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arxiv: 2506.00377 · v4 · submitted 2025-05-31 · 💻 cs.CR · cs.NE

A systematic review of metaheuristics-based and machine learning-driven intrusion detection systems in IoT

Mohammad Shamim Ahsan , Salekul Islam , Swakkhar Shatabda This is my paper

Pith reviewed 2026-05-19 12:13 UTC · model grok-4.3

classification 💻 cs.CR cs.NE
keywords IoT securityintrusion detection systemsmetaheuristic algorithmsmachine learningsystematic reviewfeature selectionparameter tuninghybrid optimization
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The pith

Metaheuristic algorithms show hidden correlations with machine learning models in IoT intrusion detection systems.

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

This systematic review surveys how metaheuristic optimization methods drawn from natural processes are combined with machine learning models to build intrusion detection systems for the Internet of Things. It identifies previously unnoticed links between specific optimizers and ML techniques appearing in recent IoT-IDS designs. The work examines the distinct roles of these methods in feature selection, parameter tuning, and hybrid setups, while offering a taxonomy that groups existing systems by their integration patterns. Such analysis matters because IoT environments operate with limited computing resources yet face constant threats from varied cyberattacks, so lighter yet effective detection approaches could reduce vulnerabilities without straining device capabilities.

Core claim

The review establishes that metaheuristic algorithms can be paired with machine learning models to improve intrusion detection systems for IoT networks. By examining state-of-the-art examples, it uncovers hidden correlations between particular optimization techniques and ML models, evaluates their separate effectiveness when used for feature selection, hyperparameter tuning, or hybrid combinations, and introduces a taxonomy to classify the systems. The analysis also covers practical integration challenges and points toward promising optimization approaches that could increase overall efficiency.

What carries the argument

The proposed taxonomy of IoT-IDSs that organizes systems according to their metaheuristic optimization strategies applied to machine learning models for tasks such as feature selection and tuning.

If this is right

  • Designers can select metaheuristic-ML pairings that match specific IoT constraints for improved detection accuracy.
  • Applications focused on feature selection may lower the data volume processed by resource-limited devices.
  • Hybrid metaheuristic and ML configurations could strengthen defenses against both known and novel attacks.
  • Resolution of identified integration issues would support more reliable deployment in decentralized IoT setups.

Where Pith is reading between the lines

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

  • The taxonomy could serve as a starting point for classifying IDS in related domains such as edge computing networks.
  • Empirical tests in live IoT testbeds would provide concrete performance data to extend the review's observations.
  • Similar correlation analysis might apply to other optimization methods outside the metaheuristic category.

Load-bearing premise

The body of published literature examined is complete and representative enough to support claims of hidden correlations and to justify the proposed taxonomy without major omissions or bias.

What would settle it

A follow-up search that locates many recent, high-impact papers on IoT intrusion detection systems whose designs show no alignment with the reported correlations or fall outside the proposed taxonomy would challenge the central findings.

Figures

Figures reproduced from arXiv: 2506.00377 by Mohammad Shamim Ahsan, Salekul Islam, Swakkhar Shatabda.

Figure 1
Figure 1. Figure 1: Categories of Intrusion Detection Methods in IoT. [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Classification of the existing IoT-IDS techniques. [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Year-wise publications for related IoT-IDSs, are included in this literature review. [PITH_FULL_IMAGE:figures/full_fig_p016_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Percentile of different quartile journals included in this literature review [Q1=29, [PITH_FULL_IMAGE:figures/full_fig_p017_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Taxonomy of the existing metaheuristics and ML-integrated IoT-IDSs. [PITH_FULL_IMAGE:figures/full_fig_p019_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Percentile of different applications of metaheuristics algorithms. [PITH_FULL_IMAGE:figures/full_fig_p026_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Percentile of the most used datasets for experimenting with the existing IoT-IDSs. [PITH_FULL_IMAGE:figures/full_fig_p027_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Usage of ML methods by the best performing IoT-IDSs. [PITH_FULL_IMAGE:figures/full_fig_p036_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Usage of metaheuristics algorithms by the best performing IoT-IDSs. [PITH_FULL_IMAGE:figures/full_fig_p036_9.png] view at source ↗
read the original abstract

The widespread adoption of the Internet of Things (IoT) has raised a new challenge for developers since it is prone to known and unknown cyberattacks due to its heterogeneity, flexibility, and close connectivity. To defend against such security breaches, researchers have focused on building sophisticated intrusion detection systems (IDSs) using machine learning (ML) techniques. Although these algorithms notably improve detection performance, they require excessive computing power and resources, which are crucial issues in IoT networks considering the recent trends of decentralized data processing and computing systems. Consequently, many optimization techniques have been incorporated with these ML models. Specifically, a special category of optimizer adopted from the behavior of living creatures and different aspects of natural phenomena, known as metaheuristic algorithms, has been a central focus in recent years and brought about remarkable results. Considering this vital significance, we present a comprehensive and systematic review of various applications of metaheuristics algorithms in developing a machine learning-based IDS, especially for IoT. A significant contribution of this study is the discovery of hidden correlations between these optimization techniques and machine learning models integrated with state-of-the-art IoT-IDSs. In addition, the effectiveness of these metaheuristic algorithms in different applications, such as feature selection, parameter or hyperparameter tuning, and hybrid usages are separately analyzed. Moreover, a taxonomy of existing IoT-IDSs is proposed. Furthermore, we investigate several critical issues related to such integration. Our extensive exploration ends with a discussion of promising optimization algorithms and technologies that can enhance the efficiency of IoT-IDSs.

