Network Intrusion Datasets: A Survey, Limitations, and Recommendations

Daniela Chud\'a; Patrik Goldschmidt

REVIEW 3 major objections 5 minor 1 cited by

Network Intrusion Datasets: A Survey, Limitations, and Recommendations

T0 review · 3 major / 5 minor · reviewed 2026-08-08 · deepseek-v4-flash

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Pith's one-line read Data scarcity is no longer the primary obstacle: the survey finds 89 public NIDS datasets, many underused or flawed, while recent top-conference work keeps its own data private.

desk verdict A genuinely useful, carefully documented census of 89 NID datasets; the popularity analysis is the softest part and should be read as top-venue usage, not the whole field. read the letter →

arxiv 2502.06688 v3 pith:4LGJK2UY submitted 2025-02-10 cs.CR cs.NI

Patrik Goldschmidt , Daniela Chudá This is my paper · ORCID

classification cs.CRcs.NI

keywords networkintrusiondetectionNIDSdatasetssystematicliteraturereviewdatasetqualitybenchmarkevaluationpopularityanalysisCICFlowMeter

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 is a systematic literature review that tries to establish what public data actually exists for network intrusion detection research. The authors downloaded, inspected, and compared 89 public datasets across 13 properties, and they argue that data scarcity is no longer the field's main problem: datasets exist, but many are unknown, poorly documented, or carry known flaws, and a popularity analysis of recent top-conference papers shows most researchers still collect custom data that is not shared. The value of the claim, if correct, is practical: researchers can use the survey to find and select suitable benchmarks, dataset creators can follow its best practices, and future work can focus on discoverability, documentation, and data quality rather than on producing yet more data.

What carries the argument

The load-bearing instrument is a 13-property extraction protocol. For each of the 89 datasets the authors downloaded the data and inspected it rather than trusting documentation, recording year, focus, origin of normal and attack traffic (real, emulated-physical, emulated-virtual, synthetic), format, feature count, anonymization, size, a continuity-corrected duration, network type, capture completeness, train-test split availability, and labeling. Because duration is recomputed from timestamps with gaps longer than 1% of the time span ignored, papers that only report first-to-last timestamps can be compared on consistent grounds. The same table, with the general/special-purpose taxonomy and the popularity analysis, carries the survey's quantitative claims about trends.

What would settle it

Independently download all 89 datasets and re-run the 13-property protocol on the actual files; if a material share of the entries disagrees on size, duration, continuity, or labels, the survey's census and trend claims fail.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that the network intrusion detection community does not suffer from a shortage of public benchmark data. The survey catalogs 89 publicly available datasets, collected through 2023, and claims this is the largest such catalog to date. Manual inspection of each dataset reveals that they vary widely in realism, labeling, completeness, and documentation, and that many recent additions are special-purpose (IoT, ICS, SDN, APT) rather than general-purpose. A popularity analysis of 45 papers from top security conferences (2020-2023) finds that most studies use their own custom data, that CIC-IDS2017 is the most-used public dataset yet appears in only about 29% of papers, and that DARPA-based datasets such as KDD'99 and NSL-KDD no longer dominate. From this the paper concludes that the bottleneck has shifted from data availability to awareness, quality, and sharing practices.

Load-bearing premise

The claim that data scarcity is no longer the primary issue assumes the 45 papers from top security conferences published between 2020 and 2023 fairly represent contemporary state-of-the-art NIDS research, and that the inclusion criteria (public network traces, with download links working in early 2025) capture the datasets the community actually needs.

Editorial extensions

If this is right

Researchers benchmarking a new NIDS can use the 13-property table to select a dataset whose environment, attack types, format, and labeling match their target scenario, rather than defaulting to the same few popular datasets.
Dataset creators can reduce the most common criticisms by providing documentation covering goals, generation, environment, timestamps, labeling, and limitations, and by publishing both raw packets and feature-extraction code.
Because most contemporary papers collect private custom data, evaluation results are hard to reproduce; following the paper's recommendation to share data and splits would make published NIDS comparisons more trustworthy.
The trend toward specialized, emulated, flow-based datasets means future benchmarks should include up-to-date datasets in addition to popular older ones, and should account for known issues in datasets like CIC-IDS2017 by using corrected versions.

