Terrorism Risk Management - CSE655

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					    Terrorism Risk Management
Book:       Bayesian Networks: Practical Guide Application
Edited By : Olivier Pourret
Chapter : 14:

Authors of the Paper:

•      David C. Daniels
•      Linwood D.Hudson
•      Kathryn B. Laskey
•      Suzanne M. Mahoney
•      Bryan S. Ware
•      Edward J. Wright
   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

    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

 Risk Mnemonics
 Algebraic Expressions of Risk
        Risk= Threat *Vulnerability*Consequence

 Fault Trees
 Simulations
     Risk Mnemonics
S#   Risk Mnemonic                 Approach          Application       Drawbacks
1.   CARVER (Criticality,          Score each        Developed by      •Non specific
     Accessibility,                factor on a ten   US forces         to particular
     Recognizability,              point scale and   during the Viet   threats
     Vulnerability, Effect and     adding the        Nam conflict      •Labor-
     Recoverability)               scores            to optimize       intensive
                                                     targeting of      •Non scalable
                                                     enemy             to many assets
2.    DSHARPP(Demography,  Installation    Subjective Risk             None of these
                           Planner assigns Assessment
      Susceptibility, History,                                         scores are
      Accessibility and    the score from used by US                   adjusted based
                           1 to 5 and
      Recognizability, Proximity           military to                 on the threat,
      CARVER :
      and Population)      then the        identify the                type of target
                           points are      assets at                   or any special
       Criticality , Accessibility, to
                           summed     Recognizability,
                                           highest risk of             consideration
       Vulnerability, Effect and Recoverability
                           rank potential terrorist
                           targets         attack
     Algebraic Expressions of Risk
S#   Risk Mnemonic             Approach         Application      Drawbacks
3.   SNJTK (Special Needs      An asset based    Developed for   •Similar to BN
     Jurisdiction Tool Kit)    risk approach    DHS by Office    approach in
                               that uses        of Domestic      expert
                               Critical Asset   Preparedness     judgment but
                               Factors for                       since threat is
                               evaluation of                     not considered
                               threat-asset                      so not a true
                               scenario                          metric of risk
4.   CAPRA (Critical Asset and Five Expert      Developed by     Though expert
     Portfolio Risk Analysis)  Evaluation       University of    based it is
                               Phases related   Maryland for     unclear how
                               to mission       asset driven     the risk
                               critical         approach         equation was
                               elements         subjected to     derived or
                                                expert           validated
                                                judgment using
Other Approaches
   Fault Trees:
   Assumes a threat baseline and uses decision
    paths to evaluate the probabilities and
    outcomes of different outcomes e.g

 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.

   Methodology employs a knowledge-base
    Bayesian Network construction to
    combine evidence from analytical models,
    simulations, historical data and user
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
   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

   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
 Bayesian Attributes, Objects and Domain
 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

   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
 Site Profiler Knowledge-base is essential
  decision support for assessing terrorist
 BN approaches not found to be selling
 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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