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

2 major / 2 minor

Summary. The manuscript presents a systematic review of metaheuristic algorithms integrated with machine learning models for intrusion detection systems (IDS) in IoT networks. It claims to identify hidden correlations between these techniques, separately analyze their effectiveness for feature selection, parameter/hyperparameter tuning, and hybrid usages, propose a taxonomy of existing IoT-IDSs, investigate critical integration issues, and discuss promising future optimization approaches.

Significance. If the underlying literature search is exhaustive and free of selection bias, the review could usefully synthesize patterns in how metaheuristics enhance ML-based IoT-IDS performance under resource constraints, with the proposed taxonomy and correlation analysis providing a structured reference for researchers. The separate treatment of feature selection versus tuning applications is a potentially helpful organizational contribution.

major comments (2)
  1. [Methods] Methods section (or equivalent): no search protocol, database list, query strings, date range, inclusion/exclusion criteria, or PRISMA-style flow diagram is described. This directly undermines the central claims of discovering representative 'hidden correlations' and justifying a general taxonomy, as the completeness and lack of bias cannot be verified from the provided information.
  2. [Results/Taxonomy] Results and taxonomy sections: the assertion of 'hidden correlations' between specific metaheuristics and ML models is stated without accompanying quantitative support (e.g., co-occurrence counts, statistical measures, or tabulated mappings across the surveyed papers). This makes it impossible to assess whether the correlations are robust or merely descriptive of the selected subset.
minor comments (2)
  1. [Abstract] Abstract: the phrase 'comprehensive and systematic review' should be qualified by explicit reference to the review protocol or guidelines followed (e.g., PRISMA).
  2. [Throughout] Notation and terminology: ensure consistent capitalization and abbreviation of 'metaheuristic' versus 'meta-heuristic' and of model names (e.g., SVM, ANN) throughout the taxonomy and analysis sections.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed comments on our systematic review. We address each major comment below and have revised the manuscript to improve methodological transparency and the empirical grounding of our claims.

read point-by-point responses

  1. Referee: [Methods] Methods section (or equivalent): no search protocol, database list, query strings, date range, inclusion/exclusion criteria, or PRISMA-style flow diagram is described. This directly undermines the central claims of discovering representative 'hidden correlations' and justifying a general taxonomy, as the completeness and lack of bias cannot be verified from the provided information.

    Authors: We agree that the current manuscript lacks an explicit description of the literature search protocol. Although the review was conducted systematically, these details were omitted from the submitted version. In the revised manuscript we will add a dedicated Methods subsection that specifies the databases searched, exact query strings, date range, inclusion/exclusion criteria, and a PRISMA flow diagram. This addition will enable verification of completeness and bias and thereby strengthen support for the taxonomy and correlation findings. revision: yes

  2. Referee: [Results/Taxonomy] Results and taxonomy sections: the assertion of 'hidden correlations' between specific metaheuristics and ML models is stated without accompanying quantitative support (e.g., co-occurrence counts, statistical measures, or tabulated mappings across the surveyed papers). This makes it impossible to assess whether the correlations are robust or merely descriptive of the selected subset.

    Authors: We accept that the presentation of hidden correlations would be more convincing with quantitative evidence. The current version relies on qualitative synthesis of the surveyed papers. In the revision we will insert a table (or matrix) reporting co-occurrence frequencies between metaheuristic algorithms and ML models, together with any feasible statistical observations. This will allow readers to evaluate the robustness of the reported correlations beyond descriptive summary. revision: yes

Circularity Check

0 steps flagged

No circularity in literature synthesis or taxonomy construction

full rationale

This is a systematic review paper that surveys and synthesizes existing literature on metaheuristic optimization integrated with machine learning for IoT intrusion detection systems. It proposes a taxonomy and identifies correlations based on analysis of cited prior works rather than any original mathematical derivation, equations, or first-principles predictions. No steps reduce a claimed result to its own inputs by construction, fitted parameters renamed as predictions, or load-bearing self-citation chains; the central claims rest on external studies and the completeness of the surveyed body of work, which is independent of the present paper's own definitions or outputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a literature review paper. It introduces no new mathematical models, empirical measurements, or theoretical derivations, therefore the ledger contains no free parameters, axioms, or invented entities.

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