• 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

1.A universal ensemble system with high performance levels was developed according to metric measures and compared to the baseline ML and DL models on the EMBER dataset.
2.Designed an integrated system that uses RandomForest, Extra Trees, XGBoost, LightGBM, Hist-GradientBoosting and unified deep-learning ensembles to control bias-variance in a balanced way.
3. Both traditional and deep learning models incorporate soft-voting, stacking, and meta-learning to ensure adaptability across diverse malware feature spaces.
4. Demonstrated scalability using memory-mapped data handling and standardized preprocessing for large datasets