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REVIEW 5 major objections 5 minor 1 cited by

Optimized IoT Intrusion Detection using Machine Learning Technique

T0 review · 5 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read A tuned Random Forest classifier achieves 99.39% test accuracy on the BoTNeTIoT-L01 IoT intrusion detection dataset.

desk verdict Routine ML benchmark on a public IoT dataset with an unverifiable 99.39% accuracy headline and a load-bearing data-split problem; the novelty claim is never actually described. read the letter →

arxiv 2412.02845 v1 pith:BKM7465G submitted 2024-12-03 cs.CR cs.LG

Muhammad Zawad Mahmud , Samiha Islam , Shahran Rahman Alve , Al Jubayer Pial This is my paper · ORCID
classification cs.CRcs.LG
keywords IoTintrusiondetectionRandomForestBoTNeTIoT-L01machinelearninghyperparametertuningGridSearchCVnetworksecurityclassifiercomparison
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 sets out to show that a Random Forest classifier, tuned with GridSearchCV and tested on the BoTNeTIoT-L01 IoT intrusion dataset, can identify network attacks with 99.39% accuracy and an F1 score of 0.994. It compares five machine-learning classifiers and reports that Random Forest beats Decision Tree, Gradient Boosting, AdaBoost, and K-Nearest Neighbor, and also beats the prior results it lists. The authors claim no earlier study has reached this accuracy for IoT intrusion detection. A reader should care because a fast, tuned ensemble that reliably flags malicious IoT traffic would be directly useful in guarding networks of connected devices.

What carries the argument

The load-bearing object is a Random Forest ensemble: many decision trees trained on bootstrap samples, each voting on whether a flow is an attack, with the majority vote deciding. The specific configuration—gini splitting, depth 8, sqrt feature sampling, 200 trees—is located by GridSearchCV, and five-fold cross-validation is used to stabilize the estimate. The claim also depends on the BoTNeTIoT-L01 dataset's class balance and its 820,834 flows, over which a random 80/20 split produces 122,373 correct and 753 wrong test predictions for Random Forest.

What would settle it

Re-run the same Random Forest configuration on BoTNeTIoT-L01 with a time-ordered or session-aware split, keeping the first 80% of flows for training and the last 20% for testing, and compare the resulting accuracy to 99.39%. If it falls materially, the random split was leaking information and the headline result does not measure detection of unseen attacks.

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Extended reading notes

Core claim

The paper's central claim is that the Random Forest classifier with criterion 'gini', max depth 8, sqrt max features, and 200 estimators—obtained through GridSearchCV and assessed with five-fold cross-validation—achieves the highest test accuracy of 99.39% on a balanced 80/20 split of the BoTNeTIoT-L01 dataset, with precision 0.997, recall 0.989, F1 0.994, and AUC 1.00. The same pipeline yields 99.23% for Decision Tree, 97.89% for Gradient Boosting, 95.26% for AdaBoost, and 94.84% for K-Nearest Neighbor. The paper further claims that this Random Forest result surpasses the previously published models it compares against, including a 99.30% Random Forest on a different industrial-IoT dataset.

Load-bearing premise

The 80/20 random split treats each network flow as independent, so training and test sets are assumed not to contain flows from the same attack session; if they do, the reported 99.39% overstates performance on unseen attacks.

Editorial extensions

If this is right

  • An IDS built on this tuned Random Forest would flag roughly 99 of every 100 attack flows in this dataset while keeping false alarms near zero, an operating point strong enough for deployment screening.
  • Because GridSearchCV and five-fold cross-validation are standard, the same tuning recipe can be applied directly to other IoT traffic datasets.
  • The comparison suggests that a tuned classical ensemble can match or beat deep-learning IDS on tabular flow features, which matters for resource-constrained IoT hardware.
  • The specific 99.39% figure is tied to this dataset and split; reproducing it requires the same hyperparameters, dataset, and test protocol.

