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REVIEW 5 major objections 5 minor 95 references

A Review of Various Datasets for Machine Learning Algorithm-Based Intrusion Detection System: Advances and Challenges

T0 review · 5 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This review compiles and critically tabulates a decade of ML-based intrusion detection research on five benchmark datasets, showing how dataset choice, class imbalance, and evaluation metrics shape reported accuracy.

desk verdict A sprawling IDS-ML review whose tables are the product, but the product has arithmetic and citation errors that make it unreliable without re-verifying every row. read the letter →

arxiv 2506.02438 v1 pith:T2T6U47A submitted 2025-06-03 cs.CR cs.AIcs.LG

Sudhanshu Sekhar Tripathy , Bichitrananda Behera This is my paper
classification cs.CRcs.AIcs.LG
keywords IntrusionDetectionSystemmachinelearningclassifiersKDDCUP99NSL-KDDUNSW-NB15CICIDS-2017CSE-CIC-IDS2018IDSdatasetreview
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 review surveys roughly a decade of studies that apply machine-learning classifiers to intrusion detection on five benchmark datasets: KDDCUP'99, NSL-KDD, CICIDS-2017, UNSW-NB15, and CSE-CIC-IDS2018. It describes how each dataset was captured, what attack classes it contains, and where it falls short, then introduces the classifiers used in the surveyed studies. The core contribution is a tabulated comparison of dozens of published results, listing dataset, classifier, evaluation metrics, accuracy, and each study's conclusion. Read as a whole, the table shows that reported accuracy varies widely with dataset and preprocessing choices, so the paper offers itself as a reference map for choosing datasets and methods in future IDS work.

What carries the argument

The load-bearing object is Table 8, a ten-year comparative overview that pairs each surveyed study with the dataset it used, the classifiers applied, the evaluation matrix reported, the accuracy figure, and the authors' stated findings. Supporting it are the per-dataset specification tables, which give record counts, attack classes, and the class-imbalance ratios that the discussion uses to explain why some classifiers appear to outperform others. The table does the argument's work: it converts a scattered literature into a single point of comparison from which the review draws its conclusions about dataset-and-metric dependence.

What would settle it

Recompute the class totals in Table 2: the NSL-KDD 20% row lists 13,449 + 9,234 + 2,289 + 11 + 209 = 25,192 records, not the 25,912 stated in the table; the same arithmetic check can be applied to every row of Table 8 by comparing each accuracy figure with its cited source paper. If a meaningful share of those checks fail, the review's comparative conclusions do not stand.

Watch

Extended reading notes

Core claim

The paper's central claim is that a reliable machine-learning-based intrusion detection system cannot be judged on classifier choice alone; the dataset, its class balance, the feature-selection step, and the evaluation metrics determine the reported outcome. To support this, the authors assemble the five most-used IDS datasets, explain their provenance and known weaknesses, including duplicate records, skew, and outdated attack profiles, and tabulate published results from studies using SVM, KNN, DT, LR, NB, RF, XGBoost, AdaBoost, ANN, and DNN. On the authors' own terms, the review establishes that ensemble and deep models frequently post the highest accuracies but that high accuracy on one dataset does not transfer automatically, and that false-alarm reduction remains the open problem.

Load-bearing premise

The load-bearing premise is that every number in the comparative and dataset tables was transcribed faithfully from the cited papers; the review performs no independent verification, and internal signs such as Table 2's row sum not matching its stated total (25,192 versus 25,912) and references [35] and [71] pointing to the same paper show the transcription is fallible.

Editorial extensions

If this is right

  • Future IDS researchers can use the compiled table to select a dataset and classifier pair for a given attack profile instead of starting from scratch.
  • Ensemble and deep classifiers such as random forest, XGBoost, and neural networks will likely keep appearing as top accuracy performers, but their reported margins over simple baselines depend on the dataset's imbalance.
  • Standardized evaluation metrics and train/test splits would be needed before cross-study accuracies can be ranked fairly, because the table mixes accuracy, precision, recall, F1, FAR, and other metrics across rows.
  • The five datasets' known defects, such as redundancy in KDDCUP'99 and class imbalance in CICIDS-2017, remain the binding constraint on how much a new classifier can improve detection.

