Improving security requirements adequacy: an interval type 2 fuzzy logic security assessment system
Organizations rely on security experts to improve the security of their systems. These professionals use background knowledge and experience to align known threats and vulnerabilities before selecting mitigation options. The substantial depth of expertise in any one area (e.g., databases, networks,...
| Main Authors: | , , |
|---|---|
| Format: | Conference or Workshop Item |
| Published: |
2016
|
| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/38651/ |
| _version_ | 1848795660665487360 |
|---|---|
| author | Hibshi, Hanan Breaux, Travis D. Wagner, Christian |
| author_facet | Hibshi, Hanan Breaux, Travis D. Wagner, Christian |
| author_sort | Hibshi, Hanan |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Organizations rely on security experts to improve the security of their systems. These professionals use background knowledge and experience to align known threats and vulnerabilities before selecting mitigation options. The substantial depth of expertise in any one area (e.g., databases, networks, operating systems) precludes the possibility that an expert would have complete knowledge about all threats and vulnerabilities. To begin addressing this problem of fragmented knowledge, we investigate the challenge of developing a security requirements rule base that mimics multi-human expert reasoning to enable new decision-support systems. In this paper, we show how to collect relevant information from cyber security experts to enable the generation of: (1) interval type-2 fuzzy sets that capture intra- and inter-expert uncertainty around vulnerability levels; and (2) fuzzy logic rules driving the decision-making process within the requirements analysis. The proposed method relies on comparative ratings of security requirements in the context of concrete vignettes, providing a novel, interdisciplinary approach to knowledge generation for fuzzy logic systems. The paper presents an initial evaluation of the proposed approach through 52 scenarios with 13 experts to compare their assessments to those of the fuzzy logic decision support system. The results show that the system provides reliable assessments to the security analysts, in particular, generating more conservative assessments in 19% of the test scenarios compared to the experts’ ratings. |
| first_indexed | 2025-11-14T19:35:37Z |
| format | Conference or Workshop Item |
| id | nottingham-38651 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:35:37Z |
| publishDate | 2016 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-386512020-05-04T18:16:56Z https://eprints.nottingham.ac.uk/38651/ Improving security requirements adequacy: an interval type 2 fuzzy logic security assessment system Hibshi, Hanan Breaux, Travis D. Wagner, Christian Organizations rely on security experts to improve the security of their systems. These professionals use background knowledge and experience to align known threats and vulnerabilities before selecting mitigation options. The substantial depth of expertise in any one area (e.g., databases, networks, operating systems) precludes the possibility that an expert would have complete knowledge about all threats and vulnerabilities. To begin addressing this problem of fragmented knowledge, we investigate the challenge of developing a security requirements rule base that mimics multi-human expert reasoning to enable new decision-support systems. In this paper, we show how to collect relevant information from cyber security experts to enable the generation of: (1) interval type-2 fuzzy sets that capture intra- and inter-expert uncertainty around vulnerability levels; and (2) fuzzy logic rules driving the decision-making process within the requirements analysis. The proposed method relies on comparative ratings of security requirements in the context of concrete vignettes, providing a novel, interdisciplinary approach to knowledge generation for fuzzy logic systems. The paper presents an initial evaluation of the proposed approach through 52 scenarios with 13 experts to compare their assessments to those of the fuzzy logic decision support system. The results show that the system provides reliable assessments to the security analysts, in particular, generating more conservative assessments in 19% of the test scenarios compared to the experts’ ratings. 2016-10-15 Conference or Workshop Item PeerReviewed Hibshi, Hanan, Breaux, Travis D. and Wagner, Christian (2016) Improving security requirements adequacy: an interval type 2 fuzzy logic security assessment system. In: IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016), 6-9 December 2016, Athens, Greece. user study; vignettes; scenarios; recommender system; security requirements; fuzzy logic; type-2; uncertainty |
| spellingShingle | user study; vignettes; scenarios; recommender system; security requirements; fuzzy logic; type-2; uncertainty Hibshi, Hanan Breaux, Travis D. Wagner, Christian Improving security requirements adequacy: an interval type 2 fuzzy logic security assessment system |
| title | Improving security requirements adequacy: an interval type 2 fuzzy logic security assessment system |
| title_full | Improving security requirements adequacy: an interval type 2 fuzzy logic security assessment system |
| title_fullStr | Improving security requirements adequacy: an interval type 2 fuzzy logic security assessment system |
| title_full_unstemmed | Improving security requirements adequacy: an interval type 2 fuzzy logic security assessment system |
| title_short | Improving security requirements adequacy: an interval type 2 fuzzy logic security assessment system |
| title_sort | improving security requirements adequacy: an interval type 2 fuzzy logic security assessment system |
| topic | user study; vignettes; scenarios; recommender system; security requirements; fuzzy logic; type-2; uncertainty |
| url | https://eprints.nottingham.ac.uk/38651/ |