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Related Experiment Videos

A scalable hybrid computational intelligence framework with bio inspired optimization for high dimensional malicious

Hua Liu1

  • 1College of Electronic and Information Engineering, Jiuquan Vocational Technical University, Jiuquan, 735000, Gansu, China. Alhmhy5522@163.com.

Scientific Reports
|March 25, 2026
PubMed
Summary

Related Concept Videos

Heuristics01:21

Heuristics

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Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
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This study introduces a scalable hybrid framework for detecting malicious URLs, integrating statistical modeling and bio-inspired optimization. The optimized models achieved over 96% accuracy, enhancing cybersecurity defenses.

Area of Science:

  • Cybersecurity and Computational Intelligence
  • Network Security and Data Science

Background:

  • Internet infrastructure complexity necessitates advanced computational frameworks for high-dimensional network data analysis.
  • Traditional detection methods face challenges with heterogeneous features and evolving threats, impacting robustness and efficiency.

Purpose of the Study:

  • To develop a scalable hybrid computational intelligence framework for large-scale malicious URL detection.
  • To integrate discriminative statistical modeling, gradient-based inference, and bio-inspired optimization for enhanced detection capabilities.

Main Methods:

  • Coupling Linear and Quadratic Discriminant Analysis with a gradient-boosted inference engine.
  • Employing Mother Optimization Algorithm and Osprey Optimization Algorithm for automated parameter tuning.
Keywords:
Computational intelligencebio-inspired optimizationdiscriminative modelingfeature attributionhigh-dimensional inferenceinternet-scale analyticsmalicious URL detectionnetwork-layer analysis

Related Experiment Videos

  • Utilizing SHAP-based feature attribution for model transparency and interpretability.

    • Main Results:

    • The bio-inspired optimized models achieved superior performance with >96% accuracy, precision, recall, F1-score, and specificity.
    • Statistical robustness was confirmed through rigorous evaluation including Shapiro-Wilk and Kruskal-Wallis tests.
    • The framework demonstrated effective inference on a large dataset of 63,191 URLs with diverse attributes.

    Conclusions:

    • The synergistic integration of hybrid discriminative intelligence and bio-inspired optimization significantly enhances URL classification performance.
    • The proposed framework offers a transferable and efficient paradigm for high-dimensional classification tasks beyond cybersecurity.
    • This approach provides a robust and interpretable solution for complex, data-intensive decision-making challenges.