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<p>The exponential growth of malware attacks, particularly those exploiting malicious URLs, poses a significant threat to cybersecurity in real-time digital environments. To address the challenges of high-dimensional feature spaces and the need for fast, accurate detection, this study proposes a hybrid bio-inspired optimization framework that combines Harris Hawks Optimization (HHO) and the Bat Algorithm (BA) for effective feature selection. The framework evaluates two strategies—union (HHO∪BA) and intersection (HHO∩BA)—to balance detection performance and computational efficiency. After feature selection, classifiers including XGBoost and Extra Trees are fine-tuned using Grid Search to ensure optimal performance. Experiments are conducted on the ISCX-URL2016 dataset, which includes a comprehensive set of benign and malware-labeled URLs. Results show that the HHO∪BA approach achieves the highest detection accuracy (up to 99.52%) and robust classification metrics, making it ideal for high-security applications where accuracy is critical. In contrast, the HHO∩BA method offers significantly faster training and inference times, making it more suitable for real-time or resource-constrained environments. These findings highlight the trade-off between accuracy and speed and provide a flexible framework that can be adapted to various cybersecurity deployment scenarios.</p>

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A bio inspired hybrid optimization framework for efficient real time malware detection

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月31日

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  • Mosleh M. Abualhaj,
  • Hani Al-Mimi,
  • Mahran Al-Zyoud,
  • Sumaya N. Al-Khatib,
  • Mohammad Sh. Daoud,
  • Hussain Al-Aqrabi,
  • Mohammed Anbar,
  • Ahmad Shalaldeh

摘要

The exponential growth of malware attacks, particularly those exploiting malicious URLs, poses a significant threat to cybersecurity in real-time digital environments. To address the challenges of high-dimensional feature spaces and the need for fast, accurate detection, this study proposes a hybrid bio-inspired optimization framework that combines Harris Hawks Optimization (HHO) and the Bat Algorithm (BA) for effective feature selection. The framework evaluates two strategies—union (HHO∪BA) and intersection (HHO∩BA)—to balance detection performance and computational efficiency. After feature selection, classifiers including XGBoost and Extra Trees are fine-tuned using Grid Search to ensure optimal performance. Experiments are conducted on the ISCX-URL2016 dataset, which includes a comprehensive set of benign and malware-labeled URLs. Results show that the HHO∪BA approach achieves the highest detection accuracy (up to 99.52%) and robust classification metrics, making it ideal for high-security applications where accuracy is critical. In contrast, the HHO∩BA method offers significantly faster training and inference times, making it more suitable for real-time or resource-constrained environments. These findings highlight the trade-off between accuracy and speed and provide a flexible framework that can be adapted to various cybersecurity deployment scenarios.