Cyber Security: Effects of Penalizing Defenders in Cyber-Security Games via Experimentation and Computational Modeling

Frontiers in Psychology

article

Zahid Maqbool¹  Palvi Aggarwal²  V. S. Chandrasekhar Pammi³  Varun Dutt¹ 

[1] Applied Cognitive Science Laboratory, Indian Institute of Technology Mandi;Dynamic Decision Making Laboratory, Carnegie Mellon University, United States;Centre of Behavioural and Cognitive Sciences, University of Allahabad

关键词: monetary penalties;    defenders;    adversaries;    cybersecurity;    decision-making;    instance-based learning theory;    recency;    frequency;   

DOI  :  10.3389/fpsyg.2020.00011

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

Cyber-attacks are deliberate attempts by adversaries to illegally access online information of other individuals or organizations. There are likely to be severe monetary consequences for organizations and its workers who face cyber-attacks. However, currently, little is known on how monetary consequences of cyber-attacks may influence the decision-making of defenders and adversaries. In this research, using a cyber-security game, we evaluate the influence of monetary penalties on decisions made by people performing in the roles of human defenders and adversaries via experimentation and computational modeling. In a laboratory experiment, participants were randomly assigned to the role of “hackers” (adversaries) or “analysts” (defenders) in a laboratory experiment across three between-subject conditions: Equal payoffs (EQP), penalizing defenders for false alarms (PDF) and penalizing defenders for misses (PDM). The PDF and PDM conditions were 10-times costlier for defender participants compared to the EQP condition, which served as a baseline. Results revealed an increase (decrease) and decrease (increase) in attack (defend) actions in the PDF and PDM conditions, respectively. Also, both attack-and-defend decisions deviated from Nash equilibriums. To understand the reasons for our results, we calibrated a model based on Instance-Based Learning Theory (IBLT) theory to the attack-and-defend decisions collected in the experiment. The model’s parameters revealed an excessive reliance on recency, frequency, and variability mechanisms by both defenders and adversaries. We discuss the implications of our results to different cyber-attack situations where defenders are penalized for their misses and false-alarms.

【 授权许可】

CC BY