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As cyber threats evolve, detecting botnet activities remains a critical challenge for digital ecosystem security. This research thoroughly investigates botnet detection techniques, introducing a novel stack-based ensemble model. Utilizing decision trees, random forests, XGBoost, and LightGBM as base learners, with a decision tree as the meta-learner, the ensemble demonstrates promising results in enhancing accuracy and resilience. The Stacking Ensemble Model achieved a high accuracy of 99.2%, with corresponding recall and F1 score values of 99.2% and 98.2%, respectively.

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Botnet Detection Through Machine Learning: A Stacking Ensemble Model Approach

  • 会议论文

  • 发表日期：

    2026年

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  • A. Rahul Reddy,
  • B. Rohith Reddy,
  • K. Sankar Sai Kumar Reddy,
  • Nirmal Keshari Swain,
  • Yugandhar Manchala

摘要

As cyber threats evolve, detecting botnet activities remains a critical challenge for digital ecosystem security. This research thoroughly investigates botnet detection techniques, introducing a novel stack-based ensemble model. Utilizing decision trees, random forests, XGBoost, and LightGBM as base learners, with a decision tree as the meta-learner, the ensemble demonstrates promising results in enhancing accuracy and resilience. The Stacking Ensemble Model achieved a high accuracy of 99.2%, with corresponding recall and F1 score values of 99.2% and 98.2%, respectively.