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REVIEW 3 major objections 5 minor 74 references

A Comparative Analysis of DNN-based White-Box Explainable AI Methods in Network Security

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This paper argues that white-box explainable AI methods (LRP, Integrated Gradients, and DeepLift) match or beat black-box ones on five of six quality metrics for network intrusion detection.

desk verdict Useful evaluation pipeline for white-box XAI on NIDS, but the headline completeness claim is not supported by the paper's own tables and needs a major fix. read the letter →

arxiv 2501.07801 v1 pith:SQ4PPMEE submitted 2025-01-14 cs.CR cs.AI

Osvaldo Arreche , Mustafa Abdallah This is my paper · ORCID
classification cs.CRcs.AI
keywords white-boxXAInetworkintrusiondetectionLayer-wiseRelevancePropagationIntegratedGradientsDeepLiftevaluationmetricsrobustnesscompleteness
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 tries to establish that white-box explainable AI methods can be trusted for explaining DNN-based network intrusion detection. It evaluates Layer-wise Relevance Propagation, Integrated Gradients, and DeepLift on three intrusion datasets using six quality metrics, and reports that these methods score high on robustness and completeness and match or beat black-box methods (LIME and SHAP) on five of six metrics, with stability the only exception. If correct, security analysts could rely on explanations drawn from the model's own internals rather than on approximations when investigating alerts. The paper also cautions that all three methods still need improvement before production deployment.

What carries the argument

The mechanism is attribution by back-propagation. LRP and DeepLift push relevance scores backward through the network under conservation rules, while IG integrates gradients along a path from a baseline to the input. Because these computations use the model's internals, the paper treats them as inherently complete and as harder to fool than black-box approximations, and it evaluates them with six operationalized metrics: accuracy loss when top-ranked features are removed, thresholded sparsity of importance scores, overlap of top features across repeated runs, wall-clock time to generate explanations, detection of a biased feature under a two-model adversarial attack, and prediction-class change under perturbation of the top features.

What would settle it

For the same DNNs, restore the softmax layer and run the completeness test while perturbing all features (not just the top two) across 100 samples per class; if a substantial share of samples never changes prediction class, the paper's completeness claim is refuted. The RoEduNet-SIMARGL2021 Normal class with DeepLift, which the paper reports as 0 percent complete, would be a decisive place to look.

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Extended reading notes

Core claim

The central claim is that white-box XAI methods, because they derive explanations from the DNN's own weights and gradients, are more resilient to adversarial perturbation and more complete than black-box surrogates in the network intrusion setting. Across NSL-KDD, CICIDS-2017, and RoEduNet-SIMARGL2021, the authors report that LRP, IG, and DeepLift outperform or tie LIME and SHAP on descriptive accuracy, sparsity, efficiency, robustness, and completeness, and lose only on stability. The paper further claims that LRP is the best overall white-box method, IG the most robust, and DeepLift the most complete on CICIDS-2017, while cautioning that none of the three is production-ready.

Load-bearing premise

The paper assumes that white-box XAI methods are complete by definition because they access the model's internals, even though the DNNs used here omitted the final softmax layer and the empirical completeness test left many samples unchanged; if that theoretical transfer fails, the claim of high completeness collapses.

Editorial extensions

If this is right

  • Security analysts can prefer white-box explanations over LIME and SHAP for DNN-based intrusion detectors, because the paper reports white-box methods matching or beating black-box ones on five of six quality metrics.
  • IG and LRP produce explanations fast enough for practical monitoring (roughly a second or less even at 10,000 samples), whereas DeepLift's runtime grows steeply with sample count.
  • The open-sourced evaluation framework lets other researchers score additional XAI methods and datasets on the same six metrics, making XAI claims in intrusion detection directly comparable.
  • White-box XAI should not yet be deployed in production intrusion detection, because the adversarial attack still succeeds often and the completeness experiment was cut short by memory limits.
  • The best method depends on the dataset: the paper finds IG best for CICIDS-2017 overall, while LRP and DeepLift do better on RoEduNet-SIMARGL2021.

