Security and privacy of internet of medical things: A contemporary review in the age of surveillance, botnets, and adversarial ML
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
ML-based intrusion detection algorithms can be used for identification of adversary actions.
“Moreover, it has been observed that ML-based intrusion detection algorithms can also be used for the identification of adversary actions [32], [33].”
From Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey (2023) · cites this paper as [32]
The citation lists haven’t been fetched yet
References 303 · Cited by 177
- Authors
- Rasool, R. U., Ahmad, H. F., Rafique, W., Qayyum, A., & Qadir, J.
- Affiliations
- Victoria University (AU)
- King Faisal University (SA)
- Université de Montréal (CA)
- Information Technology University (PK)
- Corresponding author
- Qadir, Junaid
- Venue
- Journal of Network and Computer Applications
- Publisher
- Elsevier BV
- Published
- 2022-05
- Volume
- 201
- Pages
- 103332
- ISSN
- 1084-8045 (ISSN-L)
- OpenAlex
- W4210702679
- Semantic Scholar
- fd7d06f3c1e7185f6c67846e4bb09b7a7bcffa2e
- Cited by
- 177
- References
- 303
- Citations by year
- 2026: 39 · 2025: 44 · 2024: 40 · 2023: 39 · 2022: 15
- FWCI
- 14.59
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Network and Computer Applications (publishedVersion)
- Qatar University QSpace (Qatar University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Engineering › Biomedical Engineering › Wireless Body Area Networks
- Keywords
- Internet of Medical Things, IoMT security, attack taxonomy, security protocols, botnets, adversarial learning, surveillance, healthcare cybersecurity, medical device vulnerabilities
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