Reading between the lines

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

  • A consequence the authors do not draw is that the same table can be used as a transcription-checking benchmark: each row's numbers should be reproducible from its cited source, and the duplicate labeling of references [35] and [71] suggests at least one citation needs correction.
  • If the field adopted one common protocol, fixed train/test sets, same metrics, and class-balanced reporting, the qualitative pattern visible here, that dataset choice often matters more than classifier choice, could be turned into a quantitative ranking.
  • A testable extension would be to run a single classifier suite across all five datasets under identical preprocessing; the result would show whether the accuracy spreads in Table 8 come from methods or from data.
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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 manuscript is a narrative review of machine-learning-based intrusion detection studies that use five public datasets (KDDCUP'99, NSL-KDD, UNSW-NB15, CICIDS-2017, and CSE-CIC-IDS2018). It describes the datasets, explains nine classifier families (SVM, KNN, DT, LR, NB, RF, XGBoost, AdaBoost, and ANN/DNN), and presents a large table (Table 8) summarizing roughly forty prior studies with reported accuracies and findings. The authors conclude that SVMs and ensemble methods are prominent in the literature and propose future work with additional datasets and deep learning methods.

Significance. If the compilation were accurate, this paper could serve as a convenient entry point for researchers selecting datasets and baselines for IDS experiments. The breadth is real: five widely used datasets and roughly four dozen references are covered, and the per-classifier descriptions are accessible for newcomers. However, the paper's value depends entirely on faithful transcription of primary sources, and the verification burden is substantial. The paper offers no reproducible code, no machine-checked data tables, and no stated selection protocol; its contribution is the tabulation itself, which is exactly what is called into question by the errors documented below.

major comments (5)
  1. [Section 2.2, Table 2] The row for 'KDD Train+ 20%' reports a total of 25,912 records, but the five class counts in the same row sum to 13,449 + 9,234 + 2,289 + 11 + 209 = 25,192, a discrepancy of 720. Since the review's descriptive value rests on accurate dataset statistics, this arithmetic error is a load-bearing defect, and it also raises doubt about the unverified counts in Tables 1, 3, 4, and 7.
  2. [Section 5, Table 8, references [35]/[71] and [22]/[46]/[79]] The row labeled 'Kazi A., Billal M et al. [71]' reports ANN=94.02% and SVM=82.34% on NSL-KDD, but reference [71] is the same Lin, Ke, and Tsai CANN paper already listed as [35]; the [35] row in Table 8 reports CANN/k-NN/SVM results on KDD Cup 99, not the values in the [71] row. In addition, the row 'Basheri, Mohammad & Iqbal et al. [22]' describes an SVM/RF/ELM comparison, yet reference [22] is Iqbal and Aftab's ANN paper, while the Iqbal and Aftab row is labeled [46], a number that also labels a different UNSW-NB15 study by Kumar. The citation keys in Table 8 are therefore not a reliable index to the bibliography, and the table cannot be used as a reference map in its current state.
  3. [Section 3, references [23]/[25] and [37]/[88], [51]/[58]] References [23] and [25] are the same Watson et al. paper, and Section 3 contains the same paragraph about SVM-based malware detection at the cloud hypervisor level for both entries. Similarly, references [6]/[8], [37]/[88], and [51]/[58] are duplicated. This means the survey counts the same works multiple times, and the 'ten-year overview' of Table 8 is inflated unless all duplicate entries are merged and renumbered.
  4. [Section 6, Table 8] The paper promises a 'critical analysis' of the surveyed methods, but Section 6 is a generic summary and Table 8 merely lists raw accuracies without standardizing evaluation conditions (e.g., binary vs multiclass classification, feature subsets, train/test splits, or performance metrics) or discussing how these differences affect comparability. A compilation without per-row caveats does not support the stated claim of a critical analysis.
  5. [Sections 2-5, review methodology] The manuscript does not state the search strategy, inclusion/exclusion criteria, or quality-assessment procedure used to select the studies in Table 8. Without this information, the reader cannot determine whether the selection is representative or biased, which is a fundamental requirement for a review that claims to be comprehensive. This is fixable but requires a substantial reworking of the manuscript.
minor comments (5)
  1. [Figure numbering] There is no Figure 14 or Figure 15; the figure sequence jumps from Figure 13 to Figure 16, and several figures (e.g., Figure 10) are not referenced in the text.
  2. [Abstract and Section 2.5] Several sentences contain grammatical errors that impede comprehension, for example 'an accurate evaluation matrix' in the abstract and 'The attacked administration has 30 servers' in Section 2.5.
  3. [Reference [15]] Reference [15] points to a 'Registry of open data on AWS' URL for CSE-CIC-IDS2021, while the text discusses CSE-CIC-IDS2018; please update the reference or the text.
  4. [Reference formatting] The citation for reference [10] is a bare URL and lacks access information; other references also have inconsistent formatting, with some including DOIs and others not.
  5. [Table 8 layout] Table 8 has no row numbers and the 'Findings' column is not clearly separated; adding row numbers would allow readers to refer to specific entries and would make it easier to verify the table against the bibliography.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the survey's compilation is independent of its single self-citation; transcription errors affect factual reliability but not derivational circularity.

