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Binary Tuna Swarm Optimization Algorithm-Based Feature Selection for Intrusion Detection Systems
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DOI：10.24138/jcomss-2025-0083.png)
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Abstract
En 中文
selection is crucial for improving intrusion detection systems by addressing the curse of dimensionality and eliminating irrelevant features. However, applying continuous metaheuristics-such as the Tuna Swarm Optimization (TSO) algorithm-to this inherently binary problem requires effective binarization strategies. This paper presents TUNA-FS, a novel feature selection framework that employs a binary variant of the TSO algorithm. The proposed method introduces an adaptive V-shaped transfer function that dynamically manages the binarization process, maintaining a balance between exploration and exploitation throughout the search. Additionally, a multi-objective fitness function is used to jointly optimize key objectives: enhancing detection accuracy, reducing false alarms, and minimizing the number of selected features. The effectiveness of the approach is validated through comprehensive experiments on the NSL-KDD and CIC-IDS2017 benchmark datasets. Results demonstrate that the method achieves substantial feature reduction while maintaining high detection performance across multiple classifiers, including support vector machines, decision trees, random forests, and k-nearest neighbors. Comparative analysis against state-of-the-art methods confirms the competitiveness and balanced performance of the proposed framework, positioning it as an effective technique for enhancing intrusion detection efficiency and accuracy.
Keywords:
Feature Selection
Binary Tuna Swarm Optimization
Intrusion Detection System
Adaptive Transfer Function
Multi-objective Optimization
Network Security
Journal
J
IF:
0.7
Papers:
38
Citations:
171