LiSRA: Lightweight Security Risk Assessment for Decision Support in Information Security

Schmitz, C. and Pape, S.

In Computers & Security, 90, 2020.

Abstract

Information security risk assessment frameworks support decision-makers in assessing and understanding the risks their organisation is exposed to. However, there is a lack of lightweight approaches. Most existing frameworks require security-related information that are not available and that are very challenging to gather. So they are not suitable in practice, especially for small and medium-sized enterprises (SMEs) who often lack in data and in security knowledge. On the other hand, other explicit SME approaches have far less informative value than LiSRA. Moreover, many approaches only provide extensive process descriptions that are challenging for SMEs. In order to overcome this challenge, we propose LiSRA, a lightweight, domain-specific framework to support information security decision making. It is designed with a two-sided input where domain experts initially provide domain-specific information (e.g. attack scenarios for a specific domain), whereupon users can focus on specifying their security practices and organisational characteristics by entering information that many organisations have already collected. This information is then linked to attack paths and to the corresponding adverse impacts in order to finally assess the total risk. Moreover, LiSRA can be used to get transparent recommendations for future security activities and presents detailed insights on the mitigating effects of each recommendation. They are being evaluated taking into account the security activities already in place, and also considering the dependencies between multiple overlapping activities that can be of complementary, substitutive or dependent nature. Both aspects are ignored by most existing evaluation approaches which can lead to an over-investment in security. A prototype has been implemented, and the applicability of the framework has been evaluated with performance and robustness analyses and with initial qualitative evaluations.


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