Reading between the lines

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

An extension the paper leaves implicit is a community-maintained registry with standardized metadata for every NID dataset; the survey's 13-property protocol and continuity metric could serve as its schema.
If the popularity analysis were widened to journals and lower-tier venues, the conclusion that DARPA-based datasets are no longer dominant might weaken, because earlier surveys spanning longer time periods report the opposite pattern.
A concrete next test is to re-evaluate a published NIDS result on the corrected CIC-IDS2017 and CSE-CIC-IDS2018 versions to see whether performance gaps between methods change; the paper documents the corrections but does not quantify their effect on rankings.

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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

This is the survey the NIDS community has needed since Ring et al. (2019): an up-to-date, manually checked catalog of 89 public datasets with 13 properties per dataset, shared analysis notebooks, and a thoughtful taxonomy of data limitations. The authors actually downloaded the data rather than trusting documentation, which is real work and it shows. The trends analysis (more datasets, more specialization, emulation dominant) and the best-practices sections (selection, creation, usage) are concrete and will save people time and errors. I would cite this as the current reference for dataset selection.

The reader's conditional verdict is about right, and I agree with the main reservation. The popularity analysis in Section 5.4 rests on 45 papers from CORE A*/A security conferences, and the paper itself concedes (explanation b) that the venue choice drives the conclusion that DARPA-era datasets are dead. That is a directional sample: top-tier review processes reward novelty, so observing NSL-KDD used three times there does not establish field-wide unawareness. The 'custom data' finding is also explained partly by task diversity, which the unawareness narrative never rules out. This is a genuine limitation, but not a fatal one — the census itself supports the scarcity-to-discoverability shift independently of the 45-paper sample, and the authors are transparent about their sampling.

Minor soft spots worth fixing before publication: the notation in Table 4 is inconsistent ('1,18M pkts' uses a comma as a decimal separator while '13,000M' uses it as a thousands separator, and the unit definitions in Section 4.3 have typos like 'kilo (k) =106'). The paper also disclaims being an exhaustive list in Section 2.1, which is honest but means the 'largest census' claim should be read as 'largest among published surveys,' not as complete coverage. I could not audit the SLR appendix from the provided text, but the in-text description and the shared notebooks make the core methodology plausible and reproducible.

For the right reader — anyone selecting NID data, designing a dataset, or benchmarking a detector — this is a high-value reference. It deserves a serious referee and, after minor revisions, publication. The authors should either broaden the popularity sample or explicitly frame those conclusions as top-venue trends, and clean up the table notation.

Referee Report

3 major / 5 minor

Summary. The paper presents a systematic literature review of 89 publicly available network intrusion detection (NID) datasets published up to 2023. For each dataset the authors manually download and analyze the data and extract 13 properties spanning general information, data nature, volume, network properties, and evaluation. The survey is organized around a general/special-purpose taxonomy, includes a popularity analysis of datasets used in 45 papers from CORE A*/A security conferences (2020–2023), and closes with recommendations for dataset selection, creation, and usage, as well as future research directions. The paper's central narrative is that data scarcity is no longer the primary obstacle; rather, datasets exist but the community is largely unaware of them and lacks best-practice guidance for creating and using them.

Significance. If the census and the popularity findings hold, this is a genuinely useful contribution: it is the most extensive comparative catalog of NID datasets to date, and it goes beyond enumeration by providing a taxonomy, a 13-property extraction protocol, a limitations framework, and practical recommendations. The authors deserve credit for downloading and analyzing the datasets rather than relying on documentation alone, for sharing their analysis notebooks on GitHub, for documenting exclusions, and for making the popularity-analysis protocol transparent. The paper also correctly emphasizes reproducibility problems, labeling issues, and the absence of standardized train-test splits, which are real and recurring problems in the field. The main risk is that the field-level conclusion about community unawareness rests on a narrow venue sample, and the paper's own alternative explanations for low public-dataset uptake are not fully ruled out.

major comments (3)

