Evidence map›Paper›PMID 32471252›Full record

ArticleSensors (Basel, Switzerland)2020

Teaching and Learning IoT Cybersecurity andVulnerability Assessment with Shodan through Practical Use Cases.

Tiago M Fernández-Caramés, Paula Fraga-Lamas

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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
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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

2 authors.

Tiago M Fernández-CaramésDepartment of Computer Engineering, Faculty of Computer Science, Universidade da Coruña, 15071 A Coruña, Spain.ORCID 0000-0003-2179-5917
Paula Fraga-LamasDepartment of Computer Engineering, Faculty of Computer Science, Universidade da Coruña, 15071 A Coruña, Spain.ORCID 0000-0002-4991-6808

Funding

Agencia Estatal de Investigación of Spain and ERDF funds of the EU (AEI/FEDER, UE) TEC2016-75067-C4-1-R, RED2018-102668-T, PID2019-104958RB-C42Xunta de Galicia ED431G2019/01
6 · The paper itself

Abstract

Shodan is a search engine for exploring the Internet and thus finding connected devices. Its main use is to provide a tool for cybersecurity researchers and developers to detect vulnerable Internet-connected devices without scanning them directly. Due to its features, Shodan can be used for performing cybersecurity audits on Internet of Things (IoT) systems and devices used in applications that require to be connected to the Internet. The tool allows for detecting IoT device vulnerabilities that are related to two common cybersecurity problems in IoT: the implementation of weak security mechanisms and the lack of a proper security configuration. To tackle these issues, this article describes how Shodan can be used to perform audits and thus detect potential IoT-device vulnerabilities. For such a purpose, a use case-based methodology is proposed to teach students and users to carry out such audits and then make more secure the detected exploitable IoT devices. Moreover, this work details how to automate IoT-device vulnerability assessments through Shodan scripts. Thus, this article provides an introductory practical guide to IoT cybersecurity assessment and exploitation with Shodan.

Indexed as

cyber-attackscybersecurityIoTsecurity auditShodanteaching methodologyuse case based learningvulnerabilitiesvulnerability assessment

Identifiers

PMID32471252
PMCPMC7309102

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.