TABLE OF CONTENTS William J. Tolone, University of North Carolina at Charlotte, USA
Wei-Ning Xiang, University of North Carolina at Charlotte, USA
Anita Raja, University of North Carolina at Charlotte, USA
David Wilson, University of North Carolina at Charlotte, USA
Qianhong Tang, University of North Carolina at Charlotte, USA
Ken McWilliams, University of North Carolina at Charlotte, USA
Robert K. McNally, University of North Carolina at Charlotte, USA
Copyright 2007, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited
An essential task in critical infrastructure protection is the assessment of critical infrastructure vulnerabilities. The use of scenario sets is widely regarded as the best form for such assessments. Unfortunately, the construction of scenario sets is hindered by a lack in the public domain of critical infrastructure information as such information is commonly confidential, proprietary, or business sensitive. At the same time, there is a wealth of municipal data in the public domain that is pertinent to critical infrastructures. However, to date, there are no reported studies on how to extract only the most relevant CI information from these municipal sources, nor does a methodology exist that guides the practice of CI information mining on municipal data sets. This problem is particularly challenging as these data sets are typically voluminous, heterogeneous, and even entrapping. In this chapter, we propose a knowledge-driven methodology that facilitates the extraction of CI information from public domain, that is, open source, municipal data sets. Under this...
