Evidence map›Paper›PMID 37220558›Full record

ArticleSN computer science2023

Is COVID-19 Being Used to Spread Malware.

Ruqayah N Ahmed, Amir Javed, Wafi Bedewi

Open access · hybridAbstract read
In one paragraph

Article in SN computer science, 2023. 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, top 92% of its field
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, 0 citations in OpenAlex.

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

3 authors at 2 institutions in 3 countries.

Ruqayah N AhmedComputer Center, University of Samarra, Samarra, Salah Aldin 34010 Iraq.ORCID 0000-0003-1397-1917
Amir JavedSchool of Computer Science and Informatics, Cardiff University, Cathays, Cardiff, CF24 4AG UK.
Wafi BedewiComputer Center, University of Samarra, Samarra, Salah Aldin 34010 Iraq.
University of Samarra · IQKing Abdulaziz University · SA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the rising number of people using social networks after the pandemic of COVID-19, cybercriminals took the advantage of (i) the increased base of possible victims and (ii) the use of a trending topic as the pandemic COVID-19 to lure victims and attract their attention and put malicious content to infect the most possible number of people. Twitter platform forces an auto-shortening to any included URL within a 140-character message called "tweet" and this makes it easier for the attackers to include malicious URLs within Tweets. Here comes the need to adopt new approaches to resolve the problem or at least identify it to better understand it to find a suitable solution. One of the proven effective approaches is the adaption of machine learning (ML) concepts and applying different algorithms to detect, identify, and even block the propagation of malware. Hence, this study's main objectives were to collect tweets from Twitter that are related to the topic of COVID-19 and extract features from these tweets and import them as independent variables for the machine learning models to be developed later, so they would identify imported tweets as to be malicious or not.

Indexed as

AICOVID-19Cyber securityMachine learningMaliciousMalwarePandemicTweetTwitter

Identifiers

PMID37220558
PMCPMC10189705
OpenAlexW4377041760

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.