Reading between the lines

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

  • The paper relies on a random row-wise split; a temporal or session-aware split would likely lower the accuracy because attack flows from the same burst would no longer appear on both sides of the training/test boundary. Testing that split is the quickest way to see how much of 99.39% is genuine generalization.
  • The 'novel component selection and extraction strategy' is not described in the methodology, so an ablation study removing it would clarify whether the accuracy comes from feature engineering or from the tuned Random Forest alone.
  • Applying the same tuned Random Forest to other public IoT intrusion datasets (for instance newer or less balanced ones) would show whether the 99.39% level transfers beyond BoTNeTIoT-L01.
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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

5 major / 5 minor

Summary. The paper reports an empirical comparison of five machine-learning classifiers (Random Forest, Decision Tree, K-Nearest Neighbors, Gradient Boosting, and AdaBoost) on the BoTNeTIoT-L01 IoT intrusion-detection dataset. The authors state that the data were split 80/20 for training/testing, that GridSearchCV with five-fold cross-validation was used for hyperparameter tuning, and that Random Forest achieved the highest accuracy of 99.39% with an F1 score of 0.994. The paper includes confusion matrices, per-model precision/recall/F1 tables, ROC AUC values, and a comparison table against four prior studies. The central claim is that this accuracy is higher than all previous IoT intrusion-detection results.

Significance. If the empirical result were properly established, it would be a modest benchmark result: a tuned Random Forest can reach very high accuracy on this particular IoT flow dataset. The paper has some strengths: the hyperparameters of all five models are reported, the standard evaluation metrics are defined, and the confusion-matrix totals are internally consistent (each sums to 123,126 rows). However, the evaluation protocol is not documented to a level that supports the central generalization claim. There is no temporal or session-aware split, no repeated-run variance or error bars, an unexplained discrepancy between the reported test-set size and the stated 20% of the dataset, and the 'no other study has reached this level' claim is not supported by the evidence presented. The paper does not define the 'novel component selection and extraction strategy' promised in the abstract. As presented, the contribution is a benchmark with unresolved correctness risks rather than a citable research result.

major comments (5)
  1. [Section III-A and Section IV-A] The evaluation protocol does not establish that the test set represents unseen traffic. The paper states only that the BoTNeTIoT-L01 dataset has 820,834 rows and was split into an 80-20 ratio for training and testing, with no mention of temporal ordering, session-aware grouping, or stratification. Because BoTNeTIoT-L01 is a network-flow dataset, a random split can place flows from the same attack session or the same 5-tuple on both sides of the train/test boundary, allowing the classifier to memorize addresses and ports rather than detect novel attacks. The test-set size also does not match the stated split: the confusion matrices in Section IV sum to 123,126 rows, whereas 20% of 820,834 is approximately 164,167. The paper does not report any preprocessing, deduplication, or filtering that would explain this discrepancy. This directly affects the validity of the headline 99.39% accuracy figure.
  2. [Section IV-D versus Section IV-A/B] Table I is inconsistent with the confusion matrices for Random Forest and Decision Tree. For Random Forest, the confusion matrix in Section IV-A gives precision = 59,268/(59,268+610) ≈ 0.990 and recall = 59,268/(59,268+143) ≈ 0.998, but Table I reports precision 0.997 and recall 0.989. For Decision Tree, Section IV-B gives precision ≈ 0.993 and recall ≈ 0.991, but Table I reports precision 0.991 and recall 0.993. The two rows appear to have precision and recall swapped. This error changes the per-metric ranking of the models and must be corrected.
  3. [Section II and Section IV-E] The claim that 'no other study has been able to reach this level of accuracy for IoT intrusion detection' is not supported by the evidence. Table III compares only four prior studies, and most of them use different datasets (for example, [4] uses NF-UNSW-NB15-v2). The table provides no confidence intervals, no paired statistical tests, and no repeated-run variance. The comparison therefore cannot justify the statement that the proposed model outperforms all existing work. This claim should be removed or replaced with a dataset-matched comparison with appropriate uncertainty quantification.
  4. [Abstract and Section I] The abstract states that the paper 'proposes and implements a novel component selection and extraction strategy (our strategy)', but no such feature-selection or feature-extraction strategy is described anywhere in the manuscript. Section III describes the dataset and the five classifiers but does not present a feature-selection method. The claimed novelty is therefore unsubstantiated. The authors should either describe the strategy in detail or remove the claim.
  5. [Section IV-A and Table II] The report of AUC = 1.00 for Random Forest is inconsistent with the confusion matrix. The confusion matrix in Section IV-A contains 753 misclassifications (610 false positives and 143 false negatives). With any false positive or false negative on the test set, the ROC curve cannot reach TPR = 1 at FPR = 0, so the AUC must be strictly less than 1. This suggests the AUC values in Table II were computed on a different set (perhaps the training set) or are incorrectly reported. All AUC values should be recomputed on the same test set used for the accuracy numbers.
minor comments (5)
  1. [Section IV-A] In the Random Forest confusion-matrix paragraph, '610 false negatives' should read '610 false positives'; the text already lists 143 false negatives immediately before.
  2. [Section IV-B] In the Decision Tree confusion-matrix paragraph, '430 false negatives' should read '430 false positives'; the text already lists 520 false negatives.
  3. [Tables I and II] The model name 'Decision Free' appears in both tables and should be 'Decision Tree'.
  4. [Throughout] There are repeated spelling errors ('hyperparamter', 'tuinted', 'tuined') and grammatical issues that should be corrected in a final revision.
  5. [Section III-B] The paper states that five-fold cross-validation was applied but does not clarify whether it was used for hyperparameter selection, for model evaluation, or both. This should be stated explicitly, together with the exact role of the 80/20 split.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the 99.39% accuracy is an empirical test-set measurement, not a quantity derived from the models' fitted parameters; the only self-citations support standard metric definitions and are not load-bearing.

