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Investigative Data Mining for Security and Criminal Detection is the first book to outline how data mining technologies can be used to combat crime in the 21st century. It introduces security managers, law enforcement investigators, counter-intelligence agents, fraud specialists, and information security analysts to the latest data mining techniques and shows how they can be used as investigative tools. Readers will learn how to search public and private databases and networks to flag potential security threats and root out criminal activities even before they occur.

The groundbreaking book reviews the latest data mining technologies including intelligent agents, link analysis, text mining, decision trees, self-organizing maps, machine learning, and neural networks. Using clear, understandable language, it explains the application of these technologies in such areas as computer and network security, fraud prevention, law enforcement, and national defense. International case studies throughout the book further illustrate how these technologies can be used to aid in crime prevention.

Investigative Data Mining for Security and Criminal Detection will also serve as an indispensable resource for software developers and vendors as they design new products for the law enforcement and intelligence communities.


Key Features:

* Covers cutting-edge data mining technologies available to use in evidence gathering and collection

* Includes numerous case studies, diagrams, and screen captures to illustrate real-world applications of data mining

* Easy-to-read format illustrates current and future data mining uses in preventative law enforcement, criminal profiling, counter-terrorist initiatives, and forensic science

Reviews

“It shows how myriad distributed data streams can be harnessed to fight crime. Through easy-to-read prose, the reader learns how to use both public and private databases and networks to find threats and minimize risks. Besides explaining how data mining is done, the book introduces the reader to such techniques as intelligent agents (software that performs user-delegated tasks autonomously), link analysis (a process involving the mapping of the associations between suspects and locations), and text mining (a process used to identify a document's content based on linguistic analysis) and how they can aid law enforcement.
For example, law enforcement in the United Kingdom use text mining to "institutionalize the knowledge of criminal perpetrators and organized gangs and groups," author Jesus Mena writes. Case studies buttress these points. This work is one of the first books to show security professionals the power of data mining as an investigative tool. As such, it is itself a powerful tool for the industry.?
? Security Management

“an eye-opening and powerful book on the newest weapons in criminal and terrorist detection and deterrence. Adult readers desiring an overview can scan the introductory sections to the chapters. More detail-minded and technical readers will enjoy the challenging complexity found in follow-up case studies.?
? The Chicago Sun

“The book is cleanly presented and includes screenshots of software used for data mining and analysis. Charts are used to explain how pieces of information link together in a descriptive manner, and are also used as examples of what some data analysis software can produce when used correctly.?
? Security Forums

Feature

* Introduces cutting-edge technologies in evidence gathering and collection, using clear non-technical language
* Illustrates current and future applications of data mining tools in preventative law enforcement, homeland security, and other areas of crime detection and prevention
* Shows how to construct predictive models for detecting criminal activity and for behavioral profiling of perpetrators
* Features numerous Web links, vendor resources, case studies, and screen captures illustrating the use of artificial intelligence (AI) technologies

정보제공 :

