement and government agencies in their operational, tactical and strategic decisions when employing Big Data for national security - Contextualizes the Big Data concept and how it relates to national security and crime detection and prevention - Presents strategic approaches for the design, adoption, and deployment of Big Data technologies in preventing terrorism and reducing crime - Includes a series of case studies and scenarios to demonstrate the application of Big Data in a national security context - Indicates future directions for Big Data as an enabler of advanced crime prevention and detection

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.

Autoren/Hrsg.

Akhgar, Babak

Saathoff, Gregory B.

Arabnia, Hamid R

Hill, Richard

Staniforth, Andrew

Bayerl, Petra Saskia

Inhaltsverzeichnis

1;Front Cover;1
2;Application of Big Data for National Security: A Practitioner’s Guide to Emerging Technologies;4
3;Copyright;5
4;Contents;6
5;List of Contributors;16
6;About the Editors;18
7;Foreword by Lord Carlile of Berriew;20
8;Preface by Edwin Meese III;22
9;Acknowledgments;24
10;Section 1 - INTRODUCTION TO BIG DATA;26
10.1;Chapter 1 - An Introduction to Big Data;28
10.1.1;WHAT IS BIG DATA?;28
10.1.2;HOW DIFFERENT IS BIG DATA?;29
10.1.3;MORE ON BIG DATA: TYPES AND SOURCES;29
10.1.4;THE FIVE V’S OF BIG DATA;30
10.1.5;BIG DATA IN THE BIG WORLD;32
10.1.6;ANALYTICAL CAPABILITIES OF BIG DATA;34
10.1.7;STREAMING ANALYTICS;35
10.1.8;AN OVERVIEW OF BIG DATA SOLUTIONS;36
10.1.9;CONCLUSIONS;37
10.1.10;REFERENCES;37
10.2;CHAPTER 2 - DRILLING INTO THE BIG DATA GOLD MINE: DATA FUSION AND HIGH-PERFORMANCE ANALYTICS FOR INTELLIGENCE PROFESSIONALS;39
10.2.1;INTRODUCTION;39
10.2.2;THE AGE OF BIG DATA AND HIGH-PERFORMANCE ANALYTICS;39
10.2.3;TECHNOLOGY CHALLENGES;40
10.2.4;EXAMPLES;44
10.2.5;CONCLUSION;45
10.2.6;REFERENCE;45
11;Section 2 - CORE CONCEPTS AND APPLICATION SCENARIOS;46
11.1;CHAPTER 3 - HARNESSING THE POWER OF BIG DATA TO COUNTER INTERNATIONAL TERRORISM;48
11.1.1;INTRODUCTION;48
11.1.2;A NEW TERROR;49
11.1.3;CHANGING THREAT LANDSCAPE;58
11.1.4;EMBRACING BIG DATA;59
11.1.5;CONCLUSION;61
11.1.6;REFERENCES;62
11.2;CHAPTER 4 - BIG DATA AND LAW ENFORCEMENT: ADVANCES, IMPLICATIONS, AND LESSONS FROM AN ACTIVE SHOOTER CASE STUDY;64
11.2.1;THE INTERSECTION OF BIG DATA AND LAW ENFORCEMENT;64
11.2.2;CASE EXAMPLE AND WORKSHOP OVERVIEW;66
11.2.3;SITUATIONAL AWARENESS;68
11.2.4;TWITTER AS A SOCIAL MEDIA SOURCE OF BIG DATA;70
11.2.5;SOCIAL MEDIA DATA ANALYZED FOR THE WORKSHOP;70
11.2.6;TOOLS AND CAPABILITIES PROTOTYPES DURING THE WORKSHOP;71
11.2.7;LAW ENFORCEMENT FEEDBACK FOR THE SESSIONS;76
11.2.8;DISCUSSION;76
11.2.9;ACKNOWLEDGMENTS;77
11.2.10;REFERENCES;77
11.3;CHAPTER 5 - INTERPRETATION AND INSIDER THREAT: REREADING THE ANTHRAX MAILINGS OF 2001 THROUGH A “BIG DATA” LENS;80
11.3.1;INTRODUCTION;80