full rationale

This paper reports an empirical machine-learning comparison on the BoTNeTIoT-L01 dataset; it does not attempt a derivation of its headline result from its inputs. The Random Forest accuracy of 99.39% is presented as a measured test-set performance (Section IV-A, Table II), so there is no fitted-parameter-renamed-as-prediction step and no self-definitional equation. The abstract promises a 'novel component selection and extraction strategy,' but the strategy is never defined, so it cannot be shown to reduce to the paper's own outputs. The only self-citations ([13], [14]) support the standard definitions of precision, recall, F1-score, and accuracy; those equations are textbook definitions and the citations are not load-bearing evidence for the central claim. The strongest validity concern is that the train/test split is described only as an '80-20 ratio' (Section III-A), which could permit temporal or session-level leakage and inflate the reported accuracy; however, that is a benchmarking and generalization concern, not a circularity of the kind where a prediction is equivalent to its inputs by construction. Accordingly, no specific circular step can be quoted, and the circularity score is 0.

Assumptions & free parameters 5 free parameters · 3 assumptions · 0 invented entities

The only free parameters are the hyperparameters selected by GridSearchCV; they are model choices rather than scientific constants and are reported per model. The central claim depends on dataset label quality, split independence, and standard metrics. No invented entities are introduced.

free parameters (5)
  • Random Forest tuned hyperparameters = criterion=gini, max_depth=8, max_features=sqrt, n_estimators=200
    Selected by GridSearchCV on the training split; no independent validation set is described for model selection.
  • Decision Tree tuned hyperparameters = criterion=entropy, max_depth=30, min_samples_leaf=5, min_samples_split=10, max_features=sqrt
    Selected by GridSearchCV; reported only as final configuration.
  • KNN tuned hyperparameters = n_neighbors=5, weights=distance, metric=manhattan, p=1
    Selected by GridSearchCV; reported only as final configuration.
  • Gradient Boosting tuned hyperparameters = learning_rate=0.01, max_depth=4, n_estimators=500, subsample=0.8
    Selected by GridSearchCV; reported only as final configuration.
  • AdaBoost tuned hyperparameters = algorithm=SAMME.R, learning_rate=0.1, n_estimators=100
    Selected by GridSearchCV; reported only as final configuration.
assumptions (3)
  • domain assumption The BoTNeTIoT-L01 dataset labels are correct and representative of IoT intrusion traffic.
    Section III-A uses the Kaggle dataset without external label validation; all conclusions depend on label quality.
  • domain assumption Random 80/20 split yields independent training and test samples.
    Section III-A does not address temporal or session-level correlation in network flows; leakage would inflate accuracy.
  • standard math Standard metric formulas in Eqs. (1)-(4) define the reported scores.
    Section III-D invokes standard definitions; this is unproblematic background.

