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REVIEW 3 major objections 6 minor 1 cited by

A Review on the Security Vulnerabilities of the IoMT against Malware Attacks and DDoS

T0 review · 3 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This literature review argues that inadequate encryption, weak authentication, and irregular firmware updates are the primary causes of IoMT security risk, with malware and DDoS as the dominant attack forms.

desk verdict A readable but unverifiable rehash of IoMT security surveys, whose quantitative success-rate claims don't survive contact with the reported method. read the letter →

arxiv 2501.07703 v1 pith:NO6XNZGB submitted 2025-01-13 cs.CR

Lily Dzamesi , Nelly Elsayed This is my paper · ORCID
classification cs.CR
keywords IoMTmedicaldevicesecuritymalwareDDoSattacksencryptionauthenticationfirmwareupdatesmachinelearning
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper is a systematic review of five years of peer-reviewed research on security vulnerabilities in the Internet of Medical Things (IoMT), the network of connected devices used to monitor and treat patients. It sets out to establish that the main causes of IoMT risk are inadequate encryption protocols, weak authentication mechanisms, and irregular firmware updates, and that malware and distributed denial-of-service (DDoS) attacks are the most significant threats exploiting these weaknesses. It further claims that machine learning, blockchain, and edge computing are the most promising mitigation directions, while noting that computational overhead and scalability limit all three. If the review's synthesis is right, then security efforts in healthcare should concentrate on lightweight cryptography, stronger authentication, and dependable update processes before investing in more complex defenses.

What carries the argument

The central object is the Internet of Medical Things (IoMT), the network of connected medical devices and healthcare IT systems. The argument about its security is carried by a systematic literature review: a defined search across three major scholarly databases, inclusion and exclusion criteria (peer-reviewed, 2019–2024, English, focused on IoMT security), quality screening, and categorical extraction of publication type, venue, year, technology, and vulnerability. This machinery turns individual papers into comparative claims by rating mitigation approaches on success rate, complexity, and scalability, and it is what allows the review to assert that blockchain-based solutions achieve the highest reported success rate (90%), machine learning approaches reach 82–88% for detection tasks, and edge computing best fits resource-constrained devices.

What would settle it

A reader could go to the cited studies and verify each headline number: if the sources do not contain the 90% blockchain success rate, the 82–88% machine-learning range, or the over-70% vulnerability figure, the review's quantitative synthesis is unsupported. A complementary test would be a real-world audit of hospital IoMT fleets to see whether exploited vulnerabilities trace to weak encryption, weak authentication, and firmware lag as the review claims.

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Extended reading notes

Core claim

On its own terms, the review's central discovery is that the IoMT vulnerability landscape is dominated by three recurring weaknesses—encryption, authentication, and firmware hygiene—and that the same three defenses keep recurring as solutions. Drawing on 586 peer-reviewed studies collected from three major scholarly databases, it reports that malware and DDoS attacks account for most observed threats, that over 70% of IoMT devices in current use are vulnerable to known malware, and that evaluated mitigations show success rates of about 82–88% for machine learning, 90% for blockchain, and 70–85% for cryptographic methods. The paper presents this as a synthesis of existing evidence rather than a new measurement, so the numbers are claims about what the literature already demonstrates.

Load-bearing premise

The review assumes that the summary statistics it reports—over 70% of IoMT devices vulnerable to known malware, a 90% success rate for blockchain, and 82–88% for machine learning—are accurately drawn from the cited studies, even though it provides no per-study data table, confidence interval, or meta-analytic check.

