IRO Journals
IRO Journal on Sustainable Wireless Systems

Hybrid Feature Selection with Adaptive Threshold Optimization for Efficient Machine Learning–based Intrusion Detection

Open Access
Published: 14 July, 2026
Pages: 189-206
  • Valli S P , Valli S P
    Department of Computer Science and Engineering, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India
    Department of Computer Science and Engineering, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India
  • Rifat A K , Rifat A K
    Department of Computer Science and Engineering, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India
    Department of Computer Science and Engineering, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India
  • Shameem Sakinah Shameem Sakinah
    Department of Computer Science and Engineering, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India
    Department of Computer Science and Engineering, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India
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How to Cite

S P, Valli, Rifat A K, and Shameem Sakinah. 2026. “Hybrid Feature Selection With Adaptive Threshold Optimization for Efficient Machine Learning–based Intrusion Detection”. IRO Journal on Sustainable Wireless Systems 8 (3): 189-206. https://doi.org/10.36548/jsws.2026.3.005.
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Keywords

Intrusion Detection System
Machine Learning
Feature Selection
Particle Swarm Optimization
Adaptive Threshold

Abstract

With the increasing use of computer networks and internet services, network security becomes crucially important. The existing intrusion detection methods are based on signature analysis or rules, but these methods are inefficient against zero-day attacks and evolving cyber threats. In order to overcome the disadvantages of existing solutions, this study proposes a novel IDS framework with hybrid feature selection method and adaptive threshold optimization. The IDS under consideration uses the UNSW-NB15 dataset. First, the data pre-processing algorithms such as encoding, scaling and class balancing are used. Hybrid feature selection is then implemented by means of a combination of chi-squared filtering and Particle Swarm Optimization (PSO). Several supervised learning algorithms, including K-Nearest Neighbors, Decision Tree, Logistic Regression and Random Forest are used for training and testing. Moreover, Adaptive Threshold Testing Algorithm (ATTA) is applied for dynamic optimization of decision thresholds. The results show that this solution considerably increases IDS efficiency and decreases false positive rates.

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