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REVIEW 5 major objections 5 minor 90 references

An investigation into the performances of the Current state-of-the-art Naive Bayes, Non-Bayesian and Deep Learning Based Classifier for Phishing Detection: A Survey

T0 review · 5 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A survey of recent phishing-detection studies finds Naive Bayes is the weakest URL-based classifier, with Random Forest and Decision Tree on top.

desk verdict A survey with a plausible big-picture conclusion but a central ranking that is not supported by its own meta-analytic method. read the letter →

arxiv 2411.16751 v1 pith:GIGVJ33X submitted 2024-11-24 cs.CR cs.AIcs.LG

Tosin Ige , Christopher Kiekintveld , Aritran Piplai , Amy Waggler , Olukunle Kolade , Bolanle Hafiz Matti This is my paper · ORCID
classification cs.CRcs.AIcs.LG
keywords phishingdetectionNaiveBayesRandomForestdeeplearningURLfeaturesaccuracycomparisontwo-stagesurvey
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 paper sets out to settle a practical question: among current machine-learning and deep-learning classifiers trained on URL properties, where does Naive Bayes actually stand? By pooling the accuracy reported in roughly five years of phishing-detection studies and averaging per classifier, it claims that Naive Bayes is the weakest of the ten methods compared, with mean accuracy around 80.4%, while Random Forest leads at about 97.1%. A sympathetic reader would care because Naive Bayes is a common lightweight baseline in security products, and a 16-point average gap is large enough to change deployment choices. The paper also argues that URL-only models are fundamentally limited because attackers can craft 'friendly' URLs that evade controllable URL features, and it proposes a two-stage detector plus a regularized Bayes variant to close the gap.

What carries the argument

The argument is carried by mean-accuracy aggregation: the paper converts each cited study's reported accuracy into a per-classifier average, treating those numbers as commensurable evidence of real-world performance. Around that device it builds a second distinction, controllable versus uncontrollable URL properties, which explains why URL-trained models fail against attacker-crafted URLs and against young legitimate domains. The proposed two-stage pipeline (Random Forest for URL screening, CNN for page content) and the regularized Bayes rule are the paper's forward-looking mechanisms.

What would settle it

Compute mean accuracy for each classifier using only studies that share one public dataset and identical preprocessing, and check whether Naive Bayes remains the lowest and Random Forest the highest; if the order changes or the margin collapses, the paper's ranking is an artifact of averaging incomparable studies.

Watch

Extended reading notes

Core claim

The central claim is a performance ranking for URL-properties-based phishing detection, computed as mean accuracy across recent studies: Random Forest (97.1%), Decision Tree (95.2%), CNN (94.2%), and XGBoost (94.1%) are the top four, while Naive Bayes (80.4%), SVM (89.4%), and RNN (91.6%) are the bottom three. The paper further claims the poor showing of Naive Bayes follows from its independence assumption, which rarely holds for URL features, and that both Bayesian and non-Bayesian URL-based models share a vulnerability to 'friendly' URLs that defeat controllable URL properties. It concludes with two remedies: a two-stage model in which Random Forest screens the URL and, if the site looks legitimate, a CNN classifies scraped page content; and a regularized Bayes rule that accounts for feature correlation and distribution shape.

Load-bearing premise

The ranking assumes that accuracy numbers reported in different studies, on different datasets with different preprocessing and class balances, can be averaged as if they measured the same thing.

Editorial extensions

If this is right

  • If the ranking holds, teams building URL-only phishing detectors should prefer Random Forest or XGBoost over Naive Bayes as the first-line classifier.
  • A two-stage design that checks the URL first and scrapes page content only when the URL looks benign could cut false positives on newly registered legitimate sites.
  • Regularizing the Bayes rule to account for correlated features would give Naive Bayes variants a path to competitiveness without abandoning their simplicity.
  • URL-only detection is unlikely to be sufficient on its own: attacker-controlled URL properties can be manipulated, so content and image signals are needed.
  • Because accuracy alone can be inflated by imbalanced data, the paper's choice of mean accuracy across studies is itself a pragmatic compromise.

