Intelligent Cyber-Attack Detection in IoT Networks Using IDAOA-Based Wrapper Feature Selection

Authors

  • Mohammed Abdullah Islamic Azad University South Tehran Branch, IRAN
  • Ryna Svyd Vasyl Stefanyk Precarpathian National University, The department of Computer Engineering and Electronics, Ukraine

DOI:

https://doi.org/10.31185/wjps.731

Abstract

ABSTRACT: In the realm of cybersecurity, the increasing sophistication of cyber-attacks demands the creation of sophisticated intrusion detection systems (IDS) designed to accurately detect and counteract threats in real-time. This study presents an innovative framework that integrates the Improved Dynamic Arithmetic Optimization Algorithm (IDAOA) with a Bagging technique to enhance the performance of intelligent cyber intrusion detection systems. The IDAOA serves as a wrapper-based feature selection method, optimizing the identification of the most impactful features while balancing local exploration and global exploitation. The Bagging technique further strengthens the classification phase by combining predictions from multiple classifiers, effectively addressing issues of class imbalance and improving overall system robustness. Evaluation of the proposed system using the NSL-KDD dataset demonstrates its superior performance, achieving an accuracy of 99.45%, significantly outperforming state-of-the-art approaches. These findings underscore the potential of intelligent optimization and ensemble learning techniques in advancing cybersecurity for IoT networks.

 

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Published

2025-06-30

Issue

Section

Computer

How to Cite

Abdullah, M., & Svyd, R. . (2025). Intelligent Cyber-Attack Detection in IoT Networks Using IDAOA-Based Wrapper Feature Selection. Wasit Journal for Pure Sciences , 4(2), 8-19. https://doi.org/10.31185/wjps.731