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Security under Uncertainty: Adaptive Attackers Are More Challenging to Human Defenders than Random Attackers

机译：不确定性下的安全性：与随机攻击者相比自适应攻击者对防御者的挑战更大

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Game Theory is a common approach used to understand attacker and defender motives, strategies, and allocation of limited security resources. For example, many defense algorithms are based on game-theoretic solutions that conclude that randomization of defense actions assures unpredictability, creating difficulties for a human attacker. However, many game-theoretic solutions often rely on idealized assumptions of decision making that underplay the role of human cognition and information uncertainty. The consequence is that we know little about how effective these algorithms are against human players. Using a simplified security game, we study the type of attack strategy and the uncertainty about an attacker's strategy in a laboratory experiment where participants play the role of defenders against a simulated attacker. Our goal is to compare a human defender's behavior in three levels of uncertainty (Information Level: Certain, Risky, Uncertain) and three types of attacker's strategy (Attacker's strategy: Minimax, Random, Adaptive) in a between-subjects experimental design. Best defense performance is achieved when defenders play against a minimax and a random attack strategy compared to an adaptive strategy. Furthermore, when payoffs are certain, defenders are as efficient against random attack strategy as they are against an adaptive strategy, but when payoffs are uncertain, defenders have most difficulties defending against an adaptive attacker compared to a random attacker. We conclude that given conditions of uncertainty in many security problems, defense algorithms would be more efficient if they are adaptive to the attacker actions, taking advantage of the attacker's human inefficiencies.
机译：博弈论是一种用于了解攻击者和防御者的动机，策略以及有限安全资源分配的常用方法。例如，许多防御算法都基于博弈论解决方案，得出的结论是，防御行动的随机性可确保不可预测性，从而给人类攻击者带来困难。但是，许多博弈论解决方案通常依赖于理想化的决策假设，而这些假设不发挥人类认知和信息不确定性的作用。结果是我们对这些算法对人类玩家的有效性知之甚少。通过使用简化的安全游戏，我们在实验室实验中研究了攻击策略的类型以及攻击者策略的不确定性，在该实验中，参与者扮演着防御者的角色，对抗模拟攻击者。我们的目标是在受试者之间的实验设计中，在三种不确定性级别（信息级别：确定，风险，不确定）和三种攻击者策略（攻击者策略：Minimax，随机，自适应）中比较人类防御者的行为。与防御性策略相比，防御者对抗极小极大值和随机攻击策略可达到最佳防御性能。此外，当确定有收益时，防御者在抵抗随机攻击策略方面和在适应自适应策略方面一样有效，但是，当收益不确定时，与随机攻击者相比，防御者在防御自适应攻击者方面最困难。我们得出的结论是，在许多安全问题中存在不确定性的条件下，如果防御算法能够适应攻击者的行为，并利用攻击者的人为低效率，则它们将更加高效。

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