CONTENTS
Introduction = xv
1 Precrime Data Mining = 1
 1.1 Behavioral Profiling = 1
 1.2 Rivers of Scraps = 2
 1.3 Data Mining = 3
 1.4 Investigative Data Warehousing = 4
 1.5 Link Analysis = 5
 1.6 Software Agents = 6
 1.7 Text Mining = 8
 1.8 Neural Networks = 9
 1.9 Machine Learning = 11
 1.10 Precrime = 14
 1.11 September 11, 2001 = 15
 1.12 Criminal Analysis and Data Mining = 15
 1.13 Profiling via Pattern Recognition = 19
 1.14 Calibrating Crime = 22
 1.15 Clustering Burglars : A Case Study = 24
 1.16 The Future = 37
 1.17 Bibliography = 38
2 Investigative Data Warehousing = 39
 2.1 Relevant Data = 39
 2.2 Data Testing = 40
 2.3 The Data Warehouse = 40
 2.4 Demographic Data = 42
 2.5 Real Estate and Auto Data = 46
 2.6 Credit Data = 46
 2.7 Criminal Data = 47
 2.8 Government Data = 55
 2.9 Internet Data = 55
 2.10 XML = 59
 2.11 Data Preparation = 61
 2.12 Interrogating the Data = 63
 2.13 Data Integration = 64
 2.14 Security and Privacy = 65
 2.15 Choice Point : A Case Study = 66
 2.16 Tools for Data Preparation = 68
 2.17 Standardizing Criminal Data = 72
 2.18 Bibliography = 74
3 Link Analysis : Visualizing Associations = 75
 3.1 How Link Analysis Works = 75
 3.2 What Can Link Analysis Do? = 75
 3.3 What Is Link Analysis? = 76
 3.4 Using Link Analysis Networks = 77
 3.5 Fighting Wireless Fraud with Link Analysis : A Case Study = 78
 3.6 Types of Link Analysis = 80
 3.7 Combating Drug Trafficking in Florida with Link Analysis : A Case Study = 81
 3.8 Link Analysis Applications = 82
 3.9 Focusing on Money Laundering via Link Analysis : A Case Study = 84
 3.10 Link Analysis Limitations = 85
 3.11 Link Analysis Tools = 88
 3.12 Bibliography = 104
4 Intelligent Agents : Software Detectives = 107
 4.1 What Can Agents Do? = 107
 4.2 What Is an Agent? = 108
 4.3 Agent Features = 109
 4.4 Why Are Agents Important? = 111
 4.5 Open Sources Agents = 112
 4.6 Secured Sources Agents = 113
 4.7 How Agents Work = 113
 4.8 How Agents Reason = 114
 4.9 Intelligent Agents = 116
 4.10 A Bio-Surveillance Agent : A Case Study = 117
 4.11 Data Mining Agents = 120
 4.12 Agents Tools = 121
 4.13 Bibliography = 123
5 Text Mining : Clustering Concepts = 125
 5.1 What Is Text Mining? = 125
 5.2 How Does Text Mining Work? = 126
 5.3 Text Mining Applications = 127
 5.4 Searching for Clues in Aviation Crashes : A Case Study = 128
 5.5 Clustering News Stories : A Case Study = 130
 5.6 Text Mining for Deception = 132
 5.7 Text Mining Threats = 138
 5.8 Text Mining Tools = 141
 5.9 Bibliography = 157
6 Neural Networks : Classifying Patterns = 159
 6.1 What Do Neural Networks Do? = 159
 6.2 What Is a Neural Network? = 160
 6.3 How Do Neural Networks Work? = 161
 6.4 Types of Network Architectures = 162
 6.5 Using Neural Networks = 163
 6.6 Why Use Neural Networks? = 164
 6.7 Attrasoft Facial Recognition Classifications System : A Demonstration = 165
 6.8 Chicago Internal Affairs Uses Neural Network : A Case Study = 167
 6.9 Clustering Border Smugglers with a SOM : A Demonstration = 169
 6.10 Neural Network Chromatogram Retrieval System : A Case Study = 172
 6.11 Neural Network Investigative Applications = 178
 6.12 Modus Operand! Modeling of Group Offending : A Case Study = 179
 6.13 False Positives = 195
 6.14 Neural Network Tools = 196
 6.15 Bibliography = 204
7 Machine Learning : Developing Profiles = 205
 7.1 What Is Machine Learning? = 205
 7.2 How Machine Learning Works = 206
 7.3 Decision Trees = 207
