Adversarial Risk Analysis (Overview)

通用安全博弈的计算对抗风险分析

Computational adversarial risk analysis for general security games

本文提出基于双智能体影响图和增强概率模拟的计算对抗风险分析方法，用于高效求解通用单阶段与多阶段安全博弈，并通过虚假信息战案例验证其有效性。

Adversarial classification: An adversarial risk analysis approach

Classification problems in security settings are usually contemplated as confrontations in which one or more adversaries try to fool a classifier to obtain a benefit. Most approaches to such adversarial classification problems have focused on game theoretical ideas with strong underlying common knowledge assumptions, which are actually not realistic in security domains. We provide an alternative framework to such problem based on adversarial risk analysis, which we illustrate with several examples. Computational and implementation issues are discussed.

A Bayesian-network-based cybersecurity adversarial risk analysis framework with numerical examples

Cybersecurity risk analysis plays an essential role in supporting organizations make effective decision about how to manage and control cybersecurity risk. Cybersecurity risk is a function of the interplay between the defender, i.e., the organisation, and the attacker: decisions and actions made by the defender second guess the decisions and actions taken by the attacker and vice versa. Insight into this game between these two agents provides a means for the defender to identify and make optimal decisions. To date, the adversarial risk analysis framework has provided a decision-analytical appr…

基于对抗风险分析的个性化定价决策

Personalized Pricing Decisions Through Adversarial Risk Analysis

本文提出基于对抗风险分析的个性化定价框架，通过缓解共同知识假设、预测竞争对手定价与客户购买决策，并结合零售与养老基金案例，辅助企业在竞争不确定性中制定定价策略。

对抗性驾驶行为生成：融入人类风险认知的自动驾驶车辆评估

Adversarial Driving Behavior Generation Incorporating Human Risk Cognition for Autonomous Vehicle Evaluation

本文提出一种结合累积前景理论与强化学习的框架，生成背景车辆对抗行为以评估自动驾驶车辆，并在硬件在环平台上验证其有效性。

SAAC: Safe Reinforcement Learning as an Adversarial Game of Actor-Critics

Although Reinforcement Learning (RL) is effective for sequential decision-making problems under uncertainty, it still fails to thrive in real-world systems where risk or safety is a binding constraint. In this paper, we formulate the RL problem with safety constraints as a non-zero-sum game. While deployed with maximum entropy RL, this formulation leads to a safe adversarially guided soft actor-critic framework, called SAAC. In SAAC, the adversary aims to break the safety constraint while the RL agent aims to maximize the constrained value function given the adversary's policy. The safety cons…

A General Retraining Framework for Scalable Adversarial Classification

Traditional classification algorithms assume that training and test data come from similar distributions. This assumption is violated in adversarial settings, where malicious actors modify instances to evade detection. A number of custom methods have been developed for both adversarial evasion attacks and robust learning. We propose the first systematic and general-purpose retraining framework which can: a) boost robustness of an \emph{arbitrary} learning algorithm, in the face of b) a broader class of adversarial models than any prior methods. We show that, under natural conditions, the retra…