Enhancing IoT Network Security through Adaptive Curriculum Learning and XAI

Amballa Venkata Sriram; MSVPJ Sathvik; Ranga Rao Venkatesha Prasad; Sai Prashanth Mallellu; Sathwik Narkedimilli; Sujith Makam

REVIEW 4 major objections 4 minor 30 references

Enhancing IoT Network Security through Adaptive Curriculum Learning and XAI

T0 review · 4 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Curriculum learning with LIME-guided feature removal reaches 97–98% accuracy on IoT attack datasets.

desk verdict A plausible curriculum-learning pipeline for IoT intrusion detection, but the pre-split LDA/scaling makes the reported accuracies unreliable as evidence of generalization. read the letter →

arxiv 2501.11618 v1 pith:S6GTXGDL submitted 2025-01-20 cs.CR

Sathwik Narkedimilli , Sujith Makam , Amballa Venkata Sriram , Sai Prashanth Mallellu , MSVPJ Sathvik , Ranga Rao Venkatesha Prasad This is my paper · ORCID

classification cs.CR

keywords CurriculumLearningExplainableAI(XAI)LIMEIntrusionDetectionSystemIoTSecurityEnsembleStackingEdgeDeploymentLightweightNeuralNetwork

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 claims that a curriculum learning framework, where a lightweight neural network is trained first on easy normal and attack patterns and then on harder attacks, can detect intrusions in IoT networks at 97–98% accuracy across three recent benchmark datasets. It argues that adding LIME explanations to identify and remove low-relevance features at each stage, and then stacking the resulting model with Random Forest and XGBoost, contributes measurable accuracy gains over the base network. The reason this matters is that the whole model fits in about 367 KB (94,051 parameters), so the claimed performance is supposed to be achievable on resource-constrained edge devices while remaining interpretable. If the result holds, IoT security systems could get both high detection rates and human-readable explanations without needing large cloud infrastructure.

What carries the argument

The load-bearing mechanism is the staged curriculum loop: at each of several hand-designed stages (normal data, then simple, medium, and complex attacks), the shared neural network is trained, LIME is run on a fraction of the staged data, features whose importance falls below a threshold ($-0.01$) are removed, and the model is retrained on the remaining features. This 'feature un-learning' is what ties XAI to the curriculum, letting the model discard low-relevance inputs as it progresses to harder attacks. The neural backbone—GRU and LSTM layers plus self-attention and residual connections—captures both short- and long-term temporal dependencies in sequential IoT data, and the final stacking ensemble with Random Forest and XGBoost adds a small but consistent generalization gain. The architecture is kept edge-deployable by pruning and quantization, yielding 94,051 parameters.

What would settle it

Train and test the framework on CIC-IoV-2024 using a time-ordered split—earliest 80% of messages for training, latest 20% for testing—instead of the paper's random 80-20 split, and compare the accuracy. If it drops well below 98%, the reported result depends on random splitting of sequential CAN-bus traffic and would not transfer to real deployments.

Watch

Extended reading notes

Core claim

The paper's central claim is that a neural network built from GRU, LSTM, and attention layers, trained in curriculum stages ordered by attack complexity and refined by LIME-based feature un-learning, then combined into a stacking ensemble, achieves 98% accuracy on CIC-IoV-2024 and CIC-APT-IIoT-2024 and 97% on Edge-IIoT. The framework uses an adaptive feature mask layer, a dynamic convolutional layer for short-term dependencies, an attention-based temporal encoder for long-term dependencies, residual connections, and normalization, and is pruned and quantized to 94,051 parameters (367.39 KB). The paper reports an ablation on CIC-IoV-2024 in which the base network scores 88%; adding curriculum learning gives 94%, LIME integration and feature un-learning give 97%, and the stacking ensemble gives 98%. It also reports 99–100% precision, 95–97% recall, and F1-scores of 96–99% across the three datasets.

