On the Performance of Cyber-Biomedical Features for Intrusion Detection in Healthcare 5.0

Daniel Welfer; Juliano F. Kazienko; Lucas P. Siqueira; Pedro H. Lui; Silvio E. Quincozes; Vagner E. Quincozes

REVIEW 4 major objections 5 minor 21 references

On the Performance of Cyber-Biomedical Features for Intrusion Detection in Healthcare 5.0

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Biomedical sensor data carry real predictive weight for detecting spoofing attacks in Healthcare 5.0 intrusion detection, with temperature reaching a Shapley magnitude of 0.37.

desk verdict Per-class SHAP on WUSTL-EHMS-2020 is a useful look, but the claim that biomedical features boost spoofing detection is an interpretation, not a result. read the letter →

arxiv 2506.17329 v1 pith:KKR6OBCS submitted 2025-06-19 cs.CR cs.AI

Pedro H. Lui , Lucas P. Siqueira , Juliano F. Kazienko , Vagner E. Quincozes , Silvio E. Quincozes , Daniel Welfer This is my paper · ORCID

classification cs.CRcs.AI

keywords machinelearningintrusiondetectionHealthcare5.0explainableAISHAPWUSTL-EHMS-2020biomedicalsensordataspoofing

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

This paper claims that in intrusion detection for Healthcare 5.0, network traffic features dominate overall, but biomedical physiological signals meaningfully contribute to detecting spoofing attacks. It reports that XGBoost achieves 99% F1-score for benign and data alteration traffic and 81% for spoofing, and that temperature reaches a Shapley value magnitude of 0.37 in the explainability analysis. If this claim is correct, hybrid intrusion detection systems that combine network and biomedical data should detect spoofing attacks better than network-only systems, and current datasets that omit physiological data are missing relevant signal. The paper supports this by applying SHAP to a single dataset that merges network flow metrics with biometric measurements.

What carries the argument

The central machinery is SHAP (SHapley Additive exPlanations), applied to a trained XGBoost classifier. SHAP assigns each feature a Shapley value from game theory, quantifying how much that feature pushed the model's prediction toward each class, and the paper uses summary bar plots and summary plots to rank and visualize feature contributions across samples. The dataset under analysis, WUSTL-EHMS-2020, combines 35 network flow metrics with 8 biometric measurements across 16,315 samples, and the SHAP analysis on the best-performing classifier is what carries the argument that biomedical features matter for spoofing detection.

What would settle it

If an XGBoost model trained only on the 35 network features achieves the same spoofing F1-score and recall as the hybrid model, the claimed contribution of biomedical data would be refuted; concretely, retrain with the eight biomedical features removed and compare spoofing recall, which the paper reports as 0.72 in the hybrid setting.

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Extended reading notes

Core claim

The central discovery is that combining network flow metrics with biomedical sensor data in an intrusion detection model, and explaining predictions with SHAP, reveals a division of labor: network features are the primary drivers of detection across all attack classes, while biomedical features such as temperature, pulse rate, oxygen saturation, respiratory rate, and ECG ST segment contribute specifically to spoofing detection. The paper's evidence is the SHAP summary plots for the three classes, where biomedical features appear among the most influential for the spoofing label, and the quantitative claim that temperature reaches a Shapley magnitude of 0.37. The authors interpret this as indicating that physiological signals should not be overlooked when securing connected medical environments, particularly for cyber-physical attacks that manipulate biometric data.

Load-bearing premise

The paper assumes that the importance scores it computes for each input reflect what the model actually relies on, without testing a version of the model that leaves the biomedical data out.

Editorial extensions

If this is right

If biomedical features genuinely sharpen spoofing detection, then intrusion detection datasets for healthcare that omit physiological sensor data are discarding signal relevant to a common attack class.
Hybrid cyber-biomedical intrusion detection systems should target spoofing in particular, since it is the hardest class (F1 0.81 with XGBoost, far below the 0.99 for benign and data alteration).
SHAP rankings can identify which specific physiological signals matter most, directing where to add sensor redundancy or tamper detection in wearable and implantable devices.
Network-only intrusion detection models, which are standard in much of the literature, may have a systematic blind spot for spoofing that only becomes visible when biomedical data are included in the training set.
Explainability analysis of this kind can bridge the gap between cybersecurity and clinical monitoring, allowing security models to exploit physiological context already being collected for patient care.

