ArticlePeerJ. Computer science2023
Machine learning and deep learning approaches in IoT.
Article in PeerJ. Computer science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- AI augmented edge and fog computing for Internet of Health Things (IoHT).PeerJ. Computer science · 2025Article
- DeepCorr: a novel error correction method for 3GS long reads based on deep learning.PeerJ. Computer science · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The internet is a booming sector for exchanging information because of all the gadgets in today's world. Attacks on Internet of Things (IoT) devices are alarming as these devices evolve. The two primary areas of the IoT that should be secure in terms of authentication, authorization, and data privacy are the IoMT (Internet of Medical Things) and the IoV (Internet of Vehicles). IoMT and IoV devices monitor real-time healthcare and traffic trends to protect an individual's life. With the proliferation of these devices comes a rise in security assaults and threats, necessitating the deployment of an IPS (intrusion prevention system) for these systems. As a result, machine learning and deep learning technologies are utilized to identify and control security in IoMT and IoV devices. This research study aims to investigate the research fields of current IoT security research trends. Papers about the domain were searched, and the top 50 papers were selected. In addition, research objectives are specified concerning the problem, which leads to research questions. After evaluating the associated research, data is retrieved from digital archives. Furthermore, based on the findings of this SLR, a taxonomy of IoT subdomains has been given. This article also identifies the difficult areas and suggests ideas for further research in the IoT.
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Registered trials
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.