full rationale

This paper is a literature review, not a derivation: it compiles reported accuracies from external studies and describes ML classifiers and datasets. There is no fitted parameter that is later called a prediction, no equation whose output is fixed by its input by construction, and no uniqueness theorem or ansatz imported from the authors' own prior work. The only self-citation, [4] (Tripathy & Behera), appears as one row in Table 8; the review's conclusions about datasets, classifiers, and challenges do not depend on that row, so it is a minor self-citation rather than load-bearing evidence. The internal inconsistencies noted in the manuscript—the NSL-KDD 20% total of 25,912 versus class counts summing to 25,192, and duplicate references such as [35]/[71] and [37]/[88]—are factual and reference-transcription problems that undermine the reliability of the compilation, but they are not circularity: no claim is defined in terms of itself, and no result is equivalent to its input by construction. Accordingly the circularity score is 0.

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

The review rests on the trustworthiness of the 97 cited papers and on the assumption that the five chosen datasets are representative. There are no fitted parameters or invented entities. The central assumptions are that the cited results were correctly transcribed and that the selection of studies is unbiased, both of which are questionable given the errors observed.

assumptions (2)
  • domain assumption The five datasets (KDDCUP'99, NSL-KDD, CICIDS-2017, UNSW-NB15, CSE-CIC-IDS2018) are representative of intrusion detection benchmarks.
    Section 2 introduces these five as 'popular datasets' and the entire review is built around them. If this set is not representative, the review's coverage is incomplete.
  • domain assumption The accuracy values and findings attributed to cited papers in Table 8 are accurate transcriptions of the original sources.
    Section 5's table is the core of the review. No original experiments are run, so the review's conclusions depend on the secondary data being transcribed correctly.

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Cite this review

Pith. "Pith review of A Review of Various Datasets for Machine Learning Algorithm-Based Intrusion Detection System: Advances and Challenges." pith.science (2026). https://pith.science/paper/T2T6U47A

@misc{pith2026250602438,
  author       = {Pith},
  title        = {Pith review of: A Review of Various Datasets for Machine Learning Algorithm-Based Intrusion Detection System: Advances and Challenges},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/T2T6U47A}},
  note         = {Machine review of arXiv:2506.02438}
}
read the original abstract

