Terrorism Risk Management
Book:
Bayesian Networks: Practical Guide Application
Edited By : Olivier Pourret
Chapter : 14:
Authors of the Paper:
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David C. Daniels
Linwood D.Hudson
Kathryn B. Laskey
Suzanne M. Mahoney
Bryan S. Ware
Edward J. Wright
Introduction

The U.S military defines Antiterrorism as the
defensive posture taken against terrorist threats

Antiterrorism includes
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Fostering awareness of potential threats,
Deterring aggressors,
Developing security measures,
Planning for future events,
Prohibition of an event in process and
Mitigating and managing the consequences of an event.

A key element of an en effective antiterrorist
strategy is evaluating individual sites or
assets for terrorist risk

Assessing the threat of a terrorist attack
requires combining information from
multiple disparate sources involving intrinsic
uncertainties

Terrorism Risk Management due to this
inherent uncertainty becomes a natural
domain for application of Bayesian Networks
Topics Covered

Methodologies that have been applied to Terrorism
Risk Management

Strengths and Weaknesses of each methodology

How BN addresses all the weaknesses

Description of Site Profiler Installation Security
Planner (ISP) suite for risk managers and security
planners to evaluate risk of a terrorist attack

Software Implementation of Risk Influence Network
What is Risk ?

Risk: possibility of suffering from any type of harm
or loss to individual, organization or entire society

Risk Management:
Identifying and implementing policies to protect
against a risk

Degree of Risk:
Likelihood of event * Measure of Adverse Effect

Measure of Adverse Effect:
◦ Monitory Loss
◦ Non monitory such as death, suffering etc
Terrorism Risk Management
Methodologies
Risk Mnemonics
 Algebraic Expressions of Risk

Risk= Threat *Vulnerability*Consequence
Fault Trees
 Simulations

Risk Mnemonics
S#
Risk Mnemonic
Approach
Application
Drawbacks
1.
CARVER (Criticality,
Accessibility,
Recognizability,
Vulnerability, Effect and
Recoverability)
Score each
factor on a ten
point scale and
adding the
scores
Developed by
US forces
during the Viet
Nam conflict
to optimize
targeting of
enemy
installations
•Non specific
to particular
threats
•Laborintensive
•Non scalable
to many assets
Installation
Subjective Risk
Planner assigns Assessment
the score from used by US
1 to 5 and
military to
then the
identify the
points are
assets at
Criticality , Accessibility,
Recognizability,
summed to
highest risk of
Vulnerability, Effect
and
Recoverability
rank
potential
terrorist
targets
attack
None of these
scores are
adjusted based
on the threat,
type of target
or any special
consideration
2.
DSHARPP(Demography,
Susceptibility, History,
Accessibility and
Recognizability, Proximity
Population) :
andCARVER
Algebraic Expressions of Risk
S#
Risk Mnemonic
Approach
Application
Drawbacks
3.
SNJTK (Special Needs
Jurisdiction Tool Kit)
An asset based
risk approach
that uses
Critical Asset
Factors for
evaluation of
threat-asset
scenario
Developed for
DHS by Office
of Domestic
Preparedness
•Similar to BN
approach in
expert
judgment but
since threat is
not considered
so not a true
metric of risk
4.
CAPRA (Critical Asset and Five Expert
Portfolio Risk Analysis)
Evaluation
Phases related
to mission
critical
elements
Developed by
University of
Maryland for
asset driven
approach
subjected to
expert
judgment using
parametric
equation
Though expert
based it is
unclear how
the risk
equation was
derived or
validated
Other Approaches

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Fault Trees:
Assumes a threat baseline and uses decision
paths to evaluate the probabilities and
outcomes of different outcomes e.g
OCTAVE
 Simulations:
Focus on the consequences of terrorist
attack and most are applicable to specific
type of assets and threat scenarios
Site Profiler Approach to Terrorism
Risk Management

An Asset risk management program that
has been designed to evaluate the risk of
terrorist attack.
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Methodology employs a knowledge-base
Bayesian Network construction to
combine evidence from analytical models,
simulations, historical data and user
judgments
Why Site Profiler?
Individuality of Risk Scenarios
 Intrinsic Uncertainty
 Defensible Methodology
 Flexibility
 Modifiability, maintainability and Extensibility
 Customization
 Usability
 Portfolio management
 Tractability

Why Bayesian Networks ?
Analytical Method for quantitative assessment of risks
 Coherent means of combining objective and subjective data

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Well suited for complex problem solving involving large
number of interrelated uncertain variables

Logically coherent calculus

Tractable algorithms exist for calculating and updating
evidential support

BN can combine inputs from diverse sources
Bayesian Networks for Analyzing
Risk
Clusters of variables for a particular domain
 These clusters are used to define BN fragments
 For example:
Clusters of variables corresponding to characteristics of
valuable asset. Fragment is created corresponding to the
concept of an asset
 If some uncertain variable is related more than one type
of entity we name it relational entity type to
representing pairing
 Each fragment is Manageable and tested independently
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Risk Influence Network

The heart of Site Profiler is Risk Influence
Network

It is a Bayesian network constructed on a
fly from knowledge base of BN Fragments
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Used to assess relative risk of an attack
against an asset by a specific threat
Steps Involved

Knowledge Representation (MEBN)
MEBN is not a computer language such as Java or
C++, or an application such as Netica or Hugin.
Rather, it is formal system that instantiates first-order
Bayesian logic
That is, MEBN provides syntax, a set of model
construction and inference processes, and semantics
that together provide a means of defining probability
distributions over unbounded and possibly infinite
numbers of interrelated hypotheses.

Knowledge-base development
Concept Definition:

Data Physical and Domain data

MFRag for seven type of entities
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Assets, Threats, Tactics, Weapon systems, Targets,

attacks and Attack Consequences
Formal Definition and Analysis
Subsection review by Experts
Scenario Elicitation and Revision
Implementation (cRIN and uRIN)
Operational Revision
Software Implementation

Uses Object Oriented Database to manage Mfrag
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Mfrag:
Like a BN, an MFrag contains nodes, which
represent Random Variables, arranged in a
directed graph whose edges represent direct
dependence relationships.
Context Nodes
Input Nodes
Resident Nodes
RIN
Bayesian Attributes, Objects and Domain
Objects
 RIN Structure


The Site Profiler domain objects combine to describe risk
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Assets and Threats combine to form Targets
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When targets created from Threat-Asset pair an instance of
RIN is created
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Mfrag for Assets: how critical the asset is to the organization,
how desirable to enemy and how soft accessible it is
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Mfrag for Threats: how plausible the tactic and weapon are,
intent of an actor to target, the asset types most likely to
target

These Risk Elements combine to form the key Nodes for
Target: Likelihood of an event, Susceptibility of an asset to an
event, the consequences of the event and ultimately risk of
the event
Conclusion
Site Profiler Knowledge-base is essential
decision support for assessing terrorist
threats
 BN approaches not found to be selling
point
 Many people ask wrong questions
 Power of BN comes from ability to ask:
What are the factors that make risk high
or low?
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