Reading between the lines

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

The paper's evidence for biomedical relevance is correlational: it relies on SHAP values from a single model trained on all features, so a natural test is an ablation study that removes the eight biomedical features and checks whether spoofing recall (reported as 0.72) actually drops.
The temperature-spoofing association may reflect a testbed artifact rather than a physiological invariant, since the dataset was collected in one controlled environment with a single attacker topology; re-running the same analysis on other IoMT datasets would clarify whether the 0.37 magnitude is general.
A direct experimental extension, not performed in the paper, would be to train network-only, biomedical-only, and hybrid classifiers and compare their class-wise F1 scores; such a comparison would separate the contribution of each feature group decisively.
The large gap in spoofing detection across classifiers (recall 0.72 for XGBoost versus 0.07 for random forest and 0.01 for SVC) suggests that spoofing patterns are subtle and model-dependent, so the benefit of biomedical features may also depend heavily on the chosen learning algorithm.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Quick take: this is a small, honest empirical study that does one thing usefully—it reports per-attack-class SHAP for a public cyber-biomedical dataset—and then overinterprets it. The XGBoost numbers are plausible and the code is public; the preprocessing is described clearly enough to reproduce. The observation that Temp, SpO2, and a few other physiological features show up in the spoofing class's SHAP plot is worth recording. That is a real, if modest, addition over prior work that mostly looked at aggregate rankings.

The soft spots are in proportion. The central claim—biomedical features are 'highly relevant' for spoofing detection—rests entirely on reading SHAP plots from a single all-feature XGBoost model. There is no ablation that removes the biomedical group, no network-only or biomedical-only model, no statistical test comparing attribute distributions, and no error bars: one unseeded 80/20 split. On a dataset with 87.5% benign samples and correlated features, SHAP attributions can shift arbitrarily between correlated predictors. So the headline implication—that hybrid cyber-biomedical IDS should improve spoofing detection—is not supported by the experiments. The abstract also cites a Shapley magnitude of 0.37 for temperature, but that number does not appear in the body; either report it or drop it. In the related work, the claim that prior XAI studies overlooked biomedical features is a bit strong—Aljuhani et al. already flagged blood pressure and SpO2, though not a per-class spoofing breakdown.

The paper itself half-admits the gap: Section 6 lists as future work the XAI analysis across biomedical, network, and combined data. That comparison is exactly what would validate the title. As written, the title overpromises—'performance of cyber-biomedical features' is not actually evaluated separately.

None of this is fatal. The paper is not circular or deceptive; it is a descriptive case study with a clear method and a testable hypothesis. It just needs the right experiments to back the claim. A referee should ask for feature-group ablation, a few seeded splits with error bars, and a table of the exact SHAP values behind the abstract's numbers.

Who is this for? Practitioners working with WUSTL-EHMS-2020 who want a starting point for XAI analysis. It is not a definitive answer on feature group value. I'd send it to peer review—there's enough here to warrant the extra work—but with the expectation of major revision.

Referee Report

4 major / 5 minor

Summary. The paper evaluates intrusion detection on the WUSTL-EHMS-2020 Healthcare 5.0 dataset, which combines network flow metrics with biomedical sensor readings. Four classifiers are trained on an 80/20 split with standard preprocessing, and XGBoost is reported as the best performer, achieving F1-scores of 0.99 for benign and data alteration classes and 0.81 for spoofing. The authors then apply SHAP to the XGBoost model and interpret the resulting summary plots to claim that network features dominate intrusion detection overall, while biomedical features, especially temperature, contribute meaningfully to spoofing detection. The paper concludes that hybrid cyber-biomedical IDS designs are needed for Healthcare 5.0 and lists future work including a three-way XAI comparison of biomedical, network, and combined data.

Significance. If the central claim were adequately supported, the paper would provide a useful, concrete indication that physiological sensor data can improve spoofing detection in healthcare IoT environments, a setting where prior IDS work has focused almost exclusively on network traffic. The manuscript has tangible strengths: the use of a relevant public dataset, a clearly described preprocessing pipeline, a reproducibility link to public code, and a straightforward multiclass evaluation across four classifiers with per-class precision, recall, and F1. The classification results themselves are plausible and reported transparently. However, the paper's headline contribution—that biomedical features contribute to spoofing detection—rests entirely on qualitative inspection of SHAP plots from a single all-feature model. Without feature-group ablations, biomedical-only or network-only models, multiple random splits, or statistical comparison of attribution patterns, the SHAP interpretations do not establish that biomedical features add predictive value for spoofing.

major comments (4)