IDS aims to protect computer networks from security threats by detecting, notifying, and taking appropriate action to prevent illegal access and protect confidential information. As the globe becomes increasingly dependent on technology and automated processes, ensuring secured systems, applications, and networks has become one of the most significant problems of this era. The global web and digital technology have significantly accelerated the evolution of the modern world, necessitating the use of telecommunications and data transfer platforms. Researchers are enhancing the effectiveness of IDS by incorporating popular datasets into machine learning algorithms. IDS, equipped with machine learning classifiers, enhances security attack detection accuracy by identifying normal or abnormal network traffic. This paper explores the methods of capturing and reviewing intrusion detection systems (IDS) and evaluates the challenges existing datasets face. A deluge of research on machine learning (ML) and deep learning (DL) architecture-based intrusion detection techniques has been conducted in the past ten years on various cybersecurity datasets, including KDDCUP'99, NSL-KDD, UNSW-NB15, CICIDS-2017, and CSE-CIC-IDS2018. We conducted a literature review and presented an in-depth analysis of various intrusion detection methods that use SVM, KNN, DT, LR, NB, RF, XGBOOST, Adaboost, and ANN. We provide an overview of each technique, explaining the role of the classifiers and algorithms used. A detailed tabular analysis highlights the datasets used, classifiers employed, attacks detected, evaluation metrics, and conclusions drawn. This article offers a thorough review for future IDS research.

Figures

Figures reproduced from arXiv: 2506.02438 by the authors.

Figure 2
Figure 2. A Pictorial representation of NIDS Vs HIDS Signature-based intrusion detection: Systems use a database of known attack signatures to identify attacks. They track network packets and generate alarms if they match. However, they can only detect registered intrusions and cannot identify new ones. Anomaly-based intrusion detection systems, on the other hand, search for unidentified threats. It monitors network traffic a… view at source ↗
Figure 5
Figure 5. A snapshot of the Intrusion Detection System 1.2. Conceptual layout of the system for intrusion detection [PITH_FULL_IMAGE:figures/full_fig_p003_5.png] view at source ↗
Figure 8
Figure 8. Four different types of assault categorization [PITH_FULL_IMAGE:figures/full_fig_p004_8.png] view at source ↗
Figures from the paper (9 more)
Figure 9
Figure 9. Figure 9: The architecture of the KDD CUP'99 assault dataset The dataset used for the Third International Knowledge Discovery and Data Mining Tools competition aimed to develop a network intrusion model categorizing intrusions as good, bad, or normal. It consisted of 4,898,430 s…
Figure 11
Figure 11. Figure 11: The suggested architecture of the CICIDS-2017 assault dataset 2.3.1. CICIDS-2017 dataset specifications For a total of five days, data was gathered. the event ran from Monday, July 3, 2017, at 9 a.m. to Friday, July 7, 2017, at 5 p.m. Several assaults were conducted d…
Figure 10
Figure 10. Figure 10: A scenario of an extensive investigative workflow employing the NSL-KDD data set for the detection of intrusions. 2.3. The CICIDS-2017 dataset The CIC-IDS2017 dataset [9], created by the Canadian Institute for Cybersecurity in 2017, includes various operating systems,…
Figure 12
Figure 12. Figure 12: depicts the system's conceptual layout as it is [PITH_FULL_IMAGE:figures/full_fig_p006_12.png]
Figure 13
Figure 13. Figure 13: The CSE-CIC-IDS2018 attack data Sets proposed architecture [PITH_FULL_IMAGE:figures/full_fig_p007_13.png]
Figure 16
Figure 16. Figure 16: An Overview of Support Vector Machine [PITH_FULL_IMAGE:figures/full_fig_p010_16.png]
Figure 21
Figure 21. Figure 21: It is composed of an ensemble of decision trees [PITH_FULL_IMAGE:figures/full_fig_p012_21.png]
Figure 22
Figure 22. Figure 22: An overview of the XGBOOST System's architecture 4.8. Adaboost Classifier AdaBoost is a boosting technique that builds a strong model by combining multiple weak classifiers in a sequential manner. The process involves training weak classifiers iteratively, each time a…
Figure 23
Figure 23. Figure 23: The Adaboost Algorithm's structural architecture 4.9. Artificial Neural Network (ANN) Artificial neural networks (ANNs) are efficient problem-solving techniques that mimic the human brain's structure, making them more efficient in tasks like pattern recognition than t…

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