Editorial extensions

If this is right

  • If weak encryption, weak authentication, and irregular firmware updates are the dominant root causes, then targeted investment in those three areas should reduce the majority of IoMT exploitation.
  • Machine learning-based detection is effective against malware and DDoS but demands computational resources and large datasets, so its deployment depends on lighter models or off-device processing.
  • Blockchain offers tamper-proof data storage and access control, but its high reported success rate comes with computational overhead, making efficiency the key open problem.
  • Edge computing localizes threat detection and reduces latency, making it the most scalable option for resource-constrained medical devices.
  • Standardized security protocols across IoMT ecosystems are a necessary condition for turning any of these mitigations into dependable practice.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper, the three root-cause categories—encryption, authentication, and firmware—could be turned into a minimal audit checklist for hospital IoMT procurement, and a field study could test whether those categories predict observed breaches.
  • The reported success rates are composite numbers without per-study breakdowns or confidence intervals, so treating them as point estimates is premature; a meta-analysis of the cited detection studies would be the natural check.
  • The recurring resource-constraint constraint suggests the durable solutions will be those that shift heavy computation off the device, such as edge-based anomaly detection, rather than on-device cryptography alone.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper claims to be a systematic literature review of IoMT security vulnerabilities related to malware and DDoS attacks, based on 586 papers retrieved from ACM Digital Library, IEEE Xplore, and Elsevier (2019–2024). It concludes that inadequate encryption, weak authentication, and irregular firmware updates are the main causes of IoMT risk, and it identifies machine learning, blockchain, and edge computing as promising mitigations. The paper also reports quantitative mitigation success rates (blockchain 90%, machine learning 82–88%, cryptography 70–85%) and states that over 70% of IoMT devices currently in use are vulnerable to known malware attacks.

Significance. If the quantitative synthesis were properly supported, this review would offer a useful map of mitigation effectiveness for healthcare cybersecurity researchers and practitioners. The paper usefully organizes common vulnerability themes, describes the IoMT architecture, and provides a table of representative papers and their mitigation approaches. However, the claimed systematic methodology and the headline quantitative results are not backed by the reported evidence, so the contribution cannot currently be assessed as a reliable synthesis.

major comments (3)
  1. [Section 3.4, Figure 5] The success-rate analysis (blockchain 90%, machine learning 82–88%, cryptography 70–85%) is the paper's main quantitative result, but no per-study extraction table, no definition of 'success rate,' no confidence intervals, and no aggregation method are provided. The cited works include surveys and protocol papers; it is not shown that these sources report success rates in this form. Without a data table linking each figure to a primary study, the central synthesis is unsupported.
  2. [Section 2, especially §2.3] The method section describes a systematic process with 586 initial papers, inclusion/exclusion criteria, and a quality assessment, but the manuscript reports no screening counts, no PRISMA-style flow diagram, no list of excluded studies, and no quality-assessment outcomes. Table 1 lists only ten papers, yet the text cites 35 references; the reader cannot determine how the final set was reached. Thus the 'systematic review' claim is not demonstrated.
  3. [Section 3.2] The assertion that 'over 70% of IoMT devices currently in use are vulnerable to known malware attacks' is attributed to [32], but no primary measurement, definition of vulnerability, or source data is given. This statistic is load-bearing for the paper's motivation and should be traceable to a specific study or presented as the authors' estimate with appropriate justification.
minor comments (6)
  1. [Section 4] The reference to 'Figure 5 and 5' should be 'Figures 5 and 6.'
  2. [Section 2.4] The statement 'as demonstrated in Figure 2' likely refers to Figure 3 or Figure 4; the figure cross-reference is incorrect.
  3. [Section 2.2] The sentence 'Studies either addressing IoMT security vulnerability nor published outside the designated date range...' is grammatically incomplete and should be revised for clarity.
  4. [Throughout] Capitalization of 'IOMT' is inconsistent; please use 'IoMT' consistently.
  5. [Figure 4 caption] The 'Alt:' text appears inside the figure caption; this appears to be a formatting artifact and should be moved or removed.
  6. [References] Reference [34] is published in a venue described as 'Distributed Learning and Broad Applications in Scientific Research,' which may not meet the stated peer-review inclusion criterion; this should be verified.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is a literature review whose conclusions are summaries of external cited studies; the authors' self-citations are contextual and not load-bearing.