11.3.2;IMPORTANCE OF THE CASE;82
11.3.3;THE ADVANCEMENT OF BIG DATA ANALYTICS AFTER 2001;83
11.3.4;RELEVANT EVIDENCE;84
11.3.5;POTENTIAL FOR STYLOMETRIC AND SENTIMENT ANALYSIS;86
11.3.6;POTENTIAL FOR FURTHER PATTERN ANALYSIS AND VISUALIZATION;88
11.3.7;FINAL WORDS: INTERPRETATION AND INSIDER THREAT;89
11.3.8;REFERENCES;90
11.4;CHAPTER 6 - CRITICAL INFRASTRUCTURE PROTECTION BY HARNESSING BIG DATA;93
11.4.1;INTRODUCTION;93
11.4.2;UNDERSTANDING THE STRATEGIC LANDSCAPE INTO WHICH BIG DATA MUST BE APPLIED;94
11.4.3;WHAT IS MEANT BY AN OVERARCHING ARCHITECTURE?;98
11.4.4;UNDERPINNING THE SCR;101
11.4.5;STRATEGIC COMMUNITY ARCHITECTURE FRAMEWORK;102
11.4.6;CONCLUSIONS;105
11.4.7;REFERENCES;105
11.5;CHAPTER 7 - MILITARY AND BIG DATA REVOLUTION;106
11.5.1;RISK OF COLLAPSE;106
11.5.2;INTO THE BIG DATA ARENA;107
11.5.3;SIMPLE TO COMPLEX USE CASES;108
11.5.4;CANONIC USE CASES;112
11.5.5;MORE ON THE DIGITAL VERSION OF THE REAL WORLD (SEE THE WORLD AS EVENTS);114
11.5.6;REAL-TIME BIG DATA SYSTEMS;116
11.5.7;IMPLEMENTING THE REAL-TIME BIG DATA SYSTEM;120
11.5.8;INSIGHT INTO DEEP DATA ANALYTICS TOOLS AND REAL-TIME BIG DATA SYSTEMS;127
11.5.9;VERY SHORT LOOP AND BATTLEFIELD BIG DATA DATACENTERS;129
11.5.10;CONCLUSIONS;129
11.5.11;FURTHER READING;131
11.6;CHAPTER 8 - CYBERCRIME: ATTACK MOTIVATIONS AND IMPLICATIONS FOR BIG DATA AND NATIONAL SECURITY;133
11.6.1;INTRODUCTION;133
11.6.2;DEFINING CYBERCRIME AND CYBERTERRORISM;135
11.6.3;ATTACK CLASSIFICATION AND PARAMETERS;136
11.6.4;WHO PERPETRATES THESE ATTACKS?;138
11.6.5;TOOLS USED TO FACILITATE ATTACKS;140
11.6.6;MOTIVATIONS;142
11.6.7;ATTACK MOTIVATIONS TAXONOMY;143
11.6.8;DETECTING MOTIVATIONS IN OPEN-SOURCE INFORMATION;147
11.6.9;CONCLUSION;148
11.6.10;REFERENCES;148
12;Section 3 - METHODS AND TECHNOLOGICAL SOLUTIONS;154
12.1;CHAPTER 9 - REQUIREMENTS AND CHALLENGES FOR BIG DATA ARCHITECTURES;156
12.1.1;WHAT ARE THE CHALLENGES INVOLVED IN BIG DATA PROCESSING?;156
12.1.2;TECHNOLOGICAL UNDERPINNING;157
12.1.3;PLANNING FOR A BIG DATA PLATFORM;159
12.1.4;CONCLUSIONS;164
12.1.5;REFERENCES;164
12.2;CHAPTER 10 - TOOLS AND TECHNOLOGIES FOR THE IMPLEMENTATION OF BIG DATA;165
12.2.1;INTRODUCTION;165
12.2.2;TECHNIQUES;166
12.2.3;Analysis;167
12.2.4;COMPUTATIONAL TOOLS;169
12.2.5;IMPLEMENTATION;170
12.2.6;PROJECT INITIATION AND LAUNCH;171
12.2.7;DATA SOURCES AND ANALYTICS;175
12.2.8;ANALYTICS PHILOSOPHY: ANALYSIS OR SYNTHESIS;176
12.2.9;GOVERNANCE AND COMPLIANCE;177
12.2.10;REFERENCES;178
12.3;CHAPTER 11 - MINING SOCIAL MEDIA: ARCHITECTURE, TOOLS, AND APPROACHES TO DETECTING CRIMINAL ACTIVITY;180
12.3.1;INTRODUCTION;180
12.3.2;MINING OF SOCIAL NETWORKS FOR CRIME;182
12.3.3;TEXT MINING;183
12.3.4;NATURAL LANGUAGE METHODS;183
12.3.5;GENERAL ARCHITECTURE AND VARIOUS COMPONENTS OF TEXT MINING;184
12.3.6;AUTOMATIC EXTRACTION OF BNS FROM TEXT;190
12.3.7;BNS AND CRIME DETECTION;192
