Evidence map›Paper›PMID 35336299›Full record

ArticleSensors (Basel, Switzerland)2022

A Novel Model for Vulnerability Analysis through Enhanced Directed Graphs and Quantitative Metrics.

Ángel Longueira-Romero, Rosa Iglesias, Jose Luis Flores, Iñaki Garitano

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

4 authors.

Ángel Longueira-RomeroIkerlan Technology Research Centre, Basque Research and Technology Alliance (BRTA), 20500 Arrasate, Spain.ORCID 0000-0001-6085-820X
Rosa IglesiasIkerlan Technology Research Centre, Basque Research and Technology Alliance (BRTA), 20500 Arrasate, Spain.ORCID 0000-0003-1036-3035
Jose Luis FloresIkerlan Technology Research Centre, Basque Research and Technology Alliance (BRTA), 20500 Arrasate, Spain.ORCID 0000-0002-5555-9712
Iñaki GaritanoDepartment of Electronics and Computing, Mondragon Unibertsitatea, 20500 Mondragón, Spain.ORCID 0000-0002-0387-9167

Funding

Centre for Industrial Technological Development CER-20191012
6 · The paper itself

Abstract

The rapid evolution of industrial components, the paradigm of Industry 4.0, and the new connectivity features introduced by 5G technology all increase the likelihood of cybersecurity incidents. Such incidents are caused by the vulnerabilities present in these components. Designing a secure system is critical, but it is also complex, costly, and an extra factor to manage during the lifespan of the component. This paper presents a model to analyze the known vulnerabilities of industrial components over time. The proposed Extended Dependency Graph (EDG) model is based on two main elements: a directed graph representation of the internal structure of the component, and a set of quantitative metrics based on the Common Vulnerability Scoring System (CVSS). The EDG model can be applied throughout the entire lifespan of a device to track vulnerabilities, identify new requirements, root causes, and test cases. It also helps prioritize patching activities. The model was validated by application to the OpenPLC project. The results reveal that most of the vulnerabilities associated with OpenPLC were related to memory buffer operations and were concentrated in the

Indexed as

Computer SecurityProbabilityCAPECCPECVECVSSCWEcybersecuritydirected graphIACSIEC 62443OpenPLCsecurity metricsvulnerability assessment

Identifiers

PMID35336299
PMCPMC8952879

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.