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<p>The Internet of Things (IoT) has evolved as a significant area of impact and opportunity due to the billions of connected devices that have been distributed around the world. IoT devices, on the other hand, are susceptible to being compromised and hacked. When it comes to computing power and storage capacity, these Internet of Things devices are more vulnerable to cyberattacks than traditional endpoints such as smartphones, tablets, and laptops. This study introduces and assesses a machine learning-based cyberattack detection system. The suggested method employs a Support Vector Machine (SVM) classifier with the Harris Hawks Optimization (HHO) algorithm. The HHO technique improves SVM classifier hyperparameters, while the SVM performs malicious and normal stream classification based on the best-chosen model, and produces the optimal solution for feature weighting. The utility and capacity of the suggested approach to improve detection performance is demonstrated through proper scientific testing utilizing the LITNET-2020 benchmark dataset against six well-known classification algorithms and four metaheuristic-based classifiers using five reliable assessment measures.</p>

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A harris hawks optimized SVM framework for securing IoT networks through attack detection and feature analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月21日

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  • Dana A. Al-Qudah,
  • Ala’ M. Al-Zoubi,
  • Bashar Al-Shboul,
  • Bilal Abu-Salih,
  • Raneem Qaddoura,
  • Mohammad Hijjawi

摘要

The Internet of Things (IoT) has evolved as a significant area of impact and opportunity due to the billions of connected devices that have been distributed around the world. IoT devices, on the other hand, are susceptible to being compromised and hacked. When it comes to computing power and storage capacity, these Internet of Things devices are more vulnerable to cyberattacks than traditional endpoints such as smartphones, tablets, and laptops. This study introduces and assesses a machine learning-based cyberattack detection system. The suggested method employs a Support Vector Machine (SVM) classifier with the Harris Hawks Optimization (HHO) algorithm. The HHO technique improves SVM classifier hyperparameters, while the SVM performs malicious and normal stream classification based on the best-chosen model, and produces the optimal solution for feature weighting. The utility and capacity of the suggested approach to improve detection performance is demonstrated through proper scientific testing utilizing the LITNET-2020 benchmark dataset against six well-known classification algorithms and four metaheuristic-based classifiers using five reliable assessment measures.