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Simulation And Analysis Of Cascading Failure In Critical Infrastructure
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Simulation And Analysis Of Cascading Failure In Critical Infrastructure

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  • What I will introduce you to today is some of our work being done within the Advanced Modeling and Techniques Investigations Task (AMTI), one of NISAC’s long-term investments in understanding critical infrastructures and their interdependencies (Glass et al., 2003). Our mandate is to identify theories, methods, and analytical tools from the study of general complex adaptive systems that are useful for understanding the structure, function, and evolution of complex interdependent critical infrastructures . In one of AMTI’s efforts, we are focusing on cascading failure as can occur with devastating results within and between infrastructures. NISAC: National and economic security and the quality of life in the U.S. depend on reliable operation of complex infrastructures. The National Infrastructure Simulation and Analysis Center , or NISAC , provides modeling and simulation capabilities for analyzing critical infrastructures. NISAC was founded by Congress in the late 90’s as a joint effort between Sandia and Los Alamos National Laboratories. When DHS was formed NISAC moved over to DHS/IAIP where it has continued its efforts. NISAC analyses focus on the such things as projecting the consequences of disruptions in infrastructure services and changes in security policy (power outages, hurricanes, floods, terrorist attacks, security measures, etc.). NISAC combines simulation of the various infrastructures with perturbations (natural and anthropogenic) along with disease and economic models to evaluate consequences to public health, economics of the region, their distribution and duration. A major focus of NISAC is understanding interdependencies , quantifying their effects and identifying effective strategies for reducing the potential consequences. We are focused on how and when a perturbation spills over or cascades from one infrastructure to another. We use coupled network models, agent-based simulation tools and system dynamics models with feedbacks within and between infrastructures to try to model and understand this process, evaluate consequences, and ultimately suggest mitigation strategies that minimize the compounded effects. Of course, there’s a lot of integration that you have to do to play this game. References: Glass, R.J., W.E. Beyeler, S.H. Conrad, N.S. Brodsky, P.G. Kaplan, T.J. Brown, Defining research and development directions for modeling and simulation of complex, interdependent adaptive infrastructures, 32 pages (SNL paper SAND 2003-1778P).
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    • 1. Simulation and Analysis of Cascading Failure in Critical Infrastructure Robert Glass 1 , Walt Beyeler 1 , Kimmo Soram ä ki 2 , Morten Bech 3 and Jeffrey Arnold 3 1 Sandia National Laboratories 2 European Central Bank 3 Federal Reserve Bank of New York
    • 2. First Stylized Fact: Multi-component Systems often have power-laws & “heavy tails” log(Size) log(Frequency) “ Big” events are not rare in many such systems Earthquakes: Guthenburg-Richter Wars, Epidemics, Cities Power Blackouts Telecom outages Extinctions, Forest fires Traffic jams, Stock crashes “heavy tail” region
    • 3. Power Law - Critical behavior – Phase transitions Temperature Correlation T c External Drive What keeps a non-equilibrium system at a phase boundary? Equilibrium systems: e.g., Magnets & the Curie point Dissipation
    • 4. 1987 Bak, Tang, Wiesenfeld’s “Sand-pile” or “Cascade” Model Lattice “ Self-Organized Criticality” power-laws fractals in space and time Drive Cascade from Local Rules Relaxation
    • 5. Illustrations of natural and constructed network systems from Strogatz [2001]. Food Web New York state’s Power Grid Molecular Interaction Second Stylized Fact: Networks are Ubiquitous
    • 6. Special properties of the “Scale-free” network tolerant to random failure Properties: vulnerable to informed attack… Hierarchical with “King-pin” nodes Power-law degree distribution
    • 7. Network Adapt & Rewire PolyNet Built in Repast Our Conceptual Approach: Rules ON Networks for Bottoms up Simulation of Infrastructures Nodes Links Other Networks Drive Dissipation Actors Tailored Interaction Rules
    • 8. Stylized Physical Infrastructure Applications: High Voltage Electric Power Grids Payment and Banking Systems Epidemics Self-organized Terrorist/Extremist groups Stylized Social Applications: Social/Report Network Evolution Where we are headed: Combined Physical-Human “Infrastructure” Systems Information Networks Crisis and recovery from WMD & Bio attacks Physical + SCADA + Market + Policy Forcing Development & Applications Abstract Studies
    • 9. BTW sand-pile on varied topology Random sinks Sand-pile rules and drive 10,000 nodes Fish-net or Donut Scale-free time size log(size) log(freq)
    • 10. Cascading Blackouts Sources, sinks, relay stations, 400 nodes DC circuit analogy, load, safety factors Random transactions between sources and sinks Fish-net or Donut Scale-free time size Scale-free time size Fish-net
    • 11. Cascading Liquidity Loss within Payment Systems banks payments Trading Day 0 Opening balance adapts to control risk 0 Pay Training Period Cascading Period Balance Time Balance 0 Balance Time
    • 12. Cascading Liquidity in Scale-free Network Patterned Transactions time bank defaults Random removal vs Attack of the Highest Degree node time liquidity
    • 13. Cascading Infectious Diseases Parameters can change when Symptomatic Everyone me Nuclear Family me Classes (There are 6 of these) me Extended Family me Teen Extra me Kids Teens Adults Seniors Agent classes
      • Infectivity
      • Mortality
      • Immunity
      • Etc.
      Class Specific Parameters Teen Laura Glass’s Groups Links & Frequency
    • 14. Without Immunity With Immunity & Mortality Behavioral Changes when Symptomatic Agent differentiation Influenza Epidemic in Structured Village of 10,000: Increasing Realism Structure: Heterogeneous Network + Like with Like
    • 15. Flu Epidemic Mitigation: Vaccination Strategies <60% required Network Structure + Physics of Transmission Process Allows Effective Mitigation Design Kids & Teens! Current policy Seniors only (yellow)
    • 16. General Remarks: Developmental directions Generalization/Abstraction Detailed applications with Domain experts Concepts from Complexity Science are valuable and allow a simulation approach for critical infrastructures that is flexible and has wide ranging applications Focus on POLICY Tools/Insight for Rapid deployment Encapsulation/Integration -NABLE -BOF simulator