[Section 5.4, Fig. 4 and Abstract/Section 1] The popularity analysis is based on only 45 papers from CORE A*/A security conferences published 2020–2023, and explanation (b) in Section 5.4 concedes that this venue choice selects for novelty and rigorous evaluation. Consequently, observing NSL-KDD used three times and KDD'99 once in this sample cannot, by itself, support the field-level claim in the Abstract and Section 1 that 'the community is largely unaware' of newer datasets and that DARPA-based datasets are no longer prevalent. A top-tier venue sample is precisely the population most likely to have abandoned DARPA-era data. In addition, the same section attributes the dominance of Custom data to 'the diversity of NID tasks,' which is a competing explanation for the low uptake of the catalogued datasets; the paper does not rule out this explanation. I recommend either broadening the popularity sample (e.g., including journals and lower-tier venues) or explicitly reframing the unawareness conclusion as limited to contemporary top-tier NIDS research.
[Section 4.3 and Table 4] The Size column in Table 4 mixes decimal and thousands separators inconsistently. Section 4.3 states that a comma represents a delimiter between thousands, but entries such as '1,18M pkts' for CSE-CIC-IDS2018 and '4,49M pkts' for CICIoT2023 are not interpretable under that rule and appear to use a comma as a decimal separator. Because the comparative table is the paper's core artifact, these inconsistencies undermine the reliability of the census data and should be corrected by normalizing all numeric entries to a single convention and re-verifying the affected rows.
[Section 1, Contribution 1; Section 2.2; Section 5.3] The load-bearing claim that this survey covers 'the largest number of publicly available NID datasets (89)' cannot be independently verified from the manuscript alone. Section 5.3 refers to a complete list of exclusions and reasons on GitHub, and Section 2.2 says the SLR protocol is detailed in Appendix A, but the paper should provide the full search string, the inclusion/exclusion criteria, and the exclusion list in a permanent archival form. Without this, the 'largest' claim remains a self-assessment rather than a reproducible result.

minor comments (5)

[Section 4.3, Eq. (1)] The epsilon value of 1% of the dataset's absolute time span is stated without a sensitivity analysis; a brief note on how the cont./discont. classification would change for alternative thresholds would strengthen the methodology.
[Table 1] The metric suffix definitions 'kilo (k)=106 and mega (M)=109' are ambiguous because the superscripts appear to be lost; they should read 10^3 and 10^6, respectively.
[Section 3.2, item 11] The heading 'Methodically wrong data handling' is likely a typo for 'Methodologically' or 'Procedurally'; please check and correct.
[Section 5.2.1] The phrase 'are the among the most prevalent' should read 'are among the most prevalent.'
[Section 5.5.2] The percentages reported for normal-traffic origins (17% real, 8% real+emulated, 71% solely emulated, and 8% other) appear to sum above 100%; please clarify whether categories overlap or whether one percentage is misreported.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the survey's catalog and popularity analysis are empirically anchored, not derived from its own assumptions.

full rationale

The paper is a systematic literature review, not a derivation. Its main claims — that 89 public NIDS datasets exist and that data scarcity is no longer the primary issue — are supported by a documented SLR process (Section 2 and Appendix A), manual download and analysis of datasets (Section 4.3), and a transparent popularity analysis of 45 top-tier papers (Section 5.4). The only formal equation, D = sum τ_i for τ_i ≤ ε in Section 4.3, is a definition of actual capture duration, not a fitted prediction; it does not feed back into a conclusion about dataset quality or popularity. The popularity conclusions are empirical counts, not forced by any normalization or fitting. The self-citation to Goldschmidt and Kučera (2024) in Section 5.5.3 supports a side observation about packet-based approaches and is not load-bearing; it does not justify the survey's catalog, taxonomy, or recommendations. The potential concern that the Section 5.4 conclusions depend on a CORE A*/A venue sample is a sampling-bias / external-validity issue, not circularity: the 45-paper corpus is an independent input, and the authors explicitly acknowledge the venue choice (explanation b). Similarly, the disclaimer in Section 2.1 that the survey is not exhaustive tempers scope but does not make any claim equivalent to its inputs. No self-definitional, fitted-input-as-prediction, or self-citation-chain circularity is present.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

