Implementation of the Common Vulnerability Scoring System to Assess the Cyber Vulnerability in Construction Projects
Bibliographic record
Abstract
The utilization of new technologies coupled with the digitization and automation of the construction industry (known as Construction 4.0) comes with many advantages.For example, it will make the Architecture Engineering and Construction (AEC) industry more connected, accessible, and transparent.However, the inherent nature of these connected systems will make construction networks more vulnerable and prone to cyberattacks.That will compromise not only the confidentiality of sensitive information but also the security of physical assets and project participants.With this background in mind, it is crucial to measure the security of construction networks.There are different systems to evaluate security vulnerabilities of a system, network, organization, or process; one of the most common is the Common Vulnerability Scoring System (CVSS), which provides a numerical score that reflects the severity of a given vulnerability based on specific identified metrics.This paper examines the application of CVSS to quantify and evaluate the vulnerability of project participants that can be used as the groundwork to determine the security vulnerability of construction networks.The objectives of this paper are 1) to examine the advantages and disadvantages of different scoring systems and their applicability to the AEC industry, 2) to systematically apply the identified system to determine scores for some of the most significant construction participants such as the owner, contractor, and worker.The proposed approach will help to assess the vulnerability of project participants and, eventually, the security level of construction networks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".