Terrorism Risk Management

1. 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

2. 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.

3. 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

4. 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

5. 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

6. Terrorism Risk Management Methodologies • Risk Mnemonics • Algebraic Expressions of Risk • Fault Trees • Simulations Risk= Threat *Vulnerability*Consequence

7. Risk Mnemonics • CARVER : Criticality , Accessibility, Recognizability, Vulnerability, Effect and Recoverability

8. Algebraic Expressions of Risk

9. Other Approaches • 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

10. 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

11. Why Site Profiler? • Individuality of Risk Scenarios • Intrinsic Uncertainty • Defensible Methodology • Flexibility • Modifiability, maintainability and Extensibility • Customization • Usability • Portfolio management • Tractability

12. 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

13. 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

14. 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

15. 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.

16. Knowledge-base development Concept Definition: • Data Physical and Domain data • MFRagfor 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

17. 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

18. RIN • Bayesian Attributes, Objects and Domain Objects • RIN Structure

19. 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

20. 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?