A new approach for identification of multiple threat scenarios to counter cbrn networks

Ronald L Breiger, Lauren Pinson

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In recent years, academics and policy makers have debated the growing potential for chemical, biological, radiological, and nuclear (CBRN) terrorist attacks. Current state-of-the-art research on potential adversary intent to acquire or use CBRN weapons has been formulated as linear analysis using multiple regression models (e.g., Asal & Rethemeyer, 2008 & 2009; Hayden, 2009). Analytical emphasis is on the relations among variables. Predictor variables are modeled as having homogeneous effects on the outcome, and coefficients are measures of effects averaged across the cases. By way of contrast, in this chapter we aim to use the variables to learn about the cases. "Cases" are 175 CBRN events occurring in the period 1998-2011 coded in an enhanced, comprehensive, open-source database that provides a new standard of data quality and that we will introduce in this chapter along with our new methods and their application. We turn the usual regression models "inside out" to reveal a network of profile similarity among the cases. We thus change the emphasis "from factors to actors" in predicting CBRN activities. We illustrate a specific example of the gain from our approach: we can identify clusters of cases within which relations among key variables - the effect of a perpetrator group's religious extremism on CBRN weapons pursuit - operate in opposing ways, thus aiding identification of multiple threat scenarios. Problems in the Analysis of CBRN Networks that Our Methods Address CBRN activity is not a homogeneous phenomenon, and therefore analysts require methods to identify relatively homogeneous subsets of cases. An attempted suicide bombing of large chemical tanks at an Israeli port in 2004, the roadside burying of mustard gas containers in Grozny, Chechnya, to thwart the advance of Russian troops in 1999, and private inquiries made to a Bulgarian businessman about the possibility of acquiring spent nuclear fuel rods in 2001 are examples of reported events that are difficult to treat as observations sampled from a homogeneous population. Furthermore, a key assumption of regression modeling is the independence of cases, whereas CBRN analysts are concerned with events related by complex interdependencies both temporal and spatial (a series of events in successive months with common perpetrators or common targets) and social (events perpetrated by organizations bearing distinctive names but sharing some members, or a highly specific ideological orientation, or different sets of members who nonetheless trained at the same camps).

Original languageEnglish (US)
Title of host publicationIlluminating Dark Networks
PublisherCambridge University Press
Pages157-170
Number of pages14
ISBN (Print)9781316212639, 9781107102699
DOIs
StatePublished - Jan 1 2015

Fingerprint

threat
scenario
event
biological weapon
nuclear weapon
regression
Chechnya
businessman
religious group
radicalism
data quality
suicide
Israeli

ASJC Scopus subject areas

  • Social Sciences(all)

Cite this

Breiger, R. L., & Pinson, L. (2015). A new approach for identification of multiple threat scenarios to counter cbrn networks. In Illuminating Dark Networks (pp. 157-170). Cambridge University Press. https://doi.org/10.1017/CBO9781316212639.011

A new approach for identification of multiple threat scenarios to counter cbrn networks. / Breiger, Ronald L; Pinson, Lauren.

Illuminating Dark Networks. Cambridge University Press, 2015. p. 157-170.

Research output: Chapter in Book/Report/Conference proceedingChapter

Breiger, Ronald L ; Pinson, Lauren. / A new approach for identification of multiple threat scenarios to counter cbrn networks. Illuminating Dark Networks. Cambridge University Press, 2015. pp. 157-170
@inbook{13bf1653f7ea4d628bdd85d1bbf74aab,
title = "A new approach for identification of multiple threat scenarios to counter cbrn networks",
abstract = "In recent years, academics and policy makers have debated the growing potential for chemical, biological, radiological, and nuclear (CBRN) terrorist attacks. Current state-of-the-art research on potential adversary intent to acquire or use CBRN weapons has been formulated as linear analysis using multiple regression models (e.g., Asal & Rethemeyer, 2008 & 2009; Hayden, 2009). Analytical emphasis is on the relations among variables. Predictor variables are modeled as having homogeneous effects on the outcome, and coefficients are measures of effects averaged across the cases. By way of contrast, in this chapter we aim to use the variables to learn about the cases. {"}Cases{"} are 175 CBRN events occurring in the period 1998-2011 coded in an enhanced, comprehensive, open-source database that provides a new standard of data quality and that we will introduce in this chapter along with our new methods and their application. We turn the usual regression models {"}inside out{"} to reveal a network of profile similarity among the cases. We thus change the emphasis {"}from factors to actors{"} in predicting CBRN activities. We illustrate a specific example of the gain from our approach: we can identify clusters of cases within which relations among key variables - the effect of a perpetrator group's religious extremism on CBRN weapons pursuit - operate in opposing ways, thus aiding identification of multiple threat scenarios. Problems in the Analysis of CBRN Networks that Our Methods Address CBRN activity is not a homogeneous phenomenon, and therefore analysts require methods to identify relatively homogeneous subsets of cases. An attempted suicide bombing of large chemical tanks at an Israeli port in 2004, the roadside burying of mustard gas containers in Grozny, Chechnya, to thwart the advance of Russian troops in 1999, and private inquiries made to a Bulgarian businessman about the possibility of acquiring spent nuclear fuel rods in 2001 are examples of reported events that are difficult to treat as observations sampled from a homogeneous population. Furthermore, a key assumption of regression modeling is the independence of cases, whereas CBRN analysts are concerned with events related by complex interdependencies both temporal and spatial (a series of events in successive months with common perpetrators or common targets) and social (events perpetrated by organizations bearing distinctive names but sharing some members, or a highly specific ideological orientation, or different sets of members who nonetheless trained at the same camps).",
author = "Breiger, {Ronald L} and Lauren Pinson",
year = "2015",
month = "1",
day = "1",
doi = "10.1017/CBO9781316212639.011",
language = "English (US)",
isbn = "9781316212639",
pages = "157--170",
booktitle = "Illuminating Dark Networks",
publisher = "Cambridge University Press",