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Cite this review

Pith. "Pith review of Optimized IoT Intrusion Detection using Machine Learning Technique." pith.science (2026). https://pith.science/paper/BKM7465G

@misc{pith2026241202845,
  author       = {Pith},
  title        = {Pith review of: Optimized IoT Intrusion Detection using Machine Learning Technique},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BKM7465G}},
  note         = {Machine review of arXiv:2412.02845}
}
read the original abstract

An application of software known as an Intrusion Detection System (IDS) employs machine algorithms to identify network intrusions. Selective logging, safeguarding privacy, reputation-based defense against numerous attacks, and dynamic response to threats are a few of the problems that intrusion identification is used to solve. The biological system known as IoT has seen a rapid increase in high dimensionality and information traffic. Self-protective mechanisms like intrusion detection systems (IDSs) are essential for defending against a variety of attacks. On the other hand, the functional and physical diversity of IoT IDS systems causes significant issues. These attributes make it troublesome and unrealistic to completely use all IoT elements and properties for IDS self-security. For peculiarity-based IDS, this study proposes and implements a novel component selection and extraction strategy (our strategy). A five-ML algorithm model-based IDS for machine learning-based networks with proper hyperparamater tuning is presented in this paper by examining how the most popular feature selection methods and classifiers are combined, such as K-Nearest Neighbors (KNN) Classifier, Decision Tree (DT) Classifier, Random Forest (RF) Classifier, Gradient Boosting Classifier, and Ada Boost Classifier. The Random Forest (RF) classifier had the highest accuracy of 99.39%. The K-Nearest Neighbor (KNN) classifier exhibited the lowest performance among the evaluated models, achieving an accuracy of 94.84%. This study's models have a significantly higher performance rate than those used in previous studies, indicating that they are more reliable.

Figures

Figures reproduced from arXiv: 2412.02845 by the authors.

Figure 3
Figure 3. Diagram of Decision Tree Classifier The K-Nearest Neighbor approach classifies new data points according to how similar they are to preexisting data points, saving all available data. This suggests that new data may be easily classified into an appropriate category by applying the KNN algorithm. In [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 2
Figure 2. Diagram of Random Forest Classifier model put into practice was the Decision Tree Classifier. The method is widely applied in machine learning to solve prob￾lems related to regression and classification. The internal node and the leaf node are the two types of nodes that a root node produces. Because they have several branches, internal nodes are known as decision-makers, whereas leaf nodes are known for producing r… view at source ↗
Figure 5
Figure 5. Diagram of Gradient Boosting Classifier techniques. In this process, weights are re-allocated to each instance, with greater weights assigned to incorrectly identified instances. The construction of the first model and the method by which the algorithm finds errors in the first version are depicted in [PITH_FULL_IMAGE:figures/full_fig_p004_5.png] view at source ↗
Figures from the paper (4 more)
Figure 6
Figure 6. Figure 6: Diagram of Ada Boost Classifier C. Workflow Diagram [PITH_FULL_IMAGE:figures/full_fig_p004_6.png]
Figure 8
Figure 8. Figure 8: Confusion matrix of Random Forest Classifier [PITH_FULL_IMAGE:figures/full_fig_p004_8.png]
Figure 9
Figure 9. Figure 9: ROC curve of Random Forest Classifier B. Decision Tree [PITH_FULL_IMAGE:figures/full_fig_p005_9.png]
Figure 12
Figure 12. Figure 12: Confusion matrix of K-Nearest Neighbor classifier [PITH_FULL_IMAGE:figures/full_fig_p005_12.png]

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. Deep Learning and Hybrid Approaches for Dynamic Scene Analysis, Object Detection and Motion Tracking

    cs.CV 2024-12 reject novelty 2.0 of 10

    A thesis-style report combines standard object detection and tracking tools into a surveillance pipeline and reports precision and recall numbers without revealing the dataset or evaluation protocol.

Reference graph

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Reviewed August 11, 2026 · model on record in the stance chip above.