From Past to Present: A Survey of Malicious URL Detection Techniques, Datasets and Code Repositories

Jianguo Sun; Yanbin Wang; Yanqiu Yu; Ye Tian

REVIEW 4 major objections 6 minor 255 references

From Past to Present: A Survey of Malicious URL Detection Techniques, Datasets and Code Repositories

T0 review · 4 major / 6 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read A modality-based taxonomy reorganizes the malicious URL detection literature, and a curated registry of datasets and open-source code is offered as the field's first unified benchmark for reproducible evaluation.

desk verdict Useful modality-organized survey with recent LLM coverage, but the 'unified benchmark' claim is unsupported and the resource tables contain concrete misattributions. read the letter →

arxiv 2504.16449 v2 pith:277BKY2Q submitted 2025-04-23 cs.CR cs.LG

Ye Tian , Yanqiu Yu , Jianguo Sun , Yanbin Wang This is my paper · ORCID

classification cs.CRcs.LG

keywords maliciousURLdetectionmultimodalityphishingdatasetsopen-sourceimplementationslargelanguagemodelsgraphneuralnetworksbenchmarking

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 survey argues that malicious URL detection research should be organized by the data modality each method consumes—URL text, HTML structure, JavaScript behavior, visual appearance, or several at once—rather than by the machine-learning algorithm family used. It claims that prior reviews miss this distinction, ignore Transformer, graph-neural-network, and large-language-model defenses, and leave researchers without centralized lists of datasets and open-source code. To fix that, it assembles a modality-based taxonomy, a curated table of public datasets from 2016 to 2024, a table of open-source implementations from 2013 to 2025, and a live GitHub repository, calling the result the first unified benchmark for reproducible evaluation. A sympathetic reader would care because reproducible baselines are what would let the field know whether new detection methods actually beat old ones.

What carries the argument

The carrying mechanism is a hierarchical, modality-based taxonomy defined in Section 3: URL-based, HTML-based, JavaScript-based, visual-based, and hybrid-modality detection, each with its own technical principles and representative algorithms. This taxonomy is what turns a scattered literature into a structured map. Its companion machinery is the curated registry—Table 3 listing open-source implementations with links and Table 4 listing public datasets with sources, sizes, and access methods—together with the maintained GitHub repository that keeps the registry current. The taxonomy does the analytical work; the registry does the benchmarking work.

What would settle it

Pick a random sample of the repository URLs in Table 3 and the dataset access links in Table 4 and follow them: if a substantial fraction resolve to dead pages, relocated projects, or repositories containing code that does not match the cited algorithm, the 'unified benchmark for reproducible evaluation' claim is not met. A second check would take two papers the taxonomy assigns to different modalities and show that the same detection system could be described equally well in both categories, indicating the taxonomy's boundaries are not well-defined.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that the entire malicious-URL detection literature can be re-read through a modality lens that classifies each method by its primary data source: lexical URL features, HTML document features, JavaScript execution features, visual page features, and hybrid combinations. Within each category it surveys techniques from blacklists and heuristics through classical machine learning up to Transformers, GNNs, and LLM-based prompting and fine-tuning. It further claims to establish the first unified benchmark for reproducible evaluation by compiling 15 open-source code repositories and 14 public datasets, and it distills design principles for real-world deployment from systems like Monarch. The conclusion is that this consolidated resource framework enables meaningful cross-method performance comparisons and tracking of genuine methodological advancement.

Load-bearing premise

The paper's central benchmark claim assumes that the curated dataset and code-repository links are correct, complete, and stable, and that each cited work can be assigned unambiguously to exactly one modality; it verifies neither the links nor the assignment rules.

Editorial extensions

If this is right

A researcher choosing a baseline for a new malicious-URL method can now start from the curated repository list in Table 3 instead of hunting through papers for code links.
The modality taxonomy predicts which information channels—URL, HTML, JavaScript, visual—are already saturated and which combinations remain underexplored, guiding new multimodal fusion work.
LLM- and Transformer-based defenses, which earlier reviews omitted, are folded into the same modality map, so their relationship to classical methods becomes comparable.
The design principles in Section 6 give product teams a concrete checklist (accuracy, speed, scalability, adaptability, flexibility) for turning detection research into a service.
If the curated registry stays current through the GitHub repository, cross-paper performance comparisons become reproducible rather than anecdotal.

