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Utilizing Adaptive Machine Learning Algorithms for Information Risk Warning and Network Security Scenario Awareness

Jiahao Luo1, Jingjing Xie2

  • 1Department of Information, Shanghai Proton and Heavy Ion Center, Shanghai Key Laboratory of Radiation Oncology, Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy.

Journal of Visualized Experiments : Jove
|June 22, 2026
PubMed
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This summary is machine-generated.

This study introduces a new cloud security framework using adaptive Machine Learning (ML) and Hierarchical Multi-Label Classification (HMC) for real-time cyberattack detection and risk assessment in cloud environments.

Area of Science:

  • Cloud Computing Security
  • Cybersecurity Analytics
  • Network Defense Strategies

Background:

  • Traditional security methods struggle with complex, evolving cyber threats like zero-day exploits, DDoS, and botnets.
  • Emerging attacks necessitate advanced, adaptive detection mechanisms beyond static or rule-based approaches.
  • Cloud environments present unique challenges due to their dynamic and large-scale nature.

Purpose of the Study:

  • To propose a novel framework for network security situational awareness and risk warning in cloud computing.
  • To integrate adaptive Machine Learning (ML), Hierarchical Multi-Label Classification (HMC), and dynamic trust evaluation.
  • To enhance the detection and mitigation of diverse and sophisticated cyberattacks in real-time.

Main Methods:

Related Experiment Videos

  • Developed a Software-Defined Networking (SDN)-based cloud architecture using Ryu OpenFlow controller and switches.
  • Implemented a hierarchical classification framework to decompose multiclass problems and address sample imbalance.
  • Utilized ensemble learning techniques (AdaBoost, Bagging) and a dynamic trust evaluation mechanism.
  • Collected data from DDoS datasets, cloud traffic, and simulations in Mininet and EstiNet.

    • Main Results:

    • The integrated ML-HMC-trust approach significantly improved detection precision and reduced false positives.
    • The framework demonstrated enhanced recognition of low-frequency attacks, including User to Root (U2R).
    • Real-time data collection, dynamic scheduling, and scalable transmission were enabled by the SDN architecture.

    Conclusions:

    • The proposed framework offers a robust and scalable solution for securing large-scale cloud platforms.
    • Integrating adaptive learning, hierarchical classification, and dynamic trust evaluation is effective for advanced threat detection.
    • The study highlights the potential of SDN and ML for real-time network security situational awareness and risk warning.