12.3.8;CONCLUSIONS;194
12.3.9;REFERENCES;195
12.4;CHAPTER 12 - MAKING SENSE OF UNSTRUCTURED NATURAL LANGUAGE INFORMATION;198
12.4.1;INTRODUCTION;198
12.4.2;BIG DATA AND UNSTRUCTURED DATA;199
12.4.3;ASPECTS OF UNCERTAINTY IN SENSE MAKING;200
12.4.4;SITUATION AWARENESS AND INTELLIGENCE;201
12.4.5;PROCESSING NATURAL LANGUAGE DATA;202
12.4.6;STRUCTURING NATURAL LANGUAGE DATA;203
12.4.7;TWO SIGNIFICANT WEAKNESSES;204
12.4.8;AN ALTERNATIVE REPRESENTATION FOR FLEXIBILITY;205
12.4.9;CONCLUSIONS;207
12.4.10;REFERENCES;207
12.5;CHAPTER 13 - LITERATURE MINING AND ONTOLOGY MAPPING APPLIED TO BIG DATA;209
12.5.1;INTRODUCTION;209
12.5.2;BACKGROUND;210
12.5.3;ARIANA: ADAPTIVE ROBUST INTEGRATIVE ANALYSIS FOR FINDING NOVEL ASSOCIATIONS;212
12.5.4;CONCEPTUAL FRAMEWORK OF ARIANA;212
12.5.5;IMPLEMENTATION OF ARIANA FOR BIOMEDICAL APPLICATIONS;218
12.5.6;CASE STUDIES;226
12.5.7;DISCUSSION;227
12.5.8;CONCLUSIONS;229
12.5.9;ACKNOWLEDGMENT;230
12.5.10;REFERENCES;230
12.6;CHAPTER 14 - BIG DATA CONCERNS IN AUTONOMOUS AI SYSTEMS;234
12.6.1;INTRODUCTION;234
12.6.2;ARTIFICIALLY INTELLIGENT SYSTEM MEMORY MANAGEMENT;235
12.6.3;ARTIFICIAL MEMORY PROCESSING AND ENCODING;237
12.6.4;CONSTRUCTIVIST LEARNING;243
12.6.5;PRACTICAL SOLUTIONS FOR SECURE KNOWLEDGE DEVELOPMENT IN BIG DATA ENVIRONMENTS;246
12.6.6;CONCLUSIONS;249
12.6.7;REFERENCES;250
13;Section 4 - LEGAL AND SOCIAL CHALLENGES;252
13.1;CHAPTER 15 - THE LEGAL CHALLENGES OF BIG DATA APPLICATION IN LAW ENFORCEMENT;254
13.1.1;INTRODUCTION;254
13.1.2;LEGAL FRAMEWORK;255
13.1.3;CONCLUSIONS;261
13.1.4;REFERENCES;262
13.2;CHAPTER 16 - BIG DATA AND THE ITALIAN LEGAL FRAMEWORK: OPPORTUNITIES FOR POLICE FORCES;263
13.2.1;INTRODUCTION;263
13.2.2;EUROPEAN LEGAL FRAMEWORK;264
13.2.3;THE ITALIAN LEGAL FRAMEWORK;267
13.2.4;OPPORTUNITIES AND CONSTRAINTS FOR POLICE FORCES AND INTELLIGENCE;270
13.2.5;REFERENCES;273
13.3;CHAPTER 17 - ACCOUNTING FOR CULTURAL INFLUENCES IN BIG DATA ANALYTICS;275
13.3.1;INTRODUCTION;275
13.3.2;CONSIDERATIONS FROM CROSS-CULTURAL PSYCHOLOGY FOR BIG DATA ANALYTICS;276
13.3.3;CULTURAL DEPENDENCE IN THE SUPPLY AND DEMAND SIDES OF BIG DATA ANALYTICS;277
13.3.4;(MIS)MATCHES AMONG PRODUCER, PRODUCTION, INTERPRETER, AND INTERPRETATION CONTEXTS;281
13.3.5;INTEGRATING CULTURAL INTELLIGENCE INTO BIG DATA ANALYTICS: SOME RECOMMENDATIONS;282
13.3.6;CONCLUSIONS;283
13.3.7;REFERENCES;284
13.4;CHAPTER 18 - MAKING SENSE OF THE NOISE: AN ABC APPROACH TO BIG DATA AND SECURITY;286
13.4.1;HOW HUMANS NATURALLY DEAL WITH BIG DATA;286
13.4.2;THE THREE STAGES OF DATA PROCESSING EXPLAINED;287
13.4.3;THE PUBLIC ORDER POLICING MODEL AND THE COMMON OPERATIONAL PICTURE;290
13.4.4;APPLICATIONS TO BIG DATA AND SECURITY;292
13.4.5;APPLICATION TO BIG DATA AND NATIONAL SECURITY;295
13.4.6;A FINAL CAVEAT FROM THE FBI BULLETIN;297
13.4.7;REFERENCES;297
14;Glossary;300
15;Index;304

