Terrorism Risk Management

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18 Νοε 2013 (πριν από 3 χρόνια και 10 μήνες)

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Terrorism Risk Management


Authors of the Paper:




David C. Daniels



Linwood D.Hudson



Kathryn B. Laskey



Suzanne M. Mahoney



Bryan S. Ware



Edward J. Wright




Book:

Bayesian Networks: Practical Guide Application

Edited By
:

Olivier Pourret

Chapter

: 14:



Introduction



The U.S military defines
Antiterrorism

as the
defensive posture taken against terrorist threats



Antiterrorism

includes



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:





Measure of Adverse Effect:


Monitory Loss


Non monitory such as death, suffering etc


Likelihood of event * Measure of Adverse Effect

Terrorism Risk Management
Methodologies



Risk Mnemonics


Algebraic Expressions of Risk




Fault Trees


Simulations


Risk= Threat *Vulnerability*Consequence

Risk Mnemonics





CARVER :


C
riticality ,
A
ccessibility,
R
ecognizability,
V
ulnerability,
E
ffect and
R
ecoverability


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


乯渠獰散楦楣i


灡p瑩t畬慲u
threats


Labor
-
intensive


乯渠獣慬慢汥a
瑯t浡m礠慳a整猠



D午䅒偐(D敭潧牡灨y,
卵S捥灴楢楬楴i,

䡩獴潲y,
䅣A敳e楢楬楴i 慮a
剥捯杮楺慢楬楴y, ⁐ o硩x楴i
慮a P潰畬慴o潮
)

I湳瑡汬慴楯渠
P污湮敲⁡獳楧湳i
瑨攠獣潲e

fr潭

ㄠ瑯 㔠慮a
瑨敮t瑨攠
points are
summed to
rank potential
targets

Subjective Risk
Assessment

used by US
military to
identify the
assets at
highest risk of
terrorist
attack

None of these

scores are
adjusted based
on the threat,
type of target
or any special
consideration

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


卩S楬慲⁴漠i丠
a灰p潡oh

楮i
expert
judgment but
since threat is
not considered
so not a true
metric of risk

4.

CAPRA (Critical Asset and
Portfolio

Risk Analysis
)

Five Expert
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

Algebraic Expressions of Risk

Other Approaches


Fault Trees:


Assumes a threat baseline and uses decision
paths to evaluate the probabilities and
outcomes of different outcomes
e.g

OCTAVE



Simu
lations:


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.



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



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


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



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



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



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



Assets and Threats combine to form Targets



When targets created from Threat
-
Asset pair an instance of
RIN is created



Mfrag

for Assets: how critical the asset is to the organization,
how desirable to enemy and how soft accessible it is



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?