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Cited by 35Network Security and Intrusion DetectionNetwork Packet Processing and OptimizationArtificial Immune Systems Applications

Systematic literature review on intrusion detection systems: Research trends, algorithms, methods, datasets, and limitations

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Issa, Melad Mohammed, Aljanabi, Mohammad, Muhialdeen, Hassan M.·2024·Journal of Intelligent Systems
DOI 10.1515/jisys-2023-0248
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Abstract

Machine learning (ML) and deep learning (DL) techniques have demonstrated significant potential in the development of effective intrusion detection systems. This study presents a systematic review of the utilization of ML, DL, optimization algorithms, and datasets in intrusion detection research from 2018 to 2023. We devised a comprehensive search strategy to identify relevant studies from scientific databases. After screening 393 papers meeting the inclusion criteria, we extracted and analyzed key information using bibliometric analysis techniques. The findings reveal increasing publication trends in this research domain and identify frequently used algorithms, with convolutional neural networks, support vector machines, decision trees, and genetic algorithms emerging as the top methods. The review also discusses the challenges and limitations of current techniques, providing a structured synthesis of the state-of-the-art to guide future intrusion detection research.

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  • ML can learn from previous attacks, refine detection techniques, and adapt to new forms of cyber threats, making it valuable where real-time response is crucial.

    “ML can learn from previous attacks, refine detection techniques, and adapt to new forms of cyber threats, making it particularly valuable in environments where real-time response is crucial [12].”

    From Towards enhanced cybersecurity in industrial control systems: a systematic review of context-based modeling, digital twins, and machine learning approaches (2025) · cites this paper as [12]

    The Introduction (page unavailable) describes ML- and DL-based intrusion detection as adaptable and capable of identifying known and unknown threats; §4.1 ML says trained models can quickly identify attacks. This supports much of the paraphrase, but the paper does not establish that ML refines its detection techniques from previous attacks or is particularly valuable for real-time response. Its discussion of real-time detection notes that one reviewed method may be too slow for it. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Authors
Issa, M. M., Aljanabi, M., & Muhialdeen, H. M.
Affiliations
  • Iraqi University (IQ)
  • Department of Computer Engineering, Al-Iraqia University , Baghdad , 10053 , Iraq
Corresponding author
Aljanabi, Mohammad
ORCID
Aljanabi, Mohammad
Venue
Journal of Intelligent Systems
Publisher
Walter de Gruyter GmbH
Published
2024-01-01
Volume
33
Issue
1
ISSN
  • 0334-1860 (ISSN-L)
  • 2191-026X
DOI
10.1515/jisys-2023-0248
OpenAlex
W4399451239
Cited by
35
References
1
Citations by year
2026: 11 · 2025: 22 · 2024: 2
FWCI
2.04
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Intelligent Systems (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
  • Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
  • Physical Sciences › Engineering › Biomedical Engineering › Artificial Immune Systems Applications
Keywords
intrusion detection systems, machine learning, deep learning, convolutional neural networks, support vector machine, decision tree, genetic algorithm, optimization algorithms, benchmark dataset, research trends, cybersecurity

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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