Leseproben

Chapter 1 An Introduction to Big Data
John Panneerselvam, Lu Liu,  and Richard Hill Abstract
Data generation has increased drastically over the past few years, leading enterprises dealing with data management to swim in an enormous pool of data. Data management has also grown in importance because extracting the significant value out of a huge pile of raw data is of prime important for enterprises to make business decisions. The governance and management of an organization's data involve orchestrating both people and technology in such a way that the data become a valuable asset for both enterprises and society. With the drastic volume of data being generated every day and the growing importance of data management, understanding of Big Data is a fundamental requirement for those who wish to gain new insight into future challenges. This chapter introduces the concept of Big Data and gives an overview of the types, nature, advantages, and applications of Big Data in today's technological domain. Keywords
Cloud; Datasets; Dynamic; Processing; Raw; Real time; Sources; Value What Is Big Data?
Today, roughly half of the world population interacts with online services. Data are generated at an unprecedented scale from a wide range of sources. The way we view and manipulate the data is also changing, as we discover new ways of discovering insights from unstructured data sources. Managing data volume has changed considerably over recent years (Malik, 2013), because we need to cope with demands to deal with terabytes, petabytes, and now even zettabytes. Now we need to have a vision that includes what the data might be used for in the future so that we can begin to plan and budget for likely resources. A few terabytes of data are quickly generated by a commercial business organization, and individuals are starting to accumulate this amount of personal data. Storage capacity has roughly doubled every 14 months over the past 3 decades. Concurrently, the price of data storage has reduced, which has affected the storage strategies that enterprises employ (Kumar et al., 2012) as they buy more storage rather than determine what to delete. Because enterprises have started to discover new value in data, they are treating it like a tangible asset (Laney, 2001). This enormous generation of data, along with the adoption of new strategies to deal with the data, has caused the emergence of a new era of data management, commonly referred to as Big Data. Big Data has a multitude of definitions, with some research suggesting that the term itself is a misnomer (Eaton et al., 2012). Big Data challenges the huge gap between analytical techniques used historically for data management, as opposed to what we require now (Barlow, 2013). The size of datasets has always grown over the years, but we are currently adopting improved practices for large-scale processing and storage. Big Data is not only huge in terms of volume, it is also dynamic and has various forms. On the whole, we have never seen these kinds of data in the history of technology. Broadly speaking, Big Data can be defined as the emergence of new datasets with massive volume that change at a rapid pace, are very complex, and exceed the reach of the analytical capabilities of commonly used hardware environments and software tools for data management. In short, the volume of data has become too large to handle with conventional tools and methods. With advances in science, medicine, and business, the sources that generate data increase every day, especially from electronic communications as a result of human activities. Such data are generated from e-mail, radiofrequency identification, mobile communication, social media, health care systems and records, enterprise data such as retail, transport, and utilities, and operational data from sensors and satellites. The data generated from these sources are usually unprocessed (raw) and require various stages of processing for analytics. Generally, some processing converts unstructured data into semi-structured data; if they are processed further, the data are regarded as structured. About 80% of the world’s data are semi-structured or unstructured. Some enterprises largely dealing with Big Data are Facebook, Twitter, Google, and Yahoo, because the bulk of their data are regarded as unstructured. As a consequence, these enterprises were early adopters of Big Data technology. The Internet of Things (IoT) has increased data generation dramatically, because patterns of usage of IoT devices have changed recently. A simple snapshot event has turned out to be a data generation activity. Along with image recognition, today’s technology allows users to take and name a photograph, identify the individuals in the picture, and include the geographical location, time and date, before uploading the photo over the Internet within an instance. This is a quick data generation activity with considerable volume, velocity, and variety. How Different Is Big Data?
