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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Reinforcement Learning-Enhanced Botnet Defense System in Grid Topology Networks Using the SIRO Framework.

Mohd Hafizuddin Bin Kamilin1, Shingo Yamaguchi2, Sena Yoshioka2

  • 1Department of Intelligent System Engineering, National Institute of Technology, Ube College, Yamaguchi 755-8555, Japan.

Sensors (Basel, Switzerland)
|May 4, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces a new framework to enhance botnet detection in critical infrastructure networks. The proposed method significantly improves the speed and effectiveness of reinforcement learning for cybersecurity defense.

Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Network Security

Background:

  • Digitalization of essential services increases critical infrastructure vulnerability to botnet infections.
  • Malicious bots spread infections through neighbor-to-neighbor paths in grid topology networks.
  • Existing botnet extermination methods (heuristics, supervised learning, reinforcement learning) have limitations.

Purpose of the Study:

  • To propose a novel framework to shorten reinforcement learning (RL) training time and enhance its effectiveness for botnet detection.
  • To improve the defense against botnet infections in critical infrastructure networks.

Main Methods:

  • A framework incorporating multi-tensor network status surveying, Chebyshev-based action masking, reward reinforcement for key actions, and reward optimization for winning.
Keywords:
botnetcybersecurityreinforcement learning

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Last Updated: May 5, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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  • Implementation and evaluation of four RL algorithms (vanilla policy gradient, deep Q-network, proximal policy optimization, MuZero) within a grid topology network simulation.
  • Ablation study to determine the impact of individual framework components.

    • Main Results:

    • The Chebyshev-based masking strategy significantly contributed to performance improvement.
    • Multi-tensor surveying alone, without masking and reward optimization, could decrease performance.
    • The framework demonstrated a 49.129% increase in mean winning rate and a 118.8031% increase in mean win efficiency compared to previous work.

    Conclusions:

    • The proposed framework effectively shortens RL training and improves botnet detection efficacy.
    • The combination of multi-tensor input, action masking, and reward optimization is crucial for enhancing RL performance in cybersecurity applications.
    • The framework offers a promising solution for defending critical infrastructure against botnet threats.