Reading between the lines

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

Editorial: the claim of being the 'first unified benchmark' depends on the registry staying alive; a survey can seed a benchmark, but only verified, archived copies of datasets and code would make the benchmark durable.
Editorial: the taxonomy's five modalities could be extended to a sixth—network-traffic and user-behavior features—which the paper mentions but deliberately leaves out, suggesting a natural expansion for a follow-up survey.
Editorial: a testable extension would be to run a single classifier from each modality category on a common dataset collection and report cross-modal accuracy, which would turn the curated registry into an actual leaderboard rather than a list of links.

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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

Two things to know. First, the genuinely useful part is the modality-based taxonomy plus the consolidated dataset and code tables; the paper does bring in recent Transformer/GNN/LLM work that earlier surveys miss. Second, the headline claim—'first unified benchmark for reproducible evaluation'—is not supported by anything in the paper, and the curation has concrete errors. I'd treat it as a useful starting point, not a benchmark.

What it does well: organizing the literature by data modality (URL, HTML, JavaScript, visual, hybrid) is a reasonable and readable frame, and the coverage of LLM-based detection is a real gap-filler. Tables 2 and 4 give a compact picture of performance numbers and available datasets. For a newcomer, this is a convenient map.

Where it goes soft: no evaluation protocol exists. A benchmark needs fixed splits, a shared harness, metrics computed by the same code, and baseline results. This paper provides none of those; the GitHub repo is described as 'ongoing curating,' not as a harness. So the concluding claim overstates what was delivered.

The stress-test note is right about Table 3. Entry [182] points to github.com/MjafarMashhadi/Haplophysh, but reference [182] is Saxe and Berlin's expose paper. Entry [228] points to Microsoft/CNTK, but [228] is Wei et al.'s URL phishing detector. Those are not subtle distinctions. Section 7.1.1 also cites a reference [404] that does not exist. None of this is load-bearing for the survey's descriptive sections, but it matters because the resource list is the one concrete deliverable.

The Arabic section sits oddly inside the survey: it is not integrated into the modality taxonomy and the prose quality drops noticeably. It could stay with a clear rationale, or it could be cut.

Bottom line: for someone entering the area, this is a useful map with good recent coverage. Cite it if you need a pointer to the LLM/Transformer literature, but don't cite it as a benchmark, and verify any links before relying on them. I would send it to peer review, with a firm request to soften the benchmark claim, fix the citation and link errors, and make the Arabic section cohere.

Referee Report

4 major / 6 minor

Summary. This survey reviews the field of malicious URL detection, organizing methods by data modality (URL, HTML, JavaScript, visual, and hybrid) and covering traditional machine learning, deep learning, Transformer/GNN, and LLM-based approaches. It also includes a short section on Arabic-language detection studies, tables of published algorithms with code repositories, tables of public datasets, definitions of evaluation metrics, design principles for deployed systems, and a discussion of challenges and future directions. The authors claim that the paper establishes 'the first unified benchmark for reproducible evaluation' by curating datasets and open-source implementations, and they maintain a GitHub repository of resources.

Significance. If its resource collection were accurate and its claims appropriately bounded, the survey would be a useful entry point for researchers: the modality-based taxonomy is a reasonable organizing principle, the coverage of Transformer/GNN/LLM-based detectors goes beyond several earlier surveys, and Tables 3 and 4 could help practitioners locate datasets and code. The paper does not present machine-checked proofs, experimental results, or a runnable evaluation harness; its main contribution is curation and classification of literature. However, the central 'unified benchmark' claim is substantially overreaching relative to the content, and the curation contains concrete errors (misattributed repository links, a nonexistent reference number, and a citation mismatch). These issues are fixable in revision, so the paper has potential as a survey after correction and recalibration of its claims.

major comments (4)