Reading between the lines

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

  • Editorial inference: the paper's own completeness tables undercut its theoretical completeness claim, since many cells are 0 to 43 percent; a fuller perturbation test would likely reveal that the reduced top-two-feature experiment was too weak.
  • Editorial inference: because the DNNs omit the softmax layer to accommodate the explanation software, accuracy and completeness numbers may not transfer to production classifiers; re-running with softmax-enabled models is a direct test.
  • Editorial inference: the robustness experiment uses a single engineered unrelated feature, so the reported resilience margin is an upper bound; subtler crafted perturbations could fool white-box methods too.
  • Editorial inference: a natural next step is to apply the same six-metric battery to newer white-box methods and to transformer-based IDS models to see whether the white-box advantage persists.
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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

3 major / 5 minor

Summary. The paper proposes an end-to-end framework for evaluating three white-box XAI methods (LRP, IG, DeepLift) on DNN-based intrusion detection models, using six metrics (descriptive accuracy, sparsity, stability, efficiency, robustness, completeness) across NSL-KDD, CICIDS-2017, and RoEduNet-SIMARGL2021. It also compares the white-box results with black-box XAI baselines (SHAP, LIME) reported in the authors' earlier work [25]. The authors conclude that white-box methods score high in robustness and completeness and match or surpass black-box methods in five of six metrics.

Significance. If the empirical claims were fully supported, the paper would be a useful step toward deploying white-box XAI in NIDS, and the open-sourced framework would support follow-up evaluations by the community. The paper's strengths are the breadth of its evaluation (three datasets, three white-box methods, six metrics) and the adaptation of the robustness attack from [26] to DNNs and white-box methods. However, the central completeness claim and the cross-method comparison rest on internal inconsistency and protocol mismatch described in the major comments; the current form does not yet substantiate the advertised conclusions.

major comments (3)
  1. [Section 6.6, Tables 9-11, Section 7, Section 8] the paper's central claim that white-box methods 'score high in robustness and completeness' is contradicted by its own empirical completeness results. For example, DeepLift has 0% completeness for RoEduNet Normal and PortScan (Table 9), IG has 0% for CICIDS Bot and 2% for Web Attack (Table 10), and LRP has 0% for CICIDS PortScan and Infiltration (Table 10). The text acknowledges that the reduced top-2 perturbation 'undermined its performance for this metric' and that the softmax omission 'probably impacts' Completeness (Section 7), yet the abstract and Section 8 nevertheless assert high completeness and superiority over black-box methods. The paper cannot use the same experiment as evidence for completeness when it also dismisses the experiment as invalid; the theoretical completeness argument from [17] is invoked without establishing that it applies to the softmax-free DNN models used here.
  2. [Section 6.8 compared with Section 6.6] the white-box versus black-box completeness comparison is not protocol-equivalent. The black-box baseline [25] perturbed the top-5 features, whereas the white-box experiment here perturbs only the top-2 features (the paper states this difference explicitly in Section 7). Because perturbing more features makes a class change more likely, the completeness scores are not directly comparable, and the conclusion that white-box methods 'matched or surpassed' black-box methods in completeness is not supported. The authors should either rerun the experiment with the same perturbation depth or restrict the comparison to descriptive, non-quantitative statements.
  3. [Section 6.1] the descriptive accuracy analysis lacks a random-removal baseline. The authors interpret accuracy drops after removing the top-k features as evidence of 'explainability power' and use these curves to rank LRP, IG, and DeepLift. Without a control condition in which k random features are removed, there is no way to attribute the observed accuracy changes to the quality of the XAI rankings rather than to the mere reduction of input dimensionality. This concern is particularly acute for the CICIDS-2017 LRP curve, where accuracy does not drop (and even increases) as top features are removed; the 'curse of dimensionality' explanation offered in Section 6.1 is post hoc and unfalsifiable without such a baseline.
minor comments (5)
  1. [Table 12 caption] The caption says 'Overall, SHAP provides the best performance compared to LIME for the six metrics analyzed in this work,' but this caption appears to be carried over from the authors' prior black-box paper [25]; the table actually summarizes DeepLift, IG, and LRP, and the caption should be corrected.
  2. [Section 5.3 vs Section 4.3 (C.1)] Section 5.3 states that for Descriptive Accuracy the removed-feature order is k=0,10,20,40,80, while the step-by-step algorithm in Section 4.3 (C.1) specifies k=(0,5,10,25,50,70); this inconsistency should be resolved.
  3. [References [21]] The citation for LRP in the introduction and Section 4.2 is given as [21], but reference [21] is Gunning and Aha's DARPA XAI program, not the Layer-wise Relevance Propagation paper; the correct LRP reference should be supplied.
  4. [Throughout] Several typographical issues remain, including 'white-bpx' in Section 6.2, 'auditory actions' in Section 3.6, and 'thrust' for 'trust' in Section 6.7; a careful proofreading pass is needed.
  5. [Introduction and Declarations] The GitHub URL contains a space ('XAI Whitebox') and should be URL-encoded to be accessible.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the comparative claim rests on external benchmarks and no equation reduces to a fitted input.

