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arxiv: 2604.06254 · v1 · submitted 2026-04-06 · 💻 cs.CR · cs.AI· cs.CV

SE-Enhanced ViT and BiLSTM-Based Intrusion Detection for Secure IIoT and IoMT Environments

Afrah Gueriani , Hamza Kheddar , Ahmed Cherif Mazari , Seref Sagiroglu , Onur Ceran This is my paper

Pith reviewed 2026-05-10 18:38 UTC · model grok-4.3

classification 💻 cs.CR cs.AIcs.CV
keywords intrusion detectionIIoTIoMTVision TransformerBiLSTMSqueeze-and-Excitation attentioncybersecuritymachine learning
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The pith

The SE ViT-BiLSTM hybrid model reaches 99.33 percent accuracy for intrusion detection on IIoT benchmark data.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper introduces a hybrid architecture that modifies the Vision Transformer by swapping its standard attention for Squeeze-and-Excitation attention and then stacks BiLSTM layers on top. The resulting SE ViT-BiLSTM system is trained and tested on the EdgeIIoT and CICIoMT2024 datasets, both before and after balancing the attack classes. It reports higher accuracy, lower false-positive rates, and lower per-instance latency than prior intrusion detection methods. A sympathetic reader would care because IIoT and medical IoT networks contain many resource-limited devices that must detect threats quickly to avoid downtime or harm.

Core claim

The paper establishes that replacing the multi-head attention in a Vision Transformer with Squeeze-and-Excitation attention and integrating BiLSTM layers yields an intrusion detector that achieves 99.11 percent accuracy and 0.00032 seconds per instance latency on EdgeIIoT before balancing, improving to 99.33 percent accuracy after balancing, while reaching 98.16 percent accuracy and 0.00014 seconds latency on the balanced CICIoMT2024 dataset.

What carries the argument

The SE ViT-BiLSTM architecture, in which Squeeze-and-Excitation attention replaces the Vision Transformer's standard multi-head attention and BiLSTM layers are added to capture sequential dependencies in network traffic.

If this is right

  • The model processes each instance in 0.00014 to 0.00035 seconds on the tested datasets, supporting real-time use.
  • False-positive rates stay below 0.004 percent across both datasets before and after balancing.
  • Data balancing with SMOTE and RandomOverSampler raises accuracy on the imbalanced medical IoT dataset from 96.10 to 98.16 percent.
  • The same architecture is shown to apply to both industrial and medical IoT traffic classification tasks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the Squeeze-and-Excitation replacement is the main driver of gains, the same swap could be tried in other transformer-based detectors for different IoT domains.
  • The reported low latency makes the model a candidate for direct deployment on edge hardware, but this would still require validation under live traffic loads.
  • Because performance improved after balancing, future work on similar tasks should routinely check class imbalance as a confounding factor.

Load-bearing premise

The two benchmark datasets sufficiently represent the variety of real-world attack patterns and network conditions found in IIoT and IoMT environments.

What would settle it

Testing the same model on a third independent dataset containing previously unseen attack types and network conditions that yields accuracy below 95 percent would show the reported outperformance does not hold generally.

Figures

Figures reproduced from arXiv: 2604.06254 by Afrah Gueriani, Ahmed Cherif Mazari, Hamza Kheddar, Onur Ceran, Seref Sagiroglu.

Figure 2
Figure 2. Figure 2: Accuracy and loss of the proposed model. (a): Accuracies before [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Confusion matrix of the proposed model. (a): Before balancing [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: ROC curves of the proposed model. (a): Before balancing EdgeIIoT [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
read the original abstract

With the rapid growth of interconnected devices in Industrial and Medical Internet of Things (IIoT and MIoT) ecosystems, ensuring timely and accurate detection of cyber threats has become a critical challenge. This study presents an advanced intrusion detection framework based on a hybrid Squeeze-and-Excitation Attention Vision Transformer-Bidirectional Long Short-Term Memory (SE ViT-BiLSTM) architecture. In this design, the traditional multi-head attention mechanism of the Vision Transformer is replaced with Squeeze-and-Excitation attention, and integrated with BiLSTM layers to enhance detection accuracy and computational efficiency. The proposed model was trained and evaluated on two real-world benchmark datasets; EdgeIIoT and CICIoMT2024; both before and after data balancing using the Synthetic Minority Over-sampling Technique (SMOTE) and RandomOverSampler. Experimental results demonstrate that the SE ViT-BiLSTM model outperforms existing approaches across multiple metrics. Before balancing, the model achieved accuracies of 99.11% (FPR: 0.0013%, latency: 0.00032 sec/inst) on EdgeIIoT and 96.10% (FPR: 0.0036%, latency: 0.00053 sec/inst) on CICIoMT2024. After balancing, performance further improved, reaching 99.33% accuracy with 0.00035 sec/inst latency on EdgeIIoT and 98.16% accuracy with 0.00014 sec/inst latency on CICIoMT2024.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper proposes an SE ViT-BiLSTM hybrid architecture for intrusion detection in IIoT and IoMT settings. It replaces the Vision Transformer's multi-head attention with Squeeze-and-Excitation attention, integrates BiLSTM layers, and evaluates the model on the EdgeIIoT and CICIoMT2024 datasets both before and after balancing via SMOTE and RandomOverSampler. The central claim is that this model outperforms prior approaches, with reported accuracies of 99.11% (EdgeIIoT) and 96.10% (CICIoMT2024) before balancing, improving to 99.33% and 98.16% after balancing, alongside low FPR and per-instance latency values.