[Section 8, Tables 3-4, companion repository] The conclusion asserts that the paper 'establish[es] the first unified benchmark for reproducible evaluation', but a reproducible benchmark requires fixed datasets with agreed train/test splits, a shared evaluation harness computing common metrics, and baseline results obtained by running that harness. The manuscript provides none of these: Section 5.1 lists repository URLs, Section 5.2 lists dataset sources, and the GitHub repository is described as 'ongoing curating datasets and open-source implementations' rather than as a benchmark harness. The claim should be softened to a curated resource collection unless the authors supply the actual harness, split definitions, metric implementations, and baseline results.
[Table 3] Table 3 contains misattributed code-repository entries that undermine the curation. Entry [182] links to https://github.com/MjafarMashhadi/Haplophysh, but reference [182] is Saxe and Berlin's 'expose' paper, which is not the Haplophysh project. Entry [228] links to https://github.com/Microsoft/CNTK, a general deep-learning framework, while reference [228] is Wei et al.'s URL phishing detector with a convolutional neural network. Because the stated purpose of Table 3 is to provide usable baseline implementations, these misattributions need to be corrected and every entry should be verified before the resource can support benchmarking claims.
[Section 7.1.1] Section 7.1.1 says that cloaking 'aims to evade the detection of phishing detection systems [404]', but the reference list contains no entry [404]; the subsequent discussion of server-side and client-side cloaking cites [243]-[249]. The in-text citation numbering appears corrupt. The authors must align the in-text citations with the final bibliography before resubmission.
[Section 2.2] Section 2.2 attributes TfidfVectorizer to reference [14], which is Wang's 2022 comparison study 'Malicious url detection an evaluation of feature extraction and machine learning algorithm' rather than the documentation or original source of TfidfVectorizer. This citation mismatch, together with the errors in Table 3 and Section 7.1.1, indicates that the bibliography has not been systematically verified. Please re-check all reference-citation pairs.

minor comments (6)

[Table 2] The Result column reports accuracy values obtained on different datasets and with different experimental protocols; without a dataset/column or a cautionary note, the numbers suggest direct comparability that does not exist.
[Section 5.2] The sentence 'The specific creation time, creator, and link information are shown in Table 3' should refer to Table 4, since Table 4 lists the datasets.
[Section 4] Several works reviewed in the Arabic section concern Arabic spam detection and fake news in social media rather than malicious URL detection (e.g., [213], [214], [216]); consider retitling the section or explicitly framing these as adjacent tasks that inform URL-centric detection.
[Figures 2 and 3] Figures 2 and 3 appear without in-text references, even though Figure 3 presents the proposed taxonomy; add appropriate callouts.
[Section 8] The phrase 'We rigorous coverage of Transformer, GNNs and LLMs' is an ungrammatical fragment, and the conclusion's claim of 'three high-impact directions' is not clearly aligned with the broader set of directions in Section 7.2.
[Section 3.1.2, PA/CW/AROW paragraph] The sentence 'ROW enhances the robustness...' should be 'AROW enhances the robustness...', and the stacked-matrix notation in Equation (13) should be reconciled with the separate gate equations (14)-(16).

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the survey derives no fitted quantity, and its self-citations are catalogue entries, not load-bearing evidence for a derived result.

full rationale

This manuscript is a survey and resource-curation paper rather than a derivation or empirical study, so the standard circularity patterns do not apply. There is no quantity fitted to data and then renamed as a prediction, no equation whose output is identical to its input by construction, and no uniqueness theorem imported from the authors' prior work to force a modeling choice. The paper's modality-based taxonomy is a classification scheme proposed by the authors; it organizes surveyed works by data modality, and calling it 'novel' is a novelty claim, not a derivational claim. The central conclusion that the paper 'establish[es] the first unified benchmark for reproducible evaluation' is unsupported, because Tables 3 and 4 provide curated links and the GitHub repository is described as 'ongoing curating datasets and open-source implementations' rather than a benchmark harness with splits, metrics, and baseline results. However, this is an overstatement or completeness/correctness weakness, not circularity: the claim does not reduce to its own inputs by construction, and the misattributed entries (e.g., Table 3 entry [182] pointing to a GitHub repository for Haplophysh while reference [182] is Saxe and Berlin's expose paper, and entry [228] pointing to Microsoft/CNTK while reference [228] is a CNN phishing detector) are curation errors, not circular reasoning. Similarly, the nonexistent reference [404] in Section 7.1.1 indicates a missing citation, not a self-referential derivation. The self-citations in the reference list (e.g., [74], [77], [78], [172], [234], [235] involving the corresponding author) are used only as surveyed/already-published works in the tables and narrative; none is invoked as the justification for a claimed result or as an external authority that forbids alternatives. They are therefore not load-bearing. Because the paper contains no fitted-input-as-prediction step and no self-citation chain supporting a central derived claim, the circularity score is 0.