[Section 5.2 and Section 6] The central claim that biomedical features contributed to spoofing detection is supported only by SHAP summary plots from a single XGBoost model trained on all features. No ablation removes the biomedical feature group, no biomedical-only or network-only model is trained, and no statistical test compares SHAP attributions across feature groups. In an imbalanced multiclass setting with correlated features, SHAP values describe the trained model's internal allocation of credit and do not by themselves establish that a feature group would improve detection if added or removed. The paper's own future-work statement in Section 6—"perform XAI analysis across biomedical, network, and combined data, exploring three different groups"—acknowledges that this comparison is absent. This missing experiment is load-bearing for the abstract's implication that hybrid cyber-biomedical IDS should improve spoofing detection.
[Abstract and Section 5.2] The abstract reports that temperature reached a Shapley values magnitude of 0.37, but this number does not appear anywhere in Section 5.2 or in any figure or table. The body text only says that temperature "exhibits a wide range of SHAP values." The authors should state the exact aggregation used (mean absolute SHAP, maximum per-sample value, or another quantity), the class or model output to which it applies, and provide the corresponding value in the results section, otherwise the headline quantitative claim is unverifiable from the manuscript.
[Section 4.2 and Table 2] The experimental protocol uses a single unseeded 80/20 split with no repeated runs, no cross-validation, and no reported XGBoost hyperparameters. Given the extreme variability in spoofing recall across classifiers (0.01 for SVC, 0.07 for RF, 0.67 for DT, 0.72 for XGB), the reported XGBoost advantage and the resulting SHAP interpretations could be sensitive to the particular split. The authors should add repeated stratified cross-validation or bootstrapped confidence intervals for the classification metrics and should state the random seed and hyperparameter settings needed to reproduce Figure 3 and Figure 4.
[Section 4.2 and Figure 4] The feature Source Port (Sport) is encoded with LabelEncoder and then treated as a numeric input to tree-based models, which imposes an arbitrary ordering on port numbers. Since Sport is reported as a top feature for benign and spoofing classification, this encoding choice may materially affect the SHAP analysis. The authors should justify this encoding or use a categorical-aware encoding, and they should clarify the sentence "Without this transformation, features such as Source Port would contain NaN values," since LabelEncoder does not handle missing values and the invalid entries were already removed earlier in the same paragraph.

minor comments (5)

[Section 1] The phrase "As healthcare becomes increasing reliant on digital solutions" contains a typo: "increasing" should be "increasingly."
[Section 5.2] The text states that biomedical features "demonstrate significant contributions" and "are highly relevant," but no quantitative threshold, statistical test, or comparison to a null baseline is provided; the wording should be revised to match the descriptive nature of the SHAP plots.
[Figure 3 and Figure 4] The captions and text do not specify whether the summary bar plot uses mean absolute SHAP values or another aggregation; this should be stated explicitly to make the plots interpretable.
[Section 4.1] After describing the 45 features and the removal of specific columns, the manuscript does not restate the final number of features used for model training; a brief feature-count summary after preprocessing would improve reproducibility.
[Section 4.2] The sentence describing why LabelEncoder is needed says "Without this transformation, features such as Source Port would contain NaN values," which is confusing because label encoding does not impute missing values; this should be rephrased or corrected.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper reports SHAP attributions of a trained model; the biomedical-relevance claim is an interpretation with validation gaps, not a prediction forced by construction.

full rationale

The derivation chain is self-contained: it uses the external WUSTL-EHMS-2020 dataset, a documented preprocessing pipeline, an 80/20 train/test split, four classifiers evaluated on the held-out test set, and SHAP (Lundberg and Lee) applied to the best-performing XGBoost model. Classification F1-scores are measured on unseen test samples, so they are not fitted values renamed as predictions. The SHAP values are by construction attributions of the fitted model, and the statement that biomedical features 'contributed' to spoofing detection is a reading of those attributions rather than an ablative or causal claim; Section 6 even lists as future work 'XAI analysis across biomedical, network, and combined data, exploring three different groups,' acknowledging that no feature-group ablation comparison was run. That is a validation/completeness limitation, not circularity. There is no load-bearing self-citation: the dataset and XAI method are external, and no uniqueness or equivalence theorem is imported from the authors' prior work. No step reduces its output to its input by construction.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The paper's central claim rests on the representativeness of one public dataset and on the faithfulness of SHAP as an attribution method. It adds no new theoretical constructs, but does rely on unstated modeling choices: an 80/20 split without stratification or seed, and unreported hyperparameters for all four classifiers.

free parameters (3)

train/test split ratio = 0.8/0.2
Chosen split; stratification and random seed not reported, so the split is an unstated modeling choice that affects all reported metrics.
random seed = Not reported
No random_state is given; model training and data shuffling may not be exactly reproducible.
XGBoost hyperparameters = Defaults (unreported)
No hyperparameter configuration is provided for XGBoost, RandomForest, DecisionTree, or SVC; results depend on these settings.

assumptions (4)

domain assumption WUSTL-EHMS-2020 is representative of Healthcare 5.0 IoMT environments.
Invoked in Section 4.1; the paper generalizes from this single testbed dataset to Healthcare 5.0 without evidence of distributional similarity to real deployments.
domain assumption SHAP values faithfully represent each feature's contribution to the model's decisions.
Section 5.2 treats SHAP as a 'transparent and interpretable framework'; this standard but non-trivial assumption is not tested.
domain assumption Train and test samples are independent and identically distributed draws from the same attack distribution.
Section 4.2 splits 80/20 without time-based or device-based separation; the dataset is from a single testbed session, so temporal leakage is possible.
domain assumption Class imbalance does not bias SHAP attributions for the minority spoofing class.
Spoofing is only a small fraction of samples; the paper attributes XGB's lower spoofing recall to imbalance but still interprets SHAP values for that class as reliable.

