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Knowledge Discovery for Counterterrorism and Law Enforcement
By David Skillicorn
- Price: $79.95
- Binding/Format: Hardback
- ISBN: 978-1-4200739-9-7
- Publish Date: November 13th 2008
- Imprint: CRC Press
- Pages: 332 pages
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series
Description
Most of the research aimed at counterterrorism, fraud detection, or other forensic applications assumes that this is a specialized application domain for mainstream knowledge discovery. Unfortunately, knowledge discovery changes completely when the datasets being used have been manipulated in order to conceal some underlying activity. Knowledge Discovery for Counterterrorism and Law Enforcement operates from the premise that detection algorithms must be rethought to be effective in this domain, and presents a new approach based on cutting-edge analysis for use in adversarial settings.
Reveals How Criminals Conceal Information
This volume focuses on four main forms of knowledge discovery: prediction, clustering, relationship discovery, and textual analysis. For each of these application areas, the author discusses opportunities for concealment that are available to criminals and reveals some of the tactics that can aid in detecting them. He reviews what is known about the different technologies for each area and evaluates their effectiveness. The book also supplies a preview of technologies currently under development and describes how they will fit in to existing approaches to knowledge discovery.
Provides Proactive Formulas for Staying One Step Ahead of Adversaries
While all knowledge-discovery systems are susceptible to manipulation, designers and users of algorithmic systems who are armed with the knowledge of these subversive tactics are better able to create systems to avoid these vulnerabilities. This book delineates an effective process for integrating knowledge-discovery tools, provides a unique understanding of the limits of the technology, and contains a clear presentation of the upsides and pitfalls of data collection. It is a powerful weapon in the arsenal of anyone confronting the increasingly sophisticated tactics employed by lawbreakers and other unsavory individuals.
Contents
Introduction
What is Knowledge Discovery?
What is an Adversarial Setting?
Algorithmic Knowledge Discovery
State of the Art
Data
Kinds of Data
Data That Changes
Fusion of Different Kinds of Data
How Is Data Collected?
Can Data Be Trusted?
How Much Data?
High-Level Principles
What to Look for
Subverting Knowledge Discovery
Effects of Technology Properties
Sensemaking and Situational Awareness
Taking Account of the Adversarial Setting over Time
Does This Book Help Adversaries?
What about Privacy?
Looking for Risk—Prediction and Anomaly Detection
Goals
Outline of Prediction Technology
Concealment Opportunities
Technologies
Tactics and Process
Extending the Process
Special Case: Looking for Matches
Special Case: Looking for Outliers
Special Case: Frequency Ranking
Special Case: Discrepancy Detection
Looking for Similarity—Clustering
Goals
Outline of Clustering Technology
Concealment Opportunities
Technologies
Tactics and Process
Special Case—Looking for Outliers Revisited
Looking Inside Groups—Relationship Discovery
Goals
Outline of Relationship Discovery Technology
Concealment Opportunities
Technologies
Tactics and Process
Discovery from Public Textual Data
Text as it Reveals Internal State
Goals
Outline of Textual Analysis Technology
Concealment Opportunities
Technologies
Tactics and Process
Discovery in Private Communication
The Impact of Obfuscation
Goals
Concealment Opportunities
Technologies
Tactics and Process
Discovering Mental and Emotional State
Frame Analysis for Intentions
Sentiment Analysis
Mental State Extraction
Systemic Functional Linguistics
The Bottom Line
Framing the Problem
The Process
Applying the Process
Open Problems
Bibliography
Index