Investigative Data Mining for Security and Criminal Detection

ISBN: 0750676132
EAN: 2147483647

Year: 2005
Pages: 232

Authors: Jesus Mena

BUY ON AMAZON

Table of Contents
BackCover
Investigative Data Mining for Security and Criminal Detection
Introduction
Chapter 1: Precrime Data Mining
1.2 Rivers of Scraps
1.3 Data Mining
1.4 Investigative Data Warehousing
1.5 Link Analysis
1.6 Software Agents
1.7 Text Mining
1.8 Neural Networks
1.9 Machine Learning
1.10 Precrime
1.11 September 11, 2001
1.12 Criminal Analysis and Data Mining
1.13 Profiling via Pattern Recognition
1.14 Calibrating Crime
1.15 Clustering Burglars: A Case Study
1.16 The Future
1.17 Bibliography
Chapter 2: Investigative Data Warehousing
2.2 Data Testing
2.3 The Data Warehouse
2.4 Demographic Data
2.5 Real Estate and Auto Data
2.6 Credit Data
2.7 Criminal Data
2.8 Government Data
2.9 Internet Data
2.10 XML
2.11 Data Preparation
2.12 Interrogating the Data
2.13 Data Integration
2.14 Security and Privacy
2.15 ChoicePoint: A Case Study
2.16 Tools for Data Preparation
2.17 Standardizing Criminal Data
2.18 Bibliography
Chapter 3: Link Analysis: Visualizing Associations
3.2 What Can Link Analysis Do?
3.3 What Is Link Analysis?
3.4 Using Link Analysis Networks
3.5 Fighting Wireless Fraud with Link Analysis: A Case Study
3.6 Types of Link Analysis
3.7 Combating Drug Trafficking in Florida with Link Analysis: A Case Study
3.8 Link Analysis Applications
3.9 Focusing on Money Laundering via Link Analysis: A Case Study
3.10 Link Analysis Limitations
3.11 Link Analysis Tools
3.12 Bibliography
Chapter 4: Intelligent Agents: Software Detectives
4.2 What Is an Agent?
4.3 Agent Features
4.4 Why Are Agents Important?
4.5 Open Sources Agents
4.6 Secured Sources Agents
4.7 How Agents Work
4.8 How Agents Reason
4.9 Intelligent Agents
4.10 A Bio-Surveillance Agent: A Case Study
4.11 Data Mining Agents
4.12 Agents Tools
4.13 Bibliography
Chapter 5: Text Mining: Clustering Concepts
5.2 How Does Text Mining Work?
5.3 Text Mining Applications
5.4 Searching for Clues in Aviation Crashes: A Case Study
5.5 Clustering News Stories: A Case Study
5.6 Text Mining for Deception
5.7 Text Mining Threats
5.8 Text Mining Tools
5.9 Bibliography
Chapter 6: Neural Networks: Classifying Patterns
6.2 What Is a Neural Network?
6.3 How Do Neural Networks Work?
6.4 Types of Network Architectures
6.5 Using Neural Networks
6.6 Why Use Neural Networks?
6.7 Attrasoft Facial Recognition Classifications System: A Demonstration
6.8 Chicago Internal Affairs Uses Neural Network: A Case Study
6.9 Clustering Border Smugglers with a SOM: A Demonstration
6.10 Neural Network Chromatogram Retrieval System: A Case Study
6.11 Neural Network Investigative Applications
6.12 Modus Operandi Modeling of Group Offending: A Case Study
6.13 False Positives
6.14 Neural Network Tools
6.15 Bibliography
Chapter 7: Machine Learning: Developing Profiles
7.2 How Machine Learning Works
7.3 Decision Trees
7.4 Rules Predicting Crime
7.5 Machine Learning at the Border: A Case Study
7.6 Extrapolating Military Data: A Case Study
7.7 Detecting Suspicious Government Financial Transactions: A Case Study