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

The survey introduces no fitted parameters and no invented entities. It relies on external literature and online resources. The load-bearing assumptions are: (1) the modality taxonomy cleanly classifies the reviewed literature, and (2) the curated resource links are stable and complete. Both are asserted rather than demonstrated.

assumptions (2)

domain assumption The selected references can be consistently classified into the proposed modality categories (URL, HTML, JavaScript, Visual, Hybrid).
The taxonomy's validity depends on this assignment being unambiguous; the paper does not provide a coding protocol or demonstrate inter-rater consistency.
domain assumption The datasets and code repositories listed in Tables 3 and 4, including the GitHub repository URL, are stable, complete, and correctly attributed.
The intended 'unified benchmark' and the practical utility of the survey depend on these external resources being accessible and accurate; the paper provides no local archive or validation.

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Cite this review

Pith. "Pith review of From Past to Present: A Survey of Malicious URL Detection Techniques, Datasets and Code Repositories." pith.science (2026). https://pith.science/paper/277BKY2Q

@misc{pith2026250416449,
  author       = {Pith},
  title        = {Pith review of: From Past to Present: A Survey of Malicious URL Detection Techniques, Datasets and Code Repositories},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/277BKY2Q}},
  note         = {Machine review of arXiv:2504.16449}
}

read the original abstract

Malicious URLs persistently threaten the cybersecurity ecosystem, by either deceiving users into divulging private data or distributing harmful payloads to infiltrate host systems. Gaining timely insights into the current state of this ongoing battle holds significant importance. However, existing reviews exhibit 4 critical gaps: 1) Their reliance on algorithm-centric taxonomies obscures understanding of how detection approaches exploit specific modal information channels; 2) They fail to incorporate pivotal LLM/Transformer-based defenses; 3) No open-source implementations are collected to facilitate benchmarking; 4) Insufficient dataset coverage.This paper presents a comprehensive review of malicious URL detection technologies, systematically analyzing methods from traditional blacklisting to advanced deep learning approaches (e.g. Transformer, GNNs, and LLMs). Unlike prior surveys, we propose a novel modality-based taxonomy that categorizes existing works according to their primary data modalities (URL, HTML, Visual, etc.). This hierarchical classification enables both rigorous technical analysis and clear understanding of multimodal information utilization. Furthermore, to establish a profile of accessible datasets and address the lack of standardized benchmarking (where current studies often lack proper baseline comparisons), we curate and analyze: 1) publicly available datasets (2016-2024), and 2) open-source implementations from published works(2013-2025). Then, we outline essential design principles and architectural frameworks for product-level implementations. The review concludes by examining emerging challenges and proposing actionable directions for future research. We maintain a GitHub repository for ongoing curating datasets and open-source implementations: https://github.com/sevenolu7/Malicious-URL-Detection-Open-Source/tree/master.

Figures

Figures reproduced from arXiv: 2504.16449 by the authors.

**Figure 2.** Count the number of papers published on the topic of malicious URL detection (Web of Science Core Collection) over the past four years, the features used in the publications, and the percentage of algorithms used [PITH_FULL_IMAGE:figures/full_fig_p035_2.png] view at source ↗

**Figure 3.** The proposed taxonomy of malicious url detection : Preprint submitted to Elsevier Page 35 of 34 [PITH_FULL_IMAGE:figures/full_fig_p035_3.png] view at source ↗

Figures from the paper (4 more)

**Figure 4.** Figure 4: A general processing framework for Malicious URL Detection using Machine Learning [PITH_FULL_IMAGE:figures/full_fig_p036_4.png]

**Figure 5.** Figure 5: The Transformer model architecture : Preprint submitted to Elsevier Page 36 of 34 [PITH_FULL_IMAGE:figures/full_fig_p036_5.png]

**Figure 6.** Figure 6: LSTM unit structure and sequence expansion diagram [PITH_FULL_IMAGE:figures/full_fig_p037_6.png]

**Figure 7.** Figure 7: The brief history of Large Language Models : Preprint submitted to Elsevier Page 37 of 34 [PITH_FULL_IMAGE:figures/full_fig_p037_7.png]

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