Reading between the lines

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

  • If the pooled accuracies are distorted by uneven dataset difficulty, the absolute gap may not transfer to a new deployment even if the ordinal ranking does.
  • A natural testable extension is to run all ten classifiers on a single shared phishing URL dataset with the same feature set and class balance; that would separate classifier ability from study-level confounds.
  • The paper's controllable-versus-uncontrollable URL distinction suggests a concrete adversary model: an attacker who edits URL length, hostname, and path tokens can evade models trained on those features, which may explain why high laboratory accuracy has not stopped phishing.
  • The regularized Bayes proposal points toward a family of correlation-aware Bayes variants; their success would depend on whether the added correlation terms remain cheap enough for real-time URL screening.
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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 surveys machine learning and deep learning classifiers for phishing detection, grouping methods into Naive Bayes variants, non-Bayesian classifiers, and deep learning models. It aggregates published accuracy results in Tables II and III and computes mean accuracies per classifier. The central empirical claim, stated in Section V, is that Random Forest, Decision Tree, CNN, and XGBoost have the top four mean accuracies (97.1%, 95.2%, 94.2%, 94.1%) for URL-property-based phishing detection, while Naive Bayes, SVM, and RNN have the worst three (80.4%, 89.4%, 91.6%). The paper also proposes a two-stage prediction model (Random Forest followed by CNN) and a regularized variant of the Bayes rule to improve Naive Bayes performance.

Significance. If the ranking were valid, it would provide useful guidance for practitioners selecting classifiers for URL-based phishing detection, and the two-stage proposal would be a plausible direction for future work. The paper also makes a fair point that accuracy alone is not a sufficient evaluation metric and that dataset imbalance, preprocessing differences, and heterogeneous data sources complicate any comparison across studies. However, the central contribution is the ranking in Table III and Section V, and that ranking rests on a pooling methodology that the authors themselves acknowledge, in Section IV, is threatened by the very factors they list. The paper does not provide machine-checked proofs or reproducible code; its empirical content is a hand-aggregated table of published numbers without statistical controls. The survey also usefully catalogs limitations of existing URL-based detectors, especially the vulnerability to so-called friendly URLs and the false-positive problem for newly registered legitimate domains.