Load-bearing premise

Everything rests on assuming that randomly splitting each dataset into 80% train and 20% test keeps temporally ordered IoT and CAN-bus traffic from leaking between the two, and that the hand-chosen curriculum stages and a LIME cutoff of $-0.01$ will also work on attack types the model has not seen.

Editorial extensions

If this is right

If the reported accuracies hold, a single 367 KB model can serve as an intrusion detector on edge devices for vehicular (CAN-bus), general IoT, and industrial IoT traffic, with no cloud round-trip.
The ablation implies that each design choice—curriculum ordering, LIME-based feature removal, and ensemble stacking—pays a measurable accuracy dividend (6, 3, and 1 points respectively on CIC-IoV-2024), so the framework's performance is not attributable to one component alone.
Because LIME explanations are attached to each prediction, a security analyst can see which features (e.g., arp.opcode, dns.retransmission) drove an attack decision, which is the transparency the paper argues is missing from other IoT intrusion detection approaches.
The lightweight footprint suggests the framework could be further quantized or combined with federated learning, which the paper lists as future work for privacy-preserving distributed training.

Reading between the lines

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

A natural stress test the authors did not run: replace the random 80-20 split with a time-ordered split on CIC-IoV-2024's CAN-bus stream. If accuracy drops, the random split may be letting the model see temporally adjacent attack traffic in both train and test, which would overstate real-world performance.
The LIME threshold of $-0.01$ is a fixed scalar chosen by inspection. An extension would be to make feature removal adaptive per stage, for example by keeping the top-k features by LIME weight, and to test whether the 3% XAI gain persists across thresholds.
Because curriculum stage groupings were hand-crafted per dataset (4 stages for Edge-IIoT, 2 for CIC-APT-IIoT, 3 for CIC-IoV-2024), a skeptic would want to see whether an automatic curriculum, with difficulty scored by model loss or confidence, reproduces the gains without hand-tuning.

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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

Colleague, quick take on arXiv:2501.11618. The paper's core idea is to train an intrusion detector with curriculum learning (simple attacks first), use LIME to identify and drop low-relevance features between stages, and stack a GRU/LSTM/attention net with RF and XGBoost. The ablation on CIC-IoV-2024 shows steady gains: +6% from curriculum, +3% from LIME-based feature un-learning, +1% from the ensemble, reaching 98% accuracy. That is a coherent story, and the lightweight model (94k parameters) is a plus. Credit where due: the staged learning setup is concretely described, the datasets are recent and relevant, and the ablation is a legitimate attempt to attribute gains.

The problems are in the evaluation. The biggest one is in Section 4.1: StandardScaler and LDA are applied before the 80-20 split. Fitting LDA on the full dataset uses test labels, and the scaler uses test statistics. That means the reported 97-98% accuracies are not unbiased estimates of generalization. This is load-bearing, not a quibble. Even a proper temporal split would not fix it as long as preprocessing is fit on the full set. The paper needs a re-run with preprocessing inside the training folds.

Smaller issues: Table 1 reports F1 97% for CIC-IoV-2024 while the text says 99% (and precision 100/recall 97 implies ~98.5). No code, no seeds, no error bars, and no comparison to existing methods on the same datasets. The random 80-20 split for sequential IoT/CAN data is another vulnerability, though secondary.

The XAI contribution is an in-sample feature selection loop, which is fine as a modeling choice, but the claimed +3% is evidence only that dropping low-LIME-importance features helps on these benchmarks, not that the threshold generalizes to unseen attacks.

Who is this for? A reader working on IoT intrusion detection might find the curriculum-plus-masking idea worth trying, but should not trust the headline numbers as they stand. The paper deserves a serious referee, but the first request will be a clean re-run with baselines. If those come back and the gains hold, this becomes a modest contribution. As is, it is a 'maybe' for a reading group and not something I would cite.

