Adversarial Risk Analysis (Overview)

2020/07/06 by David Banks, Víctor Gallego, Banks, David +5 · 2 citations
Computer Science · Mathematics · #Advanced Statistical Methods and Models #Adversarial Robustness in Machine Learning #Applications (stat.AP) #Computer Science and Game Theory (cs.GT) #FOS: Computer and information sciences

paper · pdf · doi:10.48550/arxiv.2007.02613

openalex publication_date 2020/07/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Adversarial risk analysis (ARA) is a relatively new area of research that informs decision-making when facing intelligent opponents and uncertain outcomes. It enables an analyst to express her Bayesian beliefs about an opponent's utilities, capabilities, probabilities and the type of strategic calculation that the opponent is using. Within that framework, the analyst then solves the problem from the perspective of the opponent while placing subjective probability distributions on all unknown quantities. This produces a distribution over the actions of the opponent that permits the analyst to maximize her expected utility. This overview covers conceptual, modeling, computational and applied issues in ARA.

Cited by

• Evasion Attacks Against Bayesian Predictive Models
• Computational adversarial risk analysis for general security games

Related

• The Many Faces of Adversarial Risk
• Insider threat modeling: An adversarial risk analysis approach
• Computational adversarial risk analysis for general security games
• Toward a Research Agenda in Adversarial Reasoning: Computational Approaches to Anticipating the Opponent's Intent and Actions
• Adversarial Risk and the Dangers of Evaluating Against Weak Attacks