Adversarial Risk Analysis for Urban Security Resource Allocation

Gil, César; Insua, David Rı́os; Ríos, Jesús

doi:10.1111/risa.12580

Cited by 18 publications

(10 citation statements)

References 38 publications

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“…It is now widely used in Bayesian statistics, game and decision theory, data mining and economics (e.g., [59][60][61][62]). More recently, expected utility has been used in Adversarial Risk Analysis models, which model the actions of the strategic adversary and find the optimal actions for the defender (e.g., [63][64][65][66]).…”

Section: Using Expected Utility To Combine Multiple Measuresmentioning

confidence: 99%

Considerations for Developing Predictive Spatial Models of Crime and New Methods for Measuring Their Accuracy

Joshi

Curtis‐Ham²,

D'Ath

et al. 2021

IJGI

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A literature review of the important trends in predictive crime modeling and the existing measures of accuracy was undertaken. It highlighted the need for a robust, comprehensive and independent evaluation and the need to include complementary measures for a more complete assessment. We develop a new measure called the penalized predictive accuracy index (PPAI), propose the use of the expected utility function to combine multiple measures and the use of the average logarithmic score, which measures accuracy differently than existing measures. The measures are illustrated using hypothetical examples. We illustrate how PPAI could identify the best model for a given problem, as well as how the expected utility measure can be used to combine different measures in a way that is the most appropriate for the problem at hand. It is important to develop measures that empower the practitioner with the ability to input the choices and preferences that are most appropriate for the problem at hand and to combine multiple measures. The measures proposed here go some way towards providing this ability. Further development along these lines is needed.

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Section: Using Expected Utility To Combine Multiple Measuresmentioning

confidence: 99%

Considerations for Developing Predictive Spatial Models of Crime and New Methods for Measuring Their Accuracy

Joshi

Curtis‐Ham²,

D'Ath

et al. 2021

IJGI

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show abstract

“…To study the appropriateness of standard game theory and ARA, we develop first the sequential defense–attack model, which is one of the two standard counterterrorism model (Other standard models include the simultaneous defense-attack model, the sequential defense–attack–defense [25] and the sequential defense-attack with private information.). We use this model to analyze the problem that is the objective of this work.…”

Section: Analysis Of Decision-making Modelsmentioning

confidence: 99%

An Adversarial-Risk-Analysis Approach to Counterterrorist Online Surveillance

Gil¹,

Parra-Arnau

2019

Sensors

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The Internet, with the rise of the IoT, is one of the most powerful means of propagating a terrorist threat, and at the same time the perfect environment for deploying ubiquitous online surveillance systems.This paper tackles the problem of online surveillance, which we define as the monitoring by a security agency of a set of websites through tracking and classification of profiles that are potentially suspected of carrying out terrorist attacks. We conduct a theoretical analysis in this scenario that investigates the introduction of automatic classification technology compared to the status quo involving manual investigation of the collected profiles. Our analysis starts examining the suitability of game-theoretic-based models for decision-making in the introduction of this technology. We propose an adversarial-risk-analysis (ARA) model as a novel way of approaching the online surveillance problem that has the advantage of discarding the hypothesis of common knowledge. The proposed model allows us to study the rationality conditions of the automatic suspect detection technology, determining under which circumstances it is better than the traditional human-based approach. Our experimental results show the benefits of the proposed model. Compared to standard game theory, our ARA-based model indicates in general greater prudence in the deployment of the automatic technology and exhibits satisfactory performance without having to relax crucial hypotheses such as common knowledge and therefore subtracting realism from the problem, although at the expense of higher computational complexity.

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“…ARA takes into account the expected utilities for the defender as well as the random expected utilities for the opponents, incorporating uncertainty regarding their strategic reasoning. Since its introduction, it has been used to model a variety of problems such as network routing for insurgency ([Wang and Banks, 2011]), international piracy ([Sevillano et al, 2012]), counter-terrorism ( ), autonomous social agents ( [Esteban and Insua, 2014], urban security resource allocation ( [Gil et al, 2016]), adversarial classification ( [Naveiro et al, 2019]), counter-terrorist online surveillance ( [Gil and Parra-Arnau, 2019]), cyber-security ( [Rios Insua et al, 2019]).…”

Section: Adversarial Risk Analysismentioning

confidence: 99%

Insider threat modeling: An adversarial risk analysis approach

Joshi¹,

Insua²,

Ríos³

2019

Preprint

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Insider threats entail major security issues in geopolitics, cyber risk management and business organization. The game theoretic models proposed so far do not take into account some important factors such as the organisational culture and whether the attacker was detected or not. They also fail to model the defensive mechanisms already put in place by an organisation to mitigate an insider attack. We propose two new models which incorporate these settings and hence are more realistic. We use the adversarial risk analysis (ARA) approach to find the solution to our models. ARA does not assume common knowledge and solves the problem from the point of view of one of the players, taking into account their knowledge and uncertainties regarding the choices available to them, to their adversaries, the possible outcomes, their utilities and their opponents' utilities. Our models and the ARA solutions are general and can be applied to most insider threat scenarios. A data security example illustrates the discussion.

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Adversarial Risk Analysis for Urban Security Resource Allocation

Cited by 18 publications

References 38 publications

Considerations for Developing Predictive Spatial Models of Crime and New Methods for Measuring Their Accuracy

Considerations for Developing Predictive Spatial Models of Crime and New Methods for Measuring Their Accuracy

An Adversarial-Risk-Analysis Approach to Counterterrorist Online Surveillance

Insider threat modeling: An adversarial risk analysis approach

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