Recommendation: send to peer review with heavy-revision expectations. The framework is not junk, but the evaluation protocol invalidates the central claim until fixed.

Referee Report

4 major / 4 minor

Summary. The paper proposes a curriculum learning framework for IoT/IIoT intrusion detection, combining a GRU/LSTM/attention neural network with a LIME-based feature un-learning mechanism, edge-oriented quantization/pruning, and a stacking ensemble of the staged model with Random Forest and XGBoost. The authors evaluate the framework on Edge-IIoT, CIC-APT-IIoT-2024, and CIC-IoV-2024, reporting accuracies of 97%, 98%, and 98%, respectively, with a lightweight model of 94,051 parameters, and present an ablation study attributing accuracy gains to curriculum learning, XAI integration, and stacking. The central claim is that this combination yields accurate, transparent, and lightweight intrusion detection for diverse IoT networks.

Significance. If the reported results were sound, the paper would offer a practically relevant contribution: a compact temporal model with explicit parameter counts and deployment-oriented techniques, evaluated on three recent IoT/IIoT security datasets. The use of CIC-IoV-2024 and CIC-APT-IIoT-2024 is timely, and the explicit ablation breakdown in Table 2 is a useful organizing device. However, the empirical core is not currently reproducible or verifiable: no code, seeds, or hyperparameters are provided, and the preprocessing description in Section 4.1 indicates a supervised dimensionality-reduction step before the train/test split. The significance of the central accuracy claim therefore depends on fixes that are within the scope of a revision.

major comments (4)

[Section 4.1, Table 1] The preprocessing description states that StandardScaler and Linear Discriminant Analysis (LDA) are applied before the 80-20 split: 'This processed data gets split up into training and testing data by an 80-20 split.' Since LDA is a supervised projection that uses class labels and StandardScaler uses full-data statistics, the test set has already influenced the feature space, making the accuracies in Table 1 optimistically biased. Please move all preprocessing inside a cross-validation or nested-split pipeline, or explicitly state that the scaler and LDA were fit only on the training portion; providing code or detailed pseudo-code for the split would also allow verification.
[Section 5, Table 1] The reported F1-score for CIC-IoV-2024 is internally inconsistent: the text states 'F1-Score 99%' while Table 1 reports 97%, and precision 100% with recall 97% implies F1 approximately 98.5%. These numbers cannot all be correct. Please recompute and harmonize all metrics, and report the exact formulas used for precision, recall, and F1.
[Section 5, Table 2] The ablation study reports single-point accuracy increments (+6% for curriculum learning, +3% for XAI/LIME un-learning, +1% for stacking) without standard deviations, repeated seeds, or statistical tests. Moreover, the LIME-based un-learning removes features based on the model's own explanations, so the +3% could be an in-sample artifact if the LIME importance scores are computed on the full dataset or if the stage-wise retraining is not nested within the training split. Please clarify which data are used to compute LIME importances, and report mean and variance over multiple runs.
[Section 5, Tables 1 and 2] The paper does not compare the proposed method against any existing intrusion-detection baselines on the same datasets, even though Section 3 cites related work reporting high accuracies on Edge-IIoT (e.g., CNN-GRU and ensemble methods). Without such comparisons, the claim that the curriculum/XAI/ensemble framework 'further enhances generalization' is not empirically established. Please add comparisons to at least the strongest prior methods on each dataset, ideally from the references cited in Section 3.

minor comments (4)

[Title page and template metadata] The manuscript contains ACM template placeholders that should be removed before submission, including 'Make sure to enter the correct conference title from your rights confirmation email', 'XX, XX', and the placeholder DOI and ISBN lines.
[Section 4.1] The text refers to 'LDA plots or Random Forest feature importance plots' and 'log scaling and binning' but none of these feature-engineering artifacts are shown or described with concrete settings; please either present them or remove the unsupported details.
[Section 5, Figure 4] The hyperparameter-importance figure is presented without any description of how the importance values were obtained; please specify the sensitivity-analysis procedure, including the ranges of hyperparameters tested.
[Throughout] The manuscript needs careful copyediting for grammar and style; for example, 'The motivation for this research is that the IoT devices are getting more and more mainstream' and 'it’s scalability' are unclear or incorrect. Also, table and figure references should be consistent (e.g., Table 2 is introduced after a paragraph that says 'the table 2 above').

