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March 25, 2026Machine Learning ResearchOpen Access

Data Science and Machine Learning for Cyber Intrusion Detection: A Systematic Review

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Authors

YRYali RenGeorgia Institute of Technology

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Implication

Systematic review reveals trends and gaps in data science and machine learning for intrusion detection.

Key Points

  • The review aims to consolidate knowledge on data science and machine learning applications in cyber intrusion detection.
  • Systematic literature review of 153 studies from 2009 to 2025
  • Categorization of data science and machine learning techniques
  • Quantitative meta-analysis of benchmark datasets and algorithm usage
  • Identification of research gaps and emerging trends
  • UNSW-NB15 and CIC-IDS2017 datasets account for 71% usage
  • Deep learning algorithms constitute 40% of approaches
  • Only 34% of studies provide recall metrics for minority attack classes
  • Nine key research gaps identified
  • Eight emerging trends in intrusion detection technologies proposed

Cite This Study

Yali Ren (2026) studied this question.

synapsesocial.com/papers/69c37b54b34aaaeb1a67d9dahttps://doi.org/10.11648/j.mlr.20261101.12
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