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A deep reinforcement based echo state network for network intrusion classification.

Khorshed Alam1, Mahbubul Haq Bhuiyan1, Dewan Md Farid1,2

  • 1Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh.

Plos One
|April 16, 2026
PubMed
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This study introduces a novel deep reinforcement learning (DRL) approach with Echo State Networks for dynamic network intrusion classification. K-means SMOTE data balancing significantly improved detection of evolving cyber threats.

Area of Science:

  • Cybersecurity
  • Machine Learning
  • Network Security

Background:

  • Network intrusion classification is crucial for identifying suspicious activities.
  • Traditional methods struggle with dynamic and evolving attack patterns.
  • Class imbalance in datasets poses a significant challenge for intrusion detection systems.

Purpose of the Study:

  • To propose a novel deep reinforcement learning (DRL) approach for network intrusion classification.
  • To integrate Echo State Networks (ESN) with DRL for adaptive threat detection.
  • To evaluate and enhance data balancing techniques for imbalanced network intrusion datasets.

Main Methods:

  • Utilized a deep reinforcement learning (DRL) framework combined with Echo State Networks (ESN).
  • Evaluated advanced data balancing techniques: Borderline-SMOTE, SMOTE-ENN, ADYSN, and K-means SMOTE.

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  • Conducted multi-dataset validation on benchmark datasets (NF-BoT-IoT, NF-UNSW-NB15, etc.) and adaptive modeling tests.

    • Main Results:

    • The K-means-based data balancing method demonstrated superior performance over other techniques.
    • The DRL-ESN approach showed improved accuracy and reliability in detecting novel and evolving threats.
    • Robust performance was validated across multiple diverse network intrusion datasets.

    Conclusions:

    • The proposed DRL-ESN approach offers a robust and adaptive solution for network intrusion classification.
    • Effective data balancing, particularly K-means SMOTE, is critical for enhancing intrusion detection performance.
    • The approach provides a viable solution for securing modern network infrastructures against sophisticated cyber threats.