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: external benchmarks carry the accuracy claims; the LIME un-learning step is an empirical ablation, not a construction.

full rationale

The paper's central accuracy claims (97% on Edge-IIoT, 98% on CIC-APT-IIoT-2024 and CIC-IoV-2024) are evaluated against external benchmark datasets and are not derived from the model's own definitions or from a self-citation chain. The reference list contains no prior work by the present authors, and no uniqueness theorem or prior result is invoked to force the proposed choice. The LIME-based feature un-learning in Section 4.2 is self-referential in the sense that the model's own explanations determine which features are removed, but the reported +3% improvement in Table 2 is an empirical ablation result, not a quantity that equals its input by construction; it could in principle have been negative. The pre-split StandardScaler and LDA described in Section 4.1 create a real risk of test information leaking into preprocessing, which would bias the reported accuracies, but evaluation leakage is a correctness concern rather than circularity. No quoted equation or fitted parameter is renamed as a prediction, so no circular step meets the specific-reduction bar required for a positive finding.

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

The central accuracy claim rests on a small number of hand-chosen design decisions (curriculum stage groupings, LIME threshold, hyperparameters) and on assumptions about the validity of random train/test splits and the reliability of benchmark labels. No new entities are introduced. The paper does not disclose enough to determine the true number of free parameters.

free parameters (4)

LIME relevance threshold = -0.01
Features with LIME importance below -0.01 are removed at each curriculum stage (Section 4.2); the threshold is chosen ad hoc and no sensitivity analysis is given.
Curriculum stage groupings = hand-crafted per dataset
Stage definitions (e.g., Stage 2 OS Fingerprinting and Port Scanning for Edge-IIoT, Stage 2 GAS/RPM/SPEED for CIC-IoV-2024) are assigned by the authors based on perceived attack complexity; no automated or validated curriculum ordering is used.
Neural network hyperparameters = not reported
Learning rate, batch size, number of GRU/LSTM layers and units, dropout rate, and PCA/LDA components are described qualitatively (Figure 4) but exact values are not given, so the architecture is under-specified.
LDA dimensionality = not specified
Section 4.1 says LDA reduces feature dimension 'where the number of elements is a function of explained variance', but the target dimension is not reported.

assumptions (5)

domain assumption Random 80-20 stratified split without temporal separation is a valid generalization test for time-ordered intrusion data
Section 4.1 describes an 80-20 split with stratified sampling; IoT/CAN-bus traces are sequential, so random splits can leak temporal context and inflate accuracy.
domain assumption LIME feature importance is a reliable guide for deleting features across curriculum stages
Section 4.2 uses LIME scores to remove features with importance below -0.01; this assumes local linear explanations are stable and that removing low-LIME features does not discard information needed for future attack classes.
domain assumption Curriculum ordering from normal to simple to complex attacks improves generalization
The paper assumes staged training on attack difficulty ordering is beneficial; no theoretical or empirical comparison to random or reverse curricula is provided.
domain assumption Benchmark labels and features are correct and representative
The framework relies on the quality of Edge-IIoT, CIC-APT-IIoT-2024, and CIC-IoV-2024; the original dataset papers are not evaluated here.
ad hoc to paper Features with LIME importance below -0.01 are unimportant and safe to remove
The threshold appears only in this paper and is not derived from any principle (Section 4.2).

