Modular Norm Models: A Lightweight Approach for Modeling and Reasoning about Legal Compliance

Mandal, Sayonnha; Gandhi, Robin A.; Siy, Harvey

doi:10.1109/dasc-picom-datacom-cyberscitec.2017.115

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…The powerful ML techniques of deep learning, already well established in other areas, seem of no relevance to researchers looking into standards mapping. This is even more surprising when considering that research into harmonising laws and regulationsarguably very similar to harmonising security standardsuses NLP approaches as, for example, in [68], [69], and [70]. There are, however, two papers listed in Table 4 that use NLP to (semi-) automate the standards and regulations comparison process authored by Winter et al [65] and Bartolini et al [43].…”

Section: Natural Language Processingmentioning

confidence: 99%

Mapping the State of Security Standards Mappings

Mussmann¹,

Brunner²,

Breu³

2020

WI2020 Zentrale Tracks

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Companies often have to comply with more than one security standard and refine parts of security standards to apply to their domain and specific security goals. To understand which requirements different security standards stipulate, a systematic overview or mapping of the relevant natural language security standards is necessary. Creating such standards mappings is a difficult task; to discover which methodologies and tools researchers and practitioners propose and use to map security standards, we conducted a systematic literature review. We identified 44 resources published between 2004 and 2018 using ACM Digital Library, IEEEXplore, SpringerLink, ScienceDirect, dblp and additional grey literature sources. We found that research focuses either on manual methods or on security ontologies to create security standards mappings. We also observed an increase in scientific publications over the investigated timespan which we attribute to the ISO 27001 standard update in 2013 and the EU GDPR coming into effect in 2018.

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