 7.4 Rules Predicting Crime = 208
 7.5 Machine Learning at the Border : A Case Study = 210
 7.6 Extrapolating Military Data : A Case Study = 212
 7.7 Detecting Suspicious Government Financial Transactions : A Case Study = 213
 7.8 Machine-Learning Criminal Patterns = 219
 7.9 The Decision Tree Tools = 221
 7.10 The Rule-Extracting Tools = 229
 7.11 Machine-Learning Software Suites = 233
 7.12 Bibliography = 248
8 NetFraud : A Case Study = 249
 8.1 Fraud Detection in Real Time = 249
 8.2 Fraud Migrates On-line = 250
 8.3 Credit-Card Fraud = 250
 8.4 The Fraud Profile = 251
 8.5 The Risk Scores = 252
 8.6 Transactional Data = 253
 8.7 Common-Sense Rules = 253
 8.8 Auction Fraud = 254
 8.9 NetFraud = 256
 8.10 Fraud-Detection Services = 257
 8.11 Building a Fraud-Detection System = 258
 8.12 Extracting Data Samples = 259
 8.13 Enhancing the Data = 259
 8.14 Assembling the Mining Tools = 261
 8.15 A View of Fraud = 261
 8.16 Clustering Fraud = 262
 8.17 Detecting Fraud = 264
 8.18 NetFraud in the United Kingdom : A Statistical Study = 266
 8.19 Machine-Learning and Fraud = 267
 8.20 The Fraud Ensemble = 271
 8.21 The Outsourcing Option = 271
 8.22 The Hybrid Solution = 272
 8.23 Bibliography = 273
9 Criminal Patterns : Detection Techniques = 275
 9.1 Patterns and Outliers = 275
 9.2 Money As Data = 276
 9.3 Financial Crime MOs = 277
 9.4 Money Laundering = 279
 9.5 Insurance Crimes = 281
 9.6 Death Claims That Did Not Add Up : A Case Study = 287
 9.7 Telecommunications Crime MOs = 288
 9.8 Identity Crimes = 291
 9.9 A Data Mining Methodology for Detecting Crimes = 293
 9.10 Ensemble Mechanisms for Crime Detection = 296
 9.11 Bibliography = 299
10 Intrusion Detection : Techniques and Systems = 301
 10.1 Cybercrimes = 301
 10.2 Intrusion MOs = 302
 10.3 Intrusion Patterns = 309
 10.4 Anomaly Detection = 309
 10.5 Misuse Detection = 310
 10.6 Intrusion Detection Systems = 310
 10.7 Data Mining for Intrusion Detection : A Case Study from the Mitre Corporation = 313
 10.8 Types of IDSs = 318
 10.9 Misuse IDSs = 318
 10.10 Anomaly IDSs = 319
 10.11 Multiple-Based IDSs = 321
 10.12 Data Mining IDSs = 321
 10.13 Advanced IDSs = 323
 10.14 Forensic Considerations = 324
 10.15 Early Warning Systems = 325
 10.16 Internet Resources = 326
 10.17 Bibliography = 326
11 The Entity Validation System(EVS) : A Conceptual Architecture = 327
 11.1 The Grid = 327
 11.2 GRASP = 328
 11.3 Access Versus Storage = 328
 11.4 The Virtual Federation = 329
 11.5 Web Services = 330
 11.6 The Software Glue = 331
 11.7 The Envisioned EVS = 333
 11.8 Needles in Moving Haystacks = 334
 11.9 Tracking Identities = 336
 11.10 The AI Apprentice = 337
 11.11 Incremental Composites = 338
 11.12 Machine Man = 340
 11.13 Bibliography = 341
12 Mapping Crime : Clustering Case Work = 343
 12.1 Crime Maps = 343
 12.2 Interactive Crime GIS = 345
 12.3 Crime Clusters = 346
 12.4 Modeling the Behavior of Offenders Who Commit Serious Sexual Assaults : A Case Study = 348
 12.5 Decomposing Signatures Software = 363
 12.6 Computer Aided Tracking and Characterization of Homicides and Sexual Assaults(CATCH) = 364
 12.7 Forensic Data Mining = 375
 12.8 Alien Intelligence = 376
 12.9 Bibliography = 378
A 1,000 Online Sources for the Investigative Data Miner = 379
B Intrusion Detection Systems(IDS) Products, Services, Freeware, and Projects = 415
C Intrusion Detection Glossary = 419
D Investigative Data Mining Products and Services = 431
Index = 435