No invented physical or conceptual entities are introduced, and no derived quantity is fitted to data. The hand-chosen epsilon and network-size thresholds affect property labels but not the main conclusion that datasets exist and are underused. The main assumptions concern completeness of the SLR search and representativeness of the popularity sample.

free parameters (2)

epsilon (temporal gap threshold) = 1% of dataset time span
Defined in Section 4.3 and used in Equation 1 to classify each dataset as continuous, discontinuous, or periodic. The threshold is hand-chosen without an external justification and directly affects the reported Duration property for all 89 datasets.
Network size class boundaries = small 0-99, medium 100-499, large 500+ hosts
Defined in Section 4.4. These hand-chosen thresholds determine the Network type property and influence the trend analysis on testbed sizes.

assumptions (4)

domain assumption The search and selection process in Appendix A correctly identifies all relevant public NID datasets published through 2023.
The central census claim depends on the completeness of the systematic literature review. The detailed protocol is referenced as Appendix A, which is not visible in the reviewed version.
domain assumption Documentation and downloaded data are an accurate basis for extracting the 13 properties.
The authors manually inspected data and documentation as described in Sections 4 and 5. If documentation is incomplete or downloaded data are corrupted or modified, the property values may be wrong.
ad hoc to paper A 1% gap threshold is a meaningful criterion for temporal continuity.
Introduced in Section 4.3 to distinguish continuous from discontinuous captures. It is transparent and consistently applied, but it is a methodological choice without independent validation.
domain assumption Forty-five papers from CORE A*/A security conferences in 2020-2023 represent contemporary state-of-the-art NIDS research.
Used in the popularity analysis in Section 5.4. If the intended population is the full NIDS literature, the usage rankings and conclusions about DARPA datasets could differ.

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Cite this review

Pith. "Pith review of Network Intrusion Datasets: A Survey, Limitations, and Recommendations." pith.science (2026). https://pith.science/paper/4LGJK2UY

@misc{pith2026250206688,
  author       = {Pith},
  title        = {Pith review of: Network Intrusion Datasets: A Survey, Limitations, and Recommendations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4LGJK2UY}},
  note         = {Machine review of arXiv:2502.06688}
}

read the original abstract

Data-driven cyberthreat detection has become a crucial defense technique in modern cybersecurity. Network defense, supported by Network Intrusion Detection Systems (NIDSs), has also increasingly adopted data-driven approaches, leading to greater reliance on data. Despite the importance of data, its scarcity has long been recognized as a major obstacle in NIDS research. In response, the community has published many new datasets recently. However, many of them remain largely unknown and unanalyzed, leaving researchers uncertain about their suitability for specific use cases. In this paper, we aim to address this knowledge gap by performing a systematic literature review (SLR) of 89 public datasets for NIDS research. Each dataset is comparatively analyzed across 13 key properties, and its potential applications are outlined. Beyond the review, we also discuss domain-specific challenges and common data limitations to facilitate a critical view on data quality. To aid in data selection, we conduct a dataset popularity analysis in contemporary state-of-the-art NIDS research. Furthermore, the paper presents best practices for dataset selection, generation, and usage. By providing a comprehensive overview of the domain and its data, this work aims to guide future research toward improving data quality and the robustness of NIDS solutions.

Figures

Figures reproduced from arXiv: 2502.06688 by the authors.

**Figure 2.** Limitations of datasets for network intrusion detection. As illustrated, [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Taxonomy of datasets for Network Intrusion Detection based on their [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 5.** Figure 5: The number of NID datasets by the year of publication. As shown, [PITH_FULL_IMAGE:figures/full_fig_p023_5.png]

**Figure 6.** Figure 6: The process of creating a NID dataset. The ten steps are further grouped into four phases – preparation, data collection, post-processing, and publication. [PITH_FULL_IMAGE:figures/full_fig_p027_6.png]

**Figure 7.** Figure 7: Future NID data research and development directions. We divide them into areas focused on generating, processing, validating, and publishing the data. [PITH_FULL_IMAGE:figures/full_fig_p031_7.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

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