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Multi-Objective Feature Selection for Intrusion Detection Systems: A Comparative Analysis of Bio-Inspired

Anıl Sezgin1,2, Mustafa Ulaş3, Aytuğ Boyacı4

  • 1Research and Development, Siemens A.S., Istanbul 34870, Turkey.

Sensors (Basel, Switzerland)
|October 16, 2025
PubMed
Summary

This study compares four bio-inspired algorithms for Intrusion Detection Systems (IDS) feature selection. Genetic Algorithm (GA) offered highest accuracy, while Ant Colony Optimization (ACO) provided the most efficient feature reduction.

Keywords:
IoT securitybio-inspired algorithmsfeature selectionintrusion detection systemsmulti-objective optimizationnetwork security

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Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Network Security

Background:

  • Sophisticated cyberattacks necessitate advanced Intrusion Detection Systems (IDS).
  • High dimensionality in network traffic data presents challenges for IDS accuracy and efficiency.
  • Bio-inspired metaheuristics offer promising approaches for optimizing IDS feature selection.

Purpose of the Study:

  • To comparatively evaluate four bio-inspired metaheuristics for multi-objective feature selection in IDS.
  • To analyze the trade-offs between accuracy, feature subset size, and computational efficiency.
  • To provide guidance on algorithm selection based on specific deployment constraints.

Main Methods:

  • Utilized Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) for feature selection.
  • Applied algorithms to the X-IIoTID dataset for a comprehensive performance analysis.
  • Evaluated performance based on accuracy, False Positive Rate (FPR), False Negative Rate (FNR), and feature reduction percentage.

Main Results:

  • Genetic Algorithm (GA) achieved the highest accuracy (99.60%) with a 0.39% FPR.
  • Grey Wolf Optimizer (GWO) provided a balance of high accuracy (99.50%) with a significant 65.08% feature reduction.
  • Ant Colony Optimization (ACO) demonstrated superior efficiency, yielding the smallest feature subset (7 features, 88.89% reduction) with 97.65% accuracy.

Conclusions:

  • Algorithm choice for IDS feature selection should align with specific deployment needs (e.g., edge, cloud).
  • Distinct trade-off regions exist: high accuracy (GA/PSO/GWO), balanced (GWO), and efficiency-focused (ACO).
  • The study provides a representative comparison of distinct bio-inspired search paradigms for IDS.