Significance. If the performance gains can be rigorously attributed to the SE modification and BiLSTM integration rather than balancing or tuning, the work could offer a practical, low-latency IDS approach for constrained IoT environments. The concrete numerical results on named public datasets provide a starting point for comparison, but the absence of ablations, baselines, and statistical validation reduces the strength of the outperformance claim.

major comments (3)
  1. [§5] §5 (Results and Discussion): The claim that the SE ViT-BiLSTM outperforms existing approaches lacks supporting baseline comparisons or tables listing prior methods' metrics on the same datasets and splits; without these, the reported accuracy/FPR/latency improvements cannot be directly verified as superior.
  2. [§4.3 and §5] §4.3 (Model Architecture) and §5: No ablation study isolates the contribution of the Squeeze-and-Excitation attention replacement versus a standard ViT-BiLSTM or versus the SMOTE/RandomOverSampler balancing step; the central attribution of gains to the SE modification is therefore unsupported.
  3. [§4.2] §4.2 (Experimental Setup): The manuscript provides no details on train/validation/test splits, number of random seeds, or statistical measures (error bars, confidence intervals, or p-values), making it impossible to assess whether the high accuracies (e.g., 99.33%) reflect stable performance or overfitting to the benchmark distributions.
minor comments (2)
  1. [Abstract and §5] Abstract and §5: Latency is reported in sec/inst but without specifying hardware platform or batch size, which limits reproducibility and comparison.
  2. [§3] §3 (Related Work): The discussion of prior ViT and BiLSTM IDS methods could include more recent 2023-2024 references on attention mechanisms in IoT security to strengthen context.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments, which highlight areas where additional details and analyses will strengthen the manuscript. We address each major comment below and will revise the paper accordingly.

read point-by-point responses

  1. Referee: §5 (Results and Discussion): The claim that the SE ViT-BiLSTM outperforms existing approaches lacks supporting baseline comparisons or tables listing prior methods' metrics on the same datasets and splits; without these, the reported accuracy/FPR/latency improvements cannot be directly verified as superior.

    Authors: We agree that a direct comparison table is needed to substantiate the outperformance claims. In the revised manuscript, we will add a table in Section 5 listing performance metrics (accuracy, FPR, latency) of relevant prior intrusion detection methods on EdgeIIoT and CICIoMT2024, using comparable evaluation settings where possible, to enable verification of the reported improvements. revision: yes

  2. Referee: §4.3 (Model Architecture) and §5: No ablation study isolates the contribution of the Squeeze-and-Excitation attention replacement versus a standard ViT-BiLSTM or versus the SMOTE/RandomOverSampler balancing step; the central attribution of gains to the SE modification is therefore unsupported.

    Authors: We acknowledge that ablation studies are required to isolate component contributions. We will add ablation experiments in the revised Section 5, including comparisons of the full SE ViT-BiLSTM against a standard ViT-BiLSTM (without SE attention) and against versions trained without SMOTE/RandomOverSampler balancing, to quantify the specific impact of the SE modification and balancing step. revision: yes

  3. Referee: §4.2 (Experimental Setup): The manuscript provides no details on train/validation/test splits, number of random seeds, or statistical measures (error bars, confidence intervals, or p-values), making it impossible to assess whether the high accuracies (e.g., 99.33%) reflect stable performance or overfitting to the benchmark distributions.

    Authors: We appreciate this observation. In the revised Section 4.2, we will specify the train/validation/test split ratios for each dataset, the number of random seeds used, and include statistical measures such as mean accuracy with standard deviations across runs and confidence intervals to demonstrate result stability. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical evaluation on external benchmarks

full rationale

The paper presents a hybrid neural architecture (SE ViT-BiLSTM) and reports its classification performance after training and evaluation on two public benchmark datasets (EdgeIIoT, CICIoMT2024), both before and after SMOTE balancing. No mathematical derivation chain, uniqueness theorem, or parameter-free prediction is claimed. The reported accuracies, FPR, and latency figures are standard supervised-learning outcomes obtained by fitting network weights to the given data splits; they are not renamed as 'predictions' derived from the inputs by construction. No self-citations are invoked as load-bearing premises, no ansatz is smuggled via prior work, and no self-definitional loop exists. The central claim therefore remains an empirical observation against external benchmarks rather than a reduction to the paper's own fitted values.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The central claim rests on empirical performance of a neural network whose weights are learned from the training portions of two specific datasets. No new physical or mathematical entities are postulated.

free parameters (2)
  • neural network weights and architecture hyperparameters
    All model parameters are fitted to the training data; their specific values are not reported in the abstract.
  • SMOTE and RandomOverSampler parameters
    Oversampling ratios and synthetic sample generation settings are chosen to balance the datasets and directly affect the reported post-balancing accuracies.
axioms (2)
  • domain assumption The labeled network traffic in EdgeIIoT and CICIoMT2024 is representative of real IIoT and IoMT attack scenarios.
    The model is trained and evaluated exclusively on these benchmarks; generalization depends on this assumption.
  • standard math Standard supervised learning assumptions hold (i.i.d. samples, fixed label definitions).
    Implicit in any supervised classification experiment on static datasets.

pith-pipeline@v0.9.0 · 5606 in / 1746 out tokens · 43344 ms · 2026-05-10T18:38:12.293976+00:00 · methodology

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Reference graph

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