major comments (5)
  1. [Table III and Section V] The central ranking is derived by averaging accuracy values from heterogeneous studies without establishing commensurability. The primary studies differ in dataset source and size (e.g., public UCI data in [61] versus internally generated or non-public data in [53] and [60]), in class balance, in preprocessing (removal vs. replacement of null values), and in the exact task formulation. Section IV explicitly lists imbalance, bias, preprocessing, and author error as accuracy distorters, yet no correction, stratification, or sensitivity analysis is applied before pooling. The resulting means are therefore not a defensible basis for the claim that Random Forest, Decision Tree, CNN, and XGBoost are the top four and Naive Bayes, SVM, and RNN the worst three. A single outlier can change the ordering: for XGBoost, dropping the 70.34 value from [47] raises its mean from about 94.2 to about 96.5, above CNN. No confidence intervals or significance tests are reported, so the precise ordering in Section V is unsupported.
  2. [Table III, CNN/RNN rows] The claim in Section V is specifically about URL properties-based phishing detection, but several deep learning entries included in the averaged rows are not trained on URL-only features. For example, [19] uses HTML and text obtained from web pages, [86] and [77] involve image or content-based inputs, and [8] uses a broader deep-learning pipeline. Mixing feature types invalidates the comparison across classifier categories, because the performance differences may reflect input modalities rather than the classifier family. The table would need to separate URL-only studies from content/image-based studies before any classifier ranking can be drawn.
  3. [Section IV and Section V, two-stage proposal] The proposed two-stage model (Random Forest on URL properties, then CNN on web-scraped content) is motivated by the aggregated ranking, so it inherits the pooling problem. The choice of Random Forest for the first stage is justified by a mean accuracy of 97% from Table III, but if that mean is not commensurable, the design rationale is not established. The proposal is also not evaluated in any experiment; it is a suggestion rather than a validated contribution. The paper would need at least a small pilot evaluation or a clearly stated feasibility argument to make this a load-bearing part of the survey.
  4. [Section IV, mean accuracy rationale] The text says mean accuracy was adopted 'to counter the effect of' uncertainty in dataset quality, imbalance, preprocessing, and author error. Averaging does not counter these effects; it merely propagates them into a single number and then treats dissimilar numbers as comparable. The manuscript contains its own admission that accuracy alone is not a perfect metric and that imbalance can 'tilt the accuracy in favor or against a classifier.' This is an internal inconsistency: the acknowledged limitations are not addressed by the chosen methodology, and no alternative analysis (e.g., stratified means by dataset, paired comparisons within studies that report multiple classifiers, or rank-based aggregation) is provided.
  5. [Table III and references [35], [36]] The survey aggregates values from many independent studies, which is appropriate, but two of the author's own prior papers ([35] on multinomial Naive Bayes and [36] on Bayesian variants for network intrusion detection) are cited in Section III as evidence for the limitations of Naive Bayes. Those papers are not phishing detection studies, so their inclusion in the narrative about phishing performance is not relevant, and their use in the motivation for the proposed regularized Bayes rule gives the proposal a weaker evidential base than the text suggests. This is a minor self-citation concern rather than a circularity problem, but it should be cleaned up.
minor comments (5)
  1. [Abstract and Introduction] The abstract contains an ungrammatical sentence: 'we also made a series of proposals on how the performance of the under-performing algorithm can improved' should read 'can be improved.' The Introduction also repeats several statistics with obvious inconsistencies (e.g., 4.8 million vs. 1.6 million vs. 4.7 billion dollars in adjacent sentences); these should be reconciled against the cited sources.
  2. [Figure 1 and reference placeholders] There are unresolved citation placeholders such as ' [ ?]' in the Introduction and ' [ ?]' in the CNN subsection. Figure 1 is said to show phishing statistics from 2013 Q3 to 2022 Q3, but the reader is never told what the plotted quantity is or where the data come from.
  3. [Section IV, controllable/uncontrollable properties] The classification of URL properties is confusing. The text first lists length of URL, hostname length, average word, and character repetition as controllable by attackers, then later proposes using 'Uncontrollable properties like the length of the URL, length of the hostname, average URL, longest word, character repetition...' The two lists are nearly identical, so the distinction between controllable and uncontrollable properties is not made clear. Please revise the terminology and the example properties so the intended contrast is precise.
  4. [Section III, SVM motivation] The description of SVM states that two theories must hold before the suitability of SVM can be determined, namely high-dimensional input space and linearly separable categories. This is an oversimplification; SVMs with kernels are specifically designed to handle non-linearly separable data, and the claim as written is misleading. A more standard description would improve the survey's accuracy.
  5. [Minor typographical and formatting issues] There are numerous typos and formatting errors throughout, including 'Munitinomial Naive Bayes' in the index terms, 'Nave Bayes' in the text, 'UCL' for the UCI dataset in Table I, and inconsistent capitalization (e.g., 'Bayesian' vs. 'Bayes'). The table captions are also inconsistent: Table I is described as 'LIMITATIONS' but contains a mix of summaries and limitations, and Tables II and III use different column layouts for essentially the same information. A careful proofread is needed.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the Section V classifier ranking is an arithmetic summary of Table III's independently sourced accuracy values, and the two self-citations are qualitative asides, not load-bearing inputs.

