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

Frederic Moisan () and Cleotilde Gonzalez
Additional contact information
Frederic Moisan: EM - EMLyon Business School

Post-Print from HAL

Abstract: 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.

Keywords: defense strategies; security games; Game theory; human behavior; Learning (search for similar items in EconPapers)
Date: 2017-06-22
Note: View the original document on HAL open archive server: https://hal.science/hal-03188217v1
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Published in Frontiers in Psychology, 2017, 8, 10 p

Downloads: (external link)
https://hal.science/hal-03188217v1/document (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03188217

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().