Evidence map›Paper›PMID 32824471›Full record

ArticleSensors (Basel, Switzerland)2020

ARES: Automated Risk Estimation in Smart Sensor Environments.

Athanasios Dimitriadis, Jose Luis Flores, Boonserm Kulvatunyou, Nenad Ivezic, Ioannis Mavridis

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. 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. Sensors Cybersecurity.Sensors (Basel, Switzerland) · 2021
    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

5 authors.

Athanasios DimitriadisDepartment of Applied Informatics, University of Macedonia, 156 Egnatia Str., 54636 Thessaloniki, Greece.
Jose Luis FloresIndustrial Cybersecurity, IKERLAN Technology Research Center, Basque Research and Technology Alliance (BRTA), P.J.M. Arizmendiarrieta 2, 20500 Arrasate/Mondragón, Spain.
Boonserm KulvatunyouSystem Integration Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, MD 20899, USA.
Nenad IvezicSystem Integration Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, MD 20899, USA.
Ioannis MavridisDepartment of Applied Informatics, University of Macedonia, 156 Egnatia Str., 54636 Thessaloniki, Greece.ORCID 0000-0001-8724-6801

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Industry 4.0 adoption demands integrability, interoperability, composability, and security. Currently, integrability, interoperability and composability are addressed by next-generation approaches for enterprise systems integration such as model-based standards, ontology, business process model life cycle management and the context of business processes. Security is addressed by conducting risk management as a first step. Nevertheless, security risks are very much influenced by the assets that the business processes are supported. To this end, this paper proposes an approach for automated risk estimation in smart sensor environments, called ARES, which integrates with the business process model life cycle management. To do so, ARES utilizes standards for platform, vulnerability, weakness, and attack pattern enumeration in conjunction with a well-known vulnerability scoring system. The applicability of ARES is demonstrated with an application example that concerns a typical case of a microSCADA controller and a prototype tool called Business Process Cataloging and Classification System. Moreover, a computer-aided procedure for mapping attack patterns-to-platforms is proposed, and evaluation results are discussed revealing few limitations.

Indexed as

business process contextCommon Security Standardsinformation system risk assessmentsmart sensor environments

Identifiers

PMID32824471
PMCPMC7472351

What OpenQuestion holds

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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.