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.
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