full rationale

This paper is a systematic literature review, not a derivation-based study. It contains no equations, no fitted parameters, and no quantity that is defined in terms of the result it is said to predict. Its central claims—that inadequate encryption, weak authentication, and irregular firmware updates are key vulnerability causes, and that machine learning, blockchain, and edge computing are promising mitigations—are presented as syntheses of the cited literature (e.g., [16], [17], [18], [25], [27], [30], [31]). Quantitative statements such as 'over 70% of IoMT devices currently in use are vulnerable' and the success-rate ranges in Section 3.4 are attributed to external references ([32]; [16], [25], [31]; [27], [30]; [17], [24], [28]). Whether those numbers accurately reflect the cited sources is a correctness or reporting-quality concern, not a circularity concern, because the numbers are not generated by the present paper's own methods. The authors do cite their own prior work ([8], [20], [22]), but these citations support background statements about seizure-detection architecture, botnet-based DDoS, and the CIA triad, respectively; none of these self-citations is load-bearing for the paper's main conclusions. No step in the paper reduces to its own inputs by construction, and no fitted input is renamed as a prediction. Therefore the appropriate circularity score is 0.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

No free parameters or invented entities appear because this is a literature review. The review relies on the accuracy of its cited sources and on the undocumented assumption that the 586-paper search was narrowed to a representative set according to the stated criteria.

assumptions (2)
  • domain assumption The cited primary studies accurately report IoMT vulnerabilities and mitigation effectiveness.
    The review makes no original measurements, so its findings inherit the correctness of its sources.
  • domain assumption The search and screening process produced a representative set of IoMT security literature.
    The paper does not report how many of the 586 papers passed screening or the results of quality assessment, yet it presents the synthesis as systematic.

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Cite this review

Pith. "Pith review of A Review on the Security Vulnerabilities of the IoMT against Malware Attacks and DDoS." pith.science (2026). https://pith.science/paper/NO6XNZGB

@misc{pith2026250107703,
  author       = {Pith},
  title        = {Pith review of: A Review on the Security Vulnerabilities of the IoMT against Malware Attacks and DDoS},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NO6XNZGB}},
  note         = {Machine review of arXiv:2501.07703}
}
read the original abstract

The Internet of Medical Things (IoMT) has transformed the healthcare industry by connecting medical devices in monitoring treatment outcomes of patients. This increased connectivity has resulted to significant security vulnerabilities in the case of malware and Distributed Denial of Service (DDoS) attacks. This literature review examines the vulnerabilities of IoMT devices, focusing on critical threats and exploring mitigation strategies. We conducted a comprehensive search across leading databases such as ACM Digital Library, IEEE Xplore, and Elsevier to analyze peer-reviewed studies published within the last five years (from 2019 to 2024). The review shows that inadequate encryption protocols, weak authentication methods, and irregular firmware updates are the main causes of risks associated with IoMT devices. We have identified emerging solutions like machine learning algorithms, blockchain technology, and edge computing as promising approaches to enhance IoMT security. This review emphasizes the pressing need to develop lightweight security measures and standardized protocols to protect patient data and ensure the integrity of healthcare services.

Figures

Figures reproduced from arXiv: 2501.07703 by the authors.

Figure 1
Figure 1. Examples of IoMT devices. cloud layer, and the application layer. The Perception layer in the Internet of Medical Things (IoMT) architecture carries data from medical devices and sensors, including wearables, implanted devices, and traditional medical equipment [4], [7]–[9]. These devices allow for continuous monitoring of vital signs and communication through protocols such as Bluetooth Low Energy (BLE), Zigbee, an… view at source ↗
Figure 2
Figure 2. The IoMT system architecture layers diagram. [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. The workflow used in our systematic search process for IoMT [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Distribution of paper sources. Alt: The diagram shows the distribution of paper sources that have been used at the systematic review. This categorization allowed us to synthesize the critical risks faced by IoMT devices. This systematic process ensured a thorough liter…
Figure 5
Figure 5. Figure 5: The success rate [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: The complexity and scalability. 3.5. Challenges in Implementing Security Measures Despite the promising strategies, many studies noted challenges in implementing robust security protocols: • High Computational Overhead: Cryptographic tech￾niques like blockchain were fo…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Extreme Learning Machine Based System for DDoS Attacks Detections on IoMT Devices

    cs.CR 2025-07 conditional novelty 3.0 of 10

    An extreme learning machine classifier obtained roughly 95% accuracy detecting DDoS attacks in the CICIoMT2024 IoMT dataset, but the evaluation may leak test information during feature selection and the low-cost claim...

Reference graph

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