}

TY - CHAP

T1 - A new approach for identification of multiple threat scenarios to counter cbrn networks

AU - Breiger, Ronald L

AU - Pinson, Lauren

PY - 2015/1/1

Y1 - 2015/1/1

N2 - In recent years, academics and policy makers have debated the growing potential for chemical, biological, radiological, and nuclear (CBRN) terrorist attacks. Current state-of-the-art research on potential adversary intent to acquire or use CBRN weapons has been formulated as linear analysis using multiple regression models (e.g., Asal & Rethemeyer, 2008 & 2009; Hayden, 2009). Analytical emphasis is on the relations among variables. Predictor variables are modeled as having homogeneous effects on the outcome, and coefficients are measures of effects averaged across the cases. By way of contrast, in this chapter we aim to use the variables to learn about the cases. "Cases" are 175 CBRN events occurring in the period 1998-2011 coded in an enhanced, comprehensive, open-source database that provides a new standard of data quality and that we will introduce in this chapter along with our new methods and their application. We turn the usual regression models "inside out" to reveal a network of profile similarity among the cases. We thus change the emphasis "from factors to actors" in predicting CBRN activities. We illustrate a specific example of the gain from our approach: we can identify clusters of cases within which relations among key variables - the effect of a perpetrator group's religious extremism on CBRN weapons pursuit - operate in opposing ways, thus aiding identification of multiple threat scenarios. Problems in the Analysis of CBRN Networks that Our Methods Address CBRN activity is not a homogeneous phenomenon, and therefore analysts require methods to identify relatively homogeneous subsets of cases. An attempted suicide bombing of large chemical tanks at an Israeli port in 2004, the roadside burying of mustard gas containers in Grozny, Chechnya, to thwart the advance of Russian troops in 1999, and private inquiries made to a Bulgarian businessman about the possibility of acquiring spent nuclear fuel rods in 2001 are examples of reported events that are difficult to treat as observations sampled from a homogeneous population. Furthermore, a key assumption of regression modeling is the independence of cases, whereas CBRN analysts are concerned with events related by complex interdependencies both temporal and spatial (a series of events in successive months with common perpetrators or common targets) and social (events perpetrated by organizations bearing distinctive names but sharing some members, or a highly specific ideological orientation, or different sets of members who nonetheless trained at the same camps).

AB - In recent years, academics and policy makers have debated the growing potential for chemical, biological, radiological, and nuclear (CBRN) terrorist attacks. Current state-of-the-art research on potential adversary intent to acquire or use CBRN weapons has been formulated as linear analysis using multiple regression models (e.g., Asal & Rethemeyer, 2008 & 2009; Hayden, 2009). Analytical emphasis is on the relations among variables. Predictor variables are modeled as having homogeneous effects on the outcome, and coefficients are measures of effects averaged across the cases. By way of contrast, in this chapter we aim to use the variables to learn about the cases. "Cases" are 175 CBRN events occurring in the period 1998-2011 coded in an enhanced, comprehensive, open-source database that provides a new standard of data quality and that we will introduce in this chapter along with our new methods and their application. We turn the usual regression models "inside out" to reveal a network of profile similarity among the cases. We thus change the emphasis "from factors to actors" in predicting CBRN activities. We illustrate a specific example of the gain from our approach: we can identify clusters of cases within which relations among key variables - the effect of a perpetrator group's religious extremism on CBRN weapons pursuit - operate in opposing ways, thus aiding identification of multiple threat scenarios. Problems in the Analysis of CBRN Networks that Our Methods Address CBRN activity is not a homogeneous phenomenon, and therefore analysts require methods to identify relatively homogeneous subsets of cases. An attempted suicide bombing of large chemical tanks at an Israeli port in 2004, the roadside burying of mustard gas containers in Grozny, Chechnya, to thwart the advance of Russian troops in 1999, and private inquiries made to a Bulgarian businessman about the possibility of acquiring spent nuclear fuel rods in 2001 are examples of reported events that are difficult to treat as observations sampled from a homogeneous population. Furthermore, a key assumption of regression modeling is the independence of cases, whereas CBRN analysts are concerned with events related by complex interdependencies both temporal and spatial (a series of events in successive months with common perpetrators or common targets) and social (events perpetrated by organizations bearing distinctive names but sharing some members, or a highly specific ideological orientation, or different sets of members who nonetheless trained at the same camps).

UR - http://www.scopus.com/inward/record.url?scp=84954286768&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84954286768&partnerID=8YFLogxK

U2 - 10.1017/CBO9781316212639.011

DO - 10.1017/CBO9781316212639.011

M3 - Chapter

SN - 9781316212639

SN - 9781107102699

SP - 157

EP - 170

BT - Illuminating Dark Networks

PB - Cambridge University Press

ER -