Binary Tuna Swarm Optimization Algorithm-Based Feature Selection for Intrusion Detection Systems

Abstract

Feature 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.

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