The concept of Big Data is not new to the technological community. It can be seen as the logical extension of already existing technology such as storage and access strategies and processing techniques. Storing data is not new, but doing something meaningful (Hofstee et al., 2013) (and quickly) with the stored data is the challenge with Big Data (Gartner, 2011). Big Data analytics has something more to do with information technology management than simply dealing with databases. Enterprises used to retrieve historical data for processing to produce a result. Now, Big Data deals with real-time processing of the data and producing quick results (Biem et al., 2013). As a result, months, weeks, and days of processing have been reduced to minutes, seconds, and even fractions of seconds. In reality, the concept of Big Data is making things possible that would have been considered impossible not long ago. Most existing storage strategies followed a knowledge management–based storage approach, using data warehouses (DW). This approach follows a hierarchy flowing from data to information, knowledge, and wisdom, known as the DIKW hierarchy. Elements in every level constitute elements for building the succeeding level. This architecture makes the accessing policies more complex and most of the existing databases are no longer able to support Big Data. Big Data storage models need more accuracy, and the semi-structured and the unstructured nature of Big Data is driving the adoption of storage models that use cross-linked data. Even though the data relate to each other and are physically located in different parts of the DW, logical connection remains between the data. Typically we use algorithms to process data in standalone machines and over the Internet. Most or all of these algorithms are bounded by space and time constraints, and they might lose logical functioning if an attempt is made to exceed their bound limitations. Big Data is processed with algorithms (Gualtieri, 2013) that possess the ability to function on a logically connected cluster of machines without limited time and space constraints. Big Data processing is expected to produce results in real time or near–real time, and it is not meaningful to produce results after a prolonged period of processing. For instance, as users search for information using a search engine, the results that are displayed may be interspersed with advertisements. The advertisements will be for products or services that are related to the user’s query. This is an example of the real-time response upon which Big Data solutions are focused. More on Big Data: Types and Sources
Big Data arises from a wide variety of sources and is categorized based on the nature of the data, their complexity in processing, and the intended analysis to extract a value for a meaningful execution. As a consequence, Big Data is classified as structured data, unstructured data, and semi-structured data. Structured Data
Most of the data contained in traditional database systems are regarded as structured. These data are particularly suited to further analysis because they are less complex with defined length, semantics, and format. Records have well-defined fields with a high degree of organization (rows and columns), and the data usually possess meaningful codes in a standard form that computers can easily read. Often, data are organized into semantic chunks, and similar chunks with common description are usually grouped together. Structured data can be easily stored in databases and show reduced analytical complexity in searching, retrieving, categorizing, sorting, and analyzing with defined criteria. Structured data come from both machine- and human-generated sources. Without the intervention of humans for data generation, some machine-generated datasets include sensor data, Web log data, call center detail records, data from smart meters, and trading systems. Humans interact with computers to generate data such as input data, XML data, click stream data, traditional enterprise data such as customer information from customer relationship management systems, and enterprise resource planning data, general ledger data, financial data, and so on. Unstructured Data
Conversely, unstructured data lack a predefined data format and do not fit well into the traditional relational database systems. Such data do not follow any rules or recognizable patterns and can be unpredictable. These data are more complex to explore, and their analytical complexity is high in terms of capture, storage, processing,...