Explainable Hybrid Intrusion Detection: Combining Ensemble Learning and XAI for Transparent Cybersecurity

• The developed an advanced intrusion detection system
achieved 0.97 accuracy and 0.99 precision for attack clas-
sification, significantly outperforming baseline models
• This study innovatively integrates SHAP and LIME in-
terpretability methods, providing both global and local
explanations that validate the model’s reliance on mean-
ingful network features such as packet time-to-live (sttl)
and protocol types (proto).
• Through rigorous evaluation metrics (AUC=0.9974) and
error analysis , the study establishes the reliability of
the model for real-world deployment in security-sensitive
environments.
• This study contributes to cybersecurity research by
demonstrating how interpretable machine learning can
effectively detect modern network threats while maintain-
ing operational transparency for security analysts.
• The study outlines critical pathways for subsequent work,
including adaptive learning mechanisms for evolving
threats and optimization of edge computing architectures
in distributed networks