With the worldwide COVID-19 pandemic in 2020 and 2021 necessitating working from home, corporate Virtual Private Networks (VPNs) have become an important item securing the continued operation of companies around the globe. However, due to their different use case, corporate VPNs and how users interact with them differ from public VPNs, which are now commonly used by end-users. In this paper, we present a first explorative study of eleven experts' and seven non-experts' mental models in the context of corporate VPNs. We find a partial alignment of these models in the high-level technical understanding while diverging in important parameters of how, when, and why VPNs are being used. While, in general, experts have a deeper technical understanding of VPN technology, we also observe that even they sometimes hold false beliefs on security aspects of VPNs. In summary, we show that the mental models of corporate VPNs differ from those for related security technology, e.g., HTTPS. Our findings allow us to draft recommendations for practitioners to encourage a secure use of VPN technology (through training interventions, better communication, and system design changes in terms of device management). Furthermore, we identify avenues for future research, e.g., into experts' knowledge and balancing privacy and security between system operators and users.@en

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.

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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.

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[Background] Industry relies on the use of tabular notations to document the risk assessment results, while academia encourages to use graphical notations. Previous studies revealed that tabular and graphical notations with textual labels provide better support for extracting correct information about security risks in comparison to iconic graphical notation. [Aim] In this study we examine how well tabular and graphical risk modeling notations support extraction and memorization of information about risks when models cannot be searched. [Method] We present results of two experiments with 60 MSc and 31 BSc students where we compared their performance in extraction and memorization of security risk models in tabular, UML-style and iconic graphical modeling notations. [Result] Once search is restricted, tabular notation demonstrates results similar to the iconic graphical notation in information extraction. In memorization task tabular and graphical notations showed equivalent results, but it is statistically significant only between two graphical notations. [Conclusion] Three notations provide similar support to decision-makers when they need to extract and remember correct information about security risks.@en

[Context] The CVSS framework provides several dimensions to score vulnerabilities. The environmental metrics allow security analysts to downgrade or upgrade vulnerability scores based on a company’s computing environments and security requirements. [Question] How difficult is for a human assessor to change the CVSS environmental score due to changes in security requirements (let alone technical configurations) for PCI-DSS compliance for networks and systems vulnerabilities of different type? [Results] A controlled experiment with 29 MSc students shows that given a segmented network it is significantly more difficult to apply the CVSS scoring guidelines on security requirements with respect to a flat network layout, both before and after the network has been changed to meet the PCI-DSS security requirements. The network configuration also impact the correctness of vulnerabilities assessment at system level but not at application level. [Contribution] This paper is the first attempt to empirically investigate the guidelines for the CVSS environmental metrics. We discuss theoretical and practical key aspects needed to move forward vulnerability assessments for large scale systems.@en

Security risk assessment methods in industry mostly use a tabular notation to represent the assessment results whilst academic works advocate graphical methods. Experiments with MSc students showed that the tabular notation is better than an iconic graphical notation for the comprehension of security risks. [Aim] We investigate whether the availability of textual labels and terse UML-style notation could improve comprehensibility. [Method] We report the results of an online comprehensibility experiment involving 61 professionals with an average of 9 years of working experience, in which we compared the ability to comprehend security risk assessments represented in tabular, UML-style with textual labels, and iconic graphical modeling notations. [Results] Tabular notation are still the most comprehensible notion in both recall and precision. However, the presence of textual labels does improve the precision and recall of participants over iconic graphical models. [Conclusion] Tabular representation better supports extraction of correct information of both simple and complex comprehensibility questions about security risks than the graphical notation but textual labels help.@en