full rationale

I find no circular step in this paper. The six evaluation metrics are defined operationally in Section 4.3, and the reported measurements (descriptive accuracy curves, sparsity, stability, efficiency, robustness bar plots, completeness tables) are empirical outputs, not fitted parameters renamed as predictions. The comparison with black-box XAI uses the authors' prior work [25] as the baseline; although this is a self-citation, it is a published, externally checkable benchmark (same metrics, different XAI methods) and is not a constant fitted in the present paper, so it qualifies as independent support under the stated rules. The claim that white-box methods are 'inherently complete' is explicitly imported from [17], which is not authored by the present authors; the mismatch between that theoretical premise and the low empirical completeness values in Tables 9-11 (e.g., DeepLift 0% on RoEduNet Normal/PortScan, IG 0% on CICIDS Bot, LRP 0% on CICIDS PortScan/Infiltration) is a correctness/transferability risk, not a circularity, because the paper does not derive those tables from the premise or vice versa. The acknowledged softmax removal and reduced top-2 perturbation protocol are limitations that affect fairness of comparison, but they do not make any equation self-referential. No prediction in this paper reduces by construction to its inputs.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central evaluation depends on protocol choices (perturbation depth, number of runs, sample counts) that are not derived from theory. The completeness conclusion relies on an imported theoretical claim rather than on the empirical results. The DNNs omit softmax for tooling reasons, and the black-box comparison uses the authors' own prior results under different conditions.

free parameters (5)
  • Completeness perturbation depth = top-2 features
    Chosen due to memory constraints; directly affects completeness scores.
  • Stability runs = N=3
    Number of repeated runs for stability; arbitrary.
  • Robustness sample count = 100 samples
    Sample count for robustness bar plots; arbitrary.
  • DNN hyperparameters = learning rates 0.001/0.01/0.1, layers 128-64-32 or 7/5 nodes
    Chosen by experimentation; no softmax due to iNNvestigate.
  • Sparsity threshold increments = 0.1 steps from 0 to 1
    Manual choice for plotting sparsity curves.
assumptions (4)
  • domain assumption White-box XAI methods are complete by default because they have access to model parameters (from [17]).
    Invoked in Sections 6.6 and 7 to argue that empirical incompleteness in Tables 9-11 is an artifact of the reduced experiment.
  • domain assumption A class change after perturbing top features indicates a valid explanation.
    Operational definition of completeness in Section C.6; unproven proxy.
  • ad hoc to paper The DNN without softmax (required by iNNvestigate) is representative enough for XAI evaluation.
    Admitted in Section 7 that lack of softmax can impact accuracy and metrics.
  • domain assumption Results from [25] (different models, different perturbation depth) are comparable for the black-box vs white-box comparison.
    Section 6.8 compares to prior black-box results without re-running in identical setup.

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Cite this review

Pith. "Pith review of A Comparative Analysis of DNN-based White-Box Explainable AI Methods in Network Security." pith.science (2026). https://pith.science/paper/SQ4PPMEE

@misc{pith2026250107801,
  author       = {Pith},
  title        = {Pith review of: A Comparative Analysis of DNN-based White-Box Explainable AI Methods in Network Security},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SQ4PPMEE}},
  note         = {Machine review of arXiv:2501.07801}
}
read the original abstract

New research focuses on creating artificial intelligence (AI) solutions for network intrusion detection systems (NIDS), drawing its inspiration from the ever-growing number of intrusions on networked systems, increasing its complexity and intelligibility. Hence, the use of explainable AI (XAI) techniques in real-world intrusion detection systems comes from the requirement to comprehend and elucidate black-box AI models to security analysts. In an effort to meet such requirements, this paper focuses on applying and evaluating White-Box XAI techniques (particularly LRP, IG, and DeepLift) for NIDS via an end-to-end framework for neural network models, using three widely used network intrusion datasets (NSL-KDD, CICIDS-2017, and RoEduNet-SIMARGL2021), assessing its global and local scopes, and examining six distinct assessment measures (descriptive accuracy, sparsity, stability, robustness, efficiency, and completeness). We also compare the performance of white-box XAI methods with black-box XAI methods. The results show that using White-box XAI techniques scores high in robustness and completeness, which are crucial metrics for IDS. Moreover, the source codes for the programs developed for our XAI evaluation framework are available to be improved and used by the research community.

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.