full rationale

The paper's central claim in Section V — that Random Forest, Decision Tree, CNN, and XGBoost have the top four mean accuracies (97.1%, 95.2%, 94.2%, 94.1%) while Naive Bayes, SVM, and RNN have the worst three (80.4%, 89.4%, 91.6%) — is obtained by taking the arithmetic means of the accuracy values listed in Table III. Those values are attributed to 11-15 external studies per classifier, and no row of Table III draws on the authors' own prior papers. The means are descriptive summaries of the collected results, not fitted parameters that are later re-predicted, so no step reduces to its own input. The only self-references, [35] and [36], appear in Sections III-A and IV to support qualitative statements that Multinomial and Gaussian Naive Bayes underperform for NLP and anomaly-detection tasks; those statements are not part of the Table III calculations, and deleting them would leave every mean and the resulting ranking unchanged. The proposed two-stage Random Forest plus CNN model is a design choice motivated by the same survey means, not a prediction validated on those means. The skeptic's concern about pooling heterogeneous datasets, class imbalances, and varying feature types is a validity or correctness critique of the averaging methodology, not an instance of circular derivation. Accordingly, the paper is not circular, and the minor self-citations do not raise the circularity score.

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

The survey's conclusions rest on the unstated comparability of accuracy numbers gathered from different papers, and on the adequacy of accuracy as a summary metric. No new physical or algorithmic entity is introduced; the two-stage model is an untested architecture proposal rather than an invented entity.

assumptions (3)
  • domain assumption Reported accuracy scores across the cited studies are commensurable and can be pooled by simple averaging.
    The authors state 'we decided to use mean accuracy as a measure of performance evaluation' (Section IV) despite noting differences in dataset quality, imbalance and preprocessing.
  • domain assumption The selected papers represent the state of the art for phishing detection over the past five years.
    The inclusion criteria for Tables II and III are never specified, so the sample may not be representative.
  • ad hoc to paper Accuracy alone is an adequate metric for ranking phishing detectors.
    The authors adopt accuracy because most papers report it (Section IV), but they acknowledge it is not perfect and can be skewed by imbalance.

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Cite this review

Pith. "Pith review of An investigation into the performances of the Current state-of-the-art Naive Bayes, Non-Bayesian and Deep Learning Based Classifier for Phishing Detection: A Survey." pith.science (2026). https://pith.science/paper/GIGVJ33X

@misc{pith2026241116751,
  author       = {Pith},
  title        = {Pith review of: An investigation into the performances of the Current state-of-the-art Naive Bayes, Non-Bayesian and Deep Learning Based Classifier for Phishing Detection: A Survey},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GIGVJ33X}},
  note         = {Machine review of arXiv:2411.16751}
}
read the original abstract

Phishing is one of the most effective ways in which cybercriminals get sensitive details such as credentials for online banking, digital wallets, state secrets, and many more from potential victims. They do this by spamming users with malicious URLs with the sole purpose of tricking them into divulging sensitive information which is later used for various cybercrimes. In this research, we did a comprehensive review of current state-of-the-art machine learning and deep learning phishing detection techniques to expose their vulnerabilities and future research direction. For better analysis and observation, we split machine learning techniques into Bayesian, non-Bayesian, and deep learning. We reviewed the most recent advances in Bayesian and non-Bayesian-based classifiers before exploiting their corresponding weaknesses to indicate future research direction. While exploiting weaknesses in both Bayesian and non-Bayesian classifiers, we also compared each performance with a deep learning classifier. For a proper review of deep learning-based classifiers, we looked at Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), and Long Short Term Memory Networks (LSTMs). We did an empirical analysis to evaluate the performance of each classifier along with many of the proposed state-of-the-art anti-phishing techniques to identify future research directions, we also made a series of proposals on how the performance of the under-performing algorithm can improved in addition to a two-stage prediction model

Figures

Figures reproduced from arXiv: 2411.16751 by the authors.

Figure 1
Figure 1. Phishing statistics from 2013 Q3 to 2022 Q3. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 3
Figure 3. Comparative Analysis of State-of-the-art Phishing Algorithm for [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

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Pith tools

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