Evidence map›Paper›PMID 38983228›Full record

ArticlePeerJ. Computer science2024

CyberEduPlatform: an educational tool to improve cybersecurity through anomaly detection with Artificial Intelligence.

Iván Ortiz-Garcés, Jaime Govea, Santiago Sánchez-Viteri, William Villegas-Ch

Abstract read
In one paragraph

Article in PeerJ. Computer science, 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

4 authors.

Iván Ortiz-GarcésEscuela de Ingeniería en Ciberseguridad, Facultad de Ingenierías y Ciencias Aplicadas, Universidad de Las Américas, Quito, Pichincha, Ecuador.
Jaime GoveaEscuela de Ingeniería en Ciberseguridad, Facultad de Ingenierías y Ciencias Aplicadas, Universidad de Las Américas, Quito, Pichincha, Ecuador.
Santiago Sánchez-ViteriDepartamento de Sistemas, Universidad Internacional del Ecuador, Universidad Internacional del Ecuador, Quito, Pichincha, Ecuador.
William Villegas-ChEscuela de Ingeniería en Ciberseguridad, Facultad de Ingenierías y Ciencias Aplicadas, Universidad de Las Américas, Quito, Pichincha, Ecuador.ORCID 0000-0002-5421-7710

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cybersecurity has become a central concern in the contemporary digital era due to the exponential increase in cyber threats. These threats, ranging from simple malware to advanced persistent attacks, put individuals and organizations at risk. This study explores the potential of artificial intelligence to detect anomalies in network traffic in a university environment. The effectiveness of automatic detection of unconventional activities was evaluated through extensive simulations and advanced artificial intelligence models. In addition, the importance of cybersecurity awareness and education is highlighted, introducing CyberEduPlatform, a tool designed to improve users' cyber awareness. The results indicate that, while AI models show high precision in detecting anomalies, complementary education and awareness play a crucial role in fortifying the first lines of defense against cyber threats. This research highlights the need for an integrated approach to cybersecurity, combining advanced technological solutions with robust educational strategies.

Indexed as

Artificial intelligence in cybersecurityCybersecurity awareness and educationNetwork anomaly detection

Identifiers

PMID38983228
PMCPMC11232618

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.