Accident Precursor Analysis and Management Reducing Technological Risk Through Diligence (2004) / Chapter Skim
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12 Ensuring Robust Military Operations and Combating Terrorism Using Accident Precursor Concepts
12 Ensuring Robust Military Operations and Combating Terrorism Using Accident Precursor Concepts
Pages 155-174
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From page 155... ...
) requires multiperspective modeling that can identify all conceivable sources of risk and all viable risk management options.
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From page 156... ...
introduced hierarchical holographic modeling (HHM)
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From page 157... ...
defines risk as a measure of the probability and severity of adverse effects. If we look for the common denominator among all "accidents," including natural hazards, terrorist attacks, and human, organizational, hardware, and software failures, we fend that all of them can be anticipated.
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From page 158... ...
As another example, the United States is vulnerable to terrorist attacks because it has an open and free society, long borders, accessible modes of communication and transportation, and a democratic system of government. Today, these long-established state variables render it vulnerable to terronsm.
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From page 159... ...
The geopolitical situation and the subject country must be carefully analyzed to support critical mitial decisions, such as the nature and extent of operations and the timely marshaling of appropriate resources. Relenmt details must be screened and carefully considered to minimize regrettable decisions, as well as wasted resources.
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From page 160... ...
The US HHM also provides supply-side information, helping decision makers marshal supplies for an OOTW. The Defense infrastructure sub Tenon documents equipment, assets, and options the United stares can offer to all OOTW.
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The Coordination HHM identifies critical user-objective spaces with predictable information needs, including staff function, policy horizon, outcome valuation, and three decision-makmg levels: strategic, operational, and tactical. Users at each decision-makmg level want mswers to specific questions pertaining to Countrv HHM subtopics that facilitate the identification of critical information for each decision maker.
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From page 164... ...
The comparison shows that the prevalence of AIDS, hepatitis A and E, and typhoid fever is higher in Serbia than in Croatia. Risk of a Cyberattack on a Water Utility Supervisory Control and Data Acquisition Systems Water systems are increasingly monitored, controlled, and operated remotely through supervisory control and data acquisition (SCADA)
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From page 165... ...
, B7 satellite, B8 alarms, and By sensors. Dependmg on the tools and skills of m attacker, hardware elements could have a signifLcimt impact on water flow for a community.
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In this case study, we are interested in the common-unconditional expected value of risk, denoted by Is and m the conditional expected value of risk of extreme events dOw probability/high consequences) denoted by f4.
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The SCADA system uses a master-slave relationship, relying on the total control of the SCADA master; the remote terminal units are dumb. There are two tanks and two pumping stations as shown in Figure 4.
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The pumping stations receive a start command from the SCADA master via the master terminal unit (MTU) and attempt to start the duty pump.
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; mst uctions to the SCADA system are also encapsulated with TCP/IP. Once mst uctions are received by the LAN, the SCADA master de-encapsulates TCP/IP, leaving the proprietary terminal emulation protocols for the SCADA system.
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Thus, the conditional expected value for this new region is 99.5 percent. Using Equations I and 2, five expected values of risk E(x)
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EVSURIVG ROB UST MIII TARY OPERATIOVS A VD COMBATIVG TERRORISM 171 Initiatmg OS P W SCADA OS SCADA Analog Operator Probability Consequenoes: Event Pmtects P W Proteots Alarms Fail-Safes Nodfied Padr Water-flow Re UChO /o Yes 0.05 0 05 N Yes 0.05 0.0475 NoBe Cybe Yes 0.05415 Small U-(O 5°~)
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From page 172... ...
the model is not dynamic, so it does not completely represent changes in the system during a cyberattack; and (3) tlte event tree produces a probability mass function that must be converted to a density function for the exceedance probability to be partitioned.
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From page 173... ...
Risk Fi tering, RaAing and Managemem Frame wodk Using Hietarchical Hologmphic Modeling. Risk Analysis 22(2)
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