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Cite this review

Pith. "Pith review of On the Performance of Cyber-Biomedical Features for Intrusion Detection in Healthcare 5.0." pith.science (2026). https://pith.science/paper/KKR6OBCS

@misc{pith2026250617329,
  author       = {Pith},
  title        = {Pith review of: On the Performance of Cyber-Biomedical Features for Intrusion Detection in Healthcare 5.0},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KKR6OBCS}},
  note         = {Machine review of arXiv:2506.17329}
}

read the original abstract

Healthcare 5.0 integrates Artificial Intelligence (AI), the Internet of Things (IoT), real-time monitoring, and human-centered design toward personalized medicine and predictive diagnostics. However, the increasing reliance on interconnected medical technologies exposes them to cyber threats. Meanwhile, current AI-driven cybersecurity models often neglect biomedical data, limiting their effectiveness and interpretability. This study addresses this gap by applying eXplainable AI (XAI) to a Healthcare 5.0 dataset that integrates network traffic and biomedical sensor data. Classification outputs indicate that XGBoost achieved 99% F1-score for benign and data alteration, and 81% for spoofing. Explainability findings reveal that network data play a dominant role in intrusion detection whereas biomedical features contributed to spoofing detection, with temperature reaching a Shapley values magnitude of 0.37.

Figures

Figures reproduced from arXiv: 2506.17329 by the authors.

**Figure 2.** Confusion matrices for RF and SVC classifiers, highlighting misclassi [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

**Figure 3.** SHAP Summary Bar Plot. anomalies in biomedical data streams, either through sensor interference, adversarial data injections, or inconsistencies in physiological responses during an attack event. For instance, anomalous fluctuations in temperature or heart rate could indicate tampering with wearable or implantable medical devices, as seen in previous research on biomedical sensor vulnerabilities [Khan et al. 2020]… view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: SHAP plots showing feature importance for (a) benign, (b) data alter [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

M., Mashatan, A., and Miri, A

Alani, M. M., Mashatan, A., and Miri, A. (2023a). Explainable ensemble-based detection of cyber attacks on internet of medical things. In Int Conf on Dependable, Autonomic and Secure Computing, Int Conf on Pervasive Intelligence and Computing, Int Conf on Cloud and Big Data Computing, Int Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/Cy...

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[2] [2]

M., Mashatan, A., and Miri, A

Alani, M. M., Mashatan, A., and Miri, A. (2023b). XMeDNN: An Explainable Deep Neural Network System for Intrusion Detection in Internet of Medical Things . In International Conf. on Information Systems Security and Privacy , pages 144--151

work page 2023

[3] [3]

M., Confalonieri, R., Guidotti, R., Del Ser , J., Díaz-Rodríguez, N., and Herrera, F

Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M., Confalonieri, R., Guidotti, R., Del Ser , J., Díaz-Rodríguez, N., and Herrera, F. (2023). Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence . Information Fusion , 99:101805

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Aljuhani, A., Alamri, A., Kumar, P., and Jolfaei, A. (2024). An Intelligent and Explainable SaaS-Based Intrusion Detection System for Resource-Constrained IoMT . IEEE Internet of Things Journal , 11(15):25454--25463

work page 2024

[5] [5]

H., Islam, M

Ashraf, K., Nawar, S., Hosen, M. H., Islam, M. T., and Uddin, M. N. (2024). Beyond the Black Box: Employing LIME and SHAP for Transparent Health Predictions with Machine Learning Models . In 2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems (iCACCESS) , pages 1--6. IEEE

work page 2024

[6] [6]

and Guestrin, C

Chen, T. and Guestrin, C. (2016). XGBoost : A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD '16, pages 785--794, New York, NY, USA. ACM

work page 2016

[7] [7]

Dave, D., Naik, H., Singhal, S., and Patel, P. (2020). Explainable AI meets Healthcare: A Study on Heart Disease Dataset . arXiv preprint arXiv:2011.03195

work page Pith review arXiv 2020

[8] [8]

R., Maddikunta, P

Gadekallu, T. R., Maddikunta, P. K. R., Boopathy, P., Deepa, N., Chengoden, R., Victor, N., Wang, W., Wang, W., Zhu, Y., and Dev, K. (2024). XAI for Industry 5.0 - Concepts, Opportunities, Challenges and Future Directions . IEEE Open Journal of the Communications Society , pages 1--1

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