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Cite this review

Pith. "Pith review of Enhancing IoT Network Security through Adaptive Curriculum Learning and XAI." pith.science (2026). https://pith.science/paper/S6GTXGDL

@misc{pith2026250111618,
  author       = {Pith},
  title        = {Pith review of: Enhancing IoT Network Security through Adaptive Curriculum Learning and XAI},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/S6GTXGDL}},
  note         = {Machine review of arXiv:2501.11618}
}

read the original abstract

To address the critical need for secure IoT networks, this study presents a scalable and lightweight curriculum learning framework enhanced with Explainable AI (XAI) techniques, including LIME, to ensure transparency and adaptability. The proposed model employs novel neural network architecture utilized at every stage of Curriculum Learning to efficiently capture and focus on both short- and long-term temporal dependencies, improve learning stability, and enhance accuracy while remaining lightweight and robust against noise in sequential IoT data. Robustness is achieved through staged learning, where the model iteratively refines itself by removing low-relevance features and optimizing performance. The workflow includes edge-optimized quantization and pruning to ensure portability that could easily be deployed in the edge-IoT devices. An ensemble model incorporating Random Forest, XGBoost, and the staged learning base further enhances generalization. Experimental results demonstrate 98% accuracy on CIC-IoV-2024 and CIC-APT-IIoT-2024 datasets and 97% on EDGE-IIoT, establishing this framework as a robust, transparent, and high-performance solution for IoT network security.

Figures

Figures reproduced from arXiv: 2501.11618 by the authors.

**Figure 3.** Accuracy Improvements by Integrating Key Com [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

**Figure 4.** Hyper-Parameters Importance in the Proposed Neu [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

Figures from the paper (1 more)

**Figure 5.** Figure 5: Example LIME Explanation for Attack Classification, Highlighting Key Features and Their Contributions to the [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

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

[1] [1]

& Tripathi, S

Madakam, S., Ramaswamy, R. & Tripathi, S. Internet of Things (IoT): A literature review. Journal Of Computer And Communications . 3, 164-173 (2015)

work page 2015

[2] [2]

& Samanta, D

Gurunath, R., Agarwal, M., Nandi, A. & Samanta, D. An overview: security issue in IoT network. 2018 2nd International Conference On I-SMAC (IoT In Social, Mobile, Analytics And Cloud)(I-SMAC) I-SMAC (IoT In Social, Mobile, Analytics And Cloud)(I-SMAC), 2018 2nd International Conference On . pp. 104-107 (2018)

work page 2018

[3] [3]

IoT Network Security: Requirements, Threats, and Countermeasures

Hamza, A., Gharakheili, H. & Sivaraman, V. IoT network security: requirements, threats, and countermeasures. ArXiv Preprint ArXiv:2008.09339. (2020)

work page Pith review arXiv 2020

[4] [4]

& Faruki, P

Chaabouni, N., Mosbah, M., Zemmari, A., Sauvignac, C. & Faruki, P. Network intrusion detection for IoT security based on learning techniques. IEEE Commu- nications Surveys & Tutorials. 21, 2671-2701 (2019)

work page 2019

[5] [5]

& Wang, L

Chen, Q., Li, D. & Wang, L. Network security in the internet of things (iot) era. Journal Of Industrial Engineering And Applied Science . 2, 36-41 (2024)

work page 2024

[6] [6]

& Zhu, X

Wheelus, C. & Zhu, X. IoT network security: Threats, risks, and a data-driven defense framework. IoT. 1, 259-285 (2020)

work page 2020

[7] [7]

& Sikdar, B

Hassija, V., Chamola, V., Saxena, V., Jain, D., Goyal, P. & Sikdar, B. A survey on IoT security: application areas, security threats, and solution architectures. IEEe Access. 7 pp. 82721-82743 (2019)

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& Narmadha, R

Pamarthi, S. & Narmadha, R. Literature review on network security in Wireless Mobile Ad-hoc Network for IoT applications: network attacks and detection mechanisms. International Journal Of Intelligent Unmanned Systems . 10, 482-506 (2022)

work page 2022