Evidence map›Paper›PMID 38785633›Full record

ArticleEntropy (Basel, Switzerland)2024

CCTFv2: Modeling Cyber Competitions.

Basheer Qolomany, Tristan J Calay, Liaquat Hossain, Aos Mulahuwaish, Jacques Bou Abdo

Abstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Basheer QolomanyCyber Systems Department, University of Nebraska at Kearney, Kearney, NE 68849, USA.ORCID 0000-0002-3270-7225
Tristan J CalayDepartment of Computer Science and Information Systems, Saginaw Valley State University, University Center, MI 48710, USA.
Liaquat HossainSchool of Computing, Montclair State University, Montclair, NJ 07043, USA.ORCID 0000-0002-7616-9182
Aos MulahuwaishDepartment of Computer Science and Information Systems, Saginaw Valley State University, University Center, MI 48710, USA.ORCID 0000-0002-2783-297X
Jacques Bou AbdoSchool of Information Technology, University of Cincinnati, Cincinnati, OH 45221, USA.ORCID 0000-0002-3482-9154

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cyber competitions are usually team activities, where team performance not only depends on the members' abilities but also on team collaboration. This seems intuitive, especially given that team formation is a well-studied discipline in competitive sports and project management, but unfortunately, team performance and team formation strategies are rarely studied in the context of cybersecurity and cyber competitions. Since cyber competitions are becoming more prevalent and organized, this gap becomes an opportunity to formalize the study of team performance in the context of cyber competitions. This work follows a cross-validating two-approach methodology. The first is the computational modeling of cyber competitions using Agent-Based Modeling. Team members are modeled, in NetLogo, as collaborating agents competing over a network in a red team/blue team match. Members' abilities, team interaction and network properties are parametrized (inputs), and the match score is reported as output. The second approach is grounded in the literature of team performance (not in the context of cyber competitions), where a theoretical framework is built in accordance with the literature. The results of the first approach are used to build a causal inference model using Structural Equation Modeling. Upon comparing the causal inference model to the theoretical model, they showed high resemblance, and this cross-validated both approaches. Two main findings are deduced: first, the body of literature studying teams remains valid and applicable in the context of cyber competitions. Second, coaches and researchers can test new team strategies computationally and achieve precise performance predictions. The targeted gap used methodology and findings which are novel to the study of cyber competitions.

Indexed as

cyber competitioncybersecuritymachine learningteams

Identifiers

PMID38785633
PMCPMC11119630

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.