Datasets for distributed denial-of-service detection in healthcare internet of things environments.

Mirza Akhi, Ciarán Eising, Lubna Luxmi Dhirani

Abstract read

In one paragraph

Article in Data in brief, 2025. 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

3 authors.

Mirza AkhiDepartment of Electronic and Computer Engineering, University of Limerick, Limerick, V94 T9PX, Ireland.

Ciarán EisingDepartment of Electronic and Computer Engineering, University of Limerick, Limerick, V94 T9PX, Ireland.

Lubna Luxmi DhiraniDepartment of Electronic and Computer Engineering, University of Limerick, Limerick, V94 T9PX, Ireland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The growing number of Internet of Things (IoT) devices in healthcare settings raises critical concerns about security, particularly in defending against Distributed Denial-of-Service (DDoS) attacks. These attacks can cause operational downtime in IoT environments. To mitigate DDoS-based attacks, advanced defense-in-depth strategies and well-labeled datasets are required for Healthcare-IoT (H-IoT), IoT, and other distributed computing contexts. This article presents two labeled datasets,

Indexed as

Anomaly detectionCooja simulatorCybersecurityIoT traffic featuresMachine Learning for cyber defenseNetwork simulationNs-3 simulatorTraffic analysis

Identifiers

PMID41476908

DOI10.1016/j.dib.2025.112222

PMCPMC12750082

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.