K. Labunets
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7 records found
1
CYBECO
Supporting cyber-insurance from a behavioural choice perspective
Assessing the effect of learning styles on risk model comprehensibility
A controlled experiment (short paper)
This paper presents the design of an experimental study and plan for the conduction of a live study with the participants of the REFSQ2019 conference. The study aims to evaluate the effect of learning styles on risk model comprehensibility throughout a controlled experiment. We combine the baseline experiment designed and conducted by one of the authors to assess the comprehensibility of graphical and tabular security risk models with the questionnaires proposed by Soloman and Felder to measure learning style of people. This study will contribute to the state-of-the-art by looking into the effect of learning styles on the communication of security requirements to the stakeholders and whether an appropriate modelling notation type would help to improve risk model comprehensibility.
Risk analysis is an essential methodology for cybersecurity as it allows organizations to deal with cyber threats potentially affecting them, prioritize the defense of their assets, and decide what security controls should be implemented. Many risk analysis methods are present in cybersecurity models, compliance frameworks, and international standards. However, most of them employ risk matrices, which suffer shortcomings that may lead to suboptimal resource allocations. We propose a comprehensive framework for cybersecurity risk analysis, covering the presence of both intentional and nonintentional threats and the use of insurance as part of the security portfolio. A simplified case study illustrates the proposed framework, serving as template for more complex problems.