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本文引用的文献

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Behavioral Determinants of Target Shifting and Deterrence in an Analog Cyber-Attack Game.行为决定因素在模拟网络攻击游戏中的目标转移和威慑。
Risk Anal. 2020 Mar;40(3):476-493. doi: 10.1111/risa.13402. Epub 2019 Sep 17.
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A Study on a Sequential One-Defender-N-Attacker Game.关于一种连续的一防御者对N攻击者博弈的研究。
Risk Anal. 2019 Jun;39(6):1414-1432. doi: 10.1111/risa.13257. Epub 2019 Jan 2.
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Deterrence and Risk Preferences in Sequential Attacker-Defender Games with Continuous Efforts.连续努力的序贯攻防博弈中的威慑与风险偏好
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Defender-Attacker Decision Tree Analysis to Combat Terrorism.防御方-攻击方决策树分析在反恐中的应用。
Risk Anal. 2016 Dec;36(12):2258-2271. doi: 10.1111/risa.12574. Epub 2016 Apr 1.
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Attacker Deterrence and Perceived Risk in a Stackelberg Security Game.
Risk Anal. 2016 Aug;36(8):1666-81. doi: 10.1111/risa.12547. Epub 2016 Feb 17.
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Risk Anal. 2011 Sep;31(9):1488-510. doi: 10.1111/j.1539-6924.2011.01590.x. Epub 2011 Mar 18.
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Risk Anal. 2010 Dec;30(12):1803-16. doi: 10.1111/j.1539-6924.2010.01472.x. Epub 2010 Aug 19.
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10
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Risk Anal. 2010 Jan;30(1):32-48. doi: 10.1111/j.1539-6924.2009.01319.x. Epub 2009 Dec 11.

一种威慑价值评估框架。

A Framework for Estimating the Value of Deterrence.

作者信息

John Richard S, Dillon Robin, Burns William, Scurich Nicholas

机构信息

University of Southern California, Los Angeles, California, USA.

Georgetown University, Washington, D.C., USA.

出版信息

Int Top Meet Probab Saf Assess Anal PSA 2019 Charlest SC April 28 May 3 2019 (2019). 2022;16:1-10.

PMID:40919075
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12409732/
Abstract

This paper presents a framework for calculating the value of deterrence related to countermeasures implemented to mitigate an attack by an adaptive adversary. We offer a methodology for adapting Defender-Attacker Decision Trees to partition the utility of countermeasures into three components: (1) threat reduction (deterrence), (2) vulnerability reduction, and (3) consequence mitigation. The Expected Utility of Imperfect Control (EUIC) attributable to a specific implementation of the countermeasure is based on calculations from decision analysis and is defined as the difference in the expected utilities of the no countermeasure branch and the branch representing the countermeasure variant. The EUIC represents the net benefit of implementing the countermeasure, including all costs associated with development, implementation, and operation. Benefits primarily derive from three sources: (1) changes in attack probability (threat reduction), (2) changes in detection probability (vulnerability reduction), and (3) changes in the distribution of attack outcomes (consequence mitigation). We partition the EUIC and estimate the unique portion attributable to threat reduction, vulnerability reduction, and consequence mitigation. Calculations follow a subtraction logic, similar to those used to calculate the Value of Information (VOI). We provide example applications of the Value of Deterrence in an airport security domain. The proposed framework provides a methodology for explicitly accounting for deterrence in benefit-cost analyses.

摘要

本文提出了一个框架,用于计算与为减轻适应性对手的攻击而实施的对策相关的威慑价值。我们提供了一种方法,通过调整防御者-攻击者决策树,将对策的效用划分为三个组成部分:(1)威胁降低(威慑)、(2)漏洞降低和(3)后果减轻。归因于对策特定实施的不完全控制预期效用(EUIC)基于决策分析的计算,并定义为无对策分支与代表对策变体的分支的预期效用之差。EUIC代表实施对策的净收益,包括与开发、实施和运营相关的所有成本。收益主要来自三个来源:(1)攻击概率的变化(威胁降低)、(2)检测概率的变化(漏洞降低)和(3)攻击结果分布的变化(后果减轻)。我们对EUIC进行划分,并估计归因于威胁降低、漏洞降低和后果减轻的独特部分。计算遵循减法逻辑,类似于用于计算信息价值(VOI)的逻辑。我们提供了威慑价值在机场安全领域的示例应用。所提出的框架提供了一种在收益成本分析中明确考虑威慑的方法。