7.8 Machine-Learning Criminal Patterns
7.9 The Decision Tree Tools
7.10 The Rule-Extracting Tools
7.11 Machine-Learning Software Suites
7.12 Bibliography
Chapter 8: NetFraud: A Case Study
8.2 Fraud Migrates On-line
8.3 Credit-Card Fraud
8.4 The Fraud Profile
8.5 The Risk Scores
8.6 Transactional Data
8.7 Common-Sense Rules
8.8 Auction Fraud
8.9 NetFraud
8.10 Fraud-Detection Services
8.11 Building a Fraud-Detection System
8.12 Extracting Data Samples
8.13 Enhancing the Data
8.14 Assembling the Mining Tools
8.15 A View of Fraud
8.16 Clustering Fraud
8.17 Detecting Fraud
8.18 NetFraud in the United Kingdom: A Statistical Study
8.19 Machine-Learning and Fraud
8.20 The Fraud Ensemble
8.21 The Outsourcing Option
8.22 The Hybrid Solution
8.23 Bibliography
Chapter 9: Criminal Patterns: Detection Techniques
9.2 Money As Data
9.3 Financial Crime MOs
9.4 Money Laundering
9.5 Insurance Crimes
9.6 Death Claims That Did Not Add Up: A Case Study
9.7 Telecommunications Crime MOs
9.8 Identity Crimes
9.9 A Data Mining Methodology for Detecting Crimes
9.10 Ensemble Mechanisms for Crime Detection
9.11 Bibliography
Chapter 10: Intrusion Detection: Techniques and Systems
10.2 Intrusion MOs
10.3 Intrusion Patterns
10.4 Anomaly Detection
10.5 Misuse Detection
10.6 Intrusion Detection Systems
10.7 Data Mining for Intrusion Detection: A Case Study from the Mitre Corporation
10.8 Types of IDSs
10.9 Misuse IDSs
10.10 Anomaly IDSs
10.11 Multiple-Based IDSs
10.12 Data Mining IDSs
10.13 Advanced IDSs
10.14 Forensic Considerations
10.15 Early Warning Systems
10.16 Internet Resources
10.17 Bibliography
Chapter 11: The Entity Validation System (EVS): A Conceptual Architecture
11.2 GRASP
11.3 Access Versus Storage
11.4 The Virtual Federation
11.5 Web Services
11.6 The Software Glue
11.7 The Envisioned EVS
11.8 Needles in Moving Haystacks
11.9 Tracking Identities
11.10 The AI Apprentice
11.11 Incremental Composites
11.12 Machine Man
11.13 Bibliography
Chapter 12: Mapping Crime: Clustering Case Work
12.2 Interactive Crime GIS
12.3 Crime Clusters
12.4 Modeling the Behavior of Offenders Who Commit Serious Sexual Assaults: A Case Study
12.5 Decomposing Signatures Software
12.6 Computer Aided Tracking and Characterization of Homicides and Sexual Assaults (CATCH)
12.7 Forensic Data Mining
12.8 Alien Intelligence
12.9 Bibliography
Appendix A: 1,000 Online Sources for the Investigative Data Miner
Military Personnel Web Sites
Directory Web Sites
Criminal Investigative Web Sites
Credit Report Web Sites
Government Web Sites
State Web Sites
Forensic Resources and Information Web Sites
Appendix B: Intrusion Detection Systems (IDS) Products, Services, Freeware, and Projects
IDS Freeware
IDS Research Projects
Appendix C: Intrusion Detection Glossary
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Appendix D: Investigative Data Mining Products and Services
Index
Index_B
Index_C
Index_D
Index_E
Index_F
Index_G
Index_H
Index_I
Index_J
Index_K
Index_L
Index_M
Index_N
Index_O
Index_P
Index_Q
Index_R
Index_S
Index_T
Index_U
Index_V
Index_W
Index_X
Index_Z
List of Figures
List of Tables