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A method for forensic-ready intrusion detection using explainable learning, prediction-aware graph modeling, and

Sghaier Guizani1,2, Sneha Xavier2,3, Amal Ajayan2,4

  • 1Department of Electrical Engineering, Alfaisal University, Riyadh, Saudi Arabia.

Methodsx
|June 19, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces a reproducible intrusion detection workflow integrating data partitioning, preprocessing, Random Forest prediction, explainability (SHAP, LIME), and graph analysis for enhanced cyber defense. The method ensures auditable and verifiable forensic-ready intrusion detection.

Area of Science:

  • Cybersecurity
  • Data Science
  • Network Security

Background:

  • Intrusion detection systems (IDS) often lack reproducibility due to fragmented workflows.
  • Challenges include undocumented preprocessing, model configuration, and analysis.
  • This hinders verification, reuse, and forensic readiness.

Purpose of the Study:

  • To present a unified, reproducible, and forensic-ready intrusion detection method.
  • To integrate multiple stages of the detection pipeline into a single executable workflow.
  • To enhance audibility and verifiability of intrusion detection processes.

Main Methods:

  • Developed a leakage-safe stratified partitioning and deterministic preprocessing workflow.
  • Implemented Random Forest for multi-class prediction.
Keywords:
Counterfactual reasoningDiCEExplainable artificial intelligenceForensic analyticsIntrusion detectionPrediction-aware graph modelingRandomforestReproducible machine learning

Related Experiment Videos

  • Integrated explainability methods (SHAP, LIME) and prediction-aware interaction graph construction.
  • Incorporated counterfactual reasoning (DiCE) for decision-sensitive analysis.

    • Main Results:

    • The complete workflow was implemented on the TON-IoT dataset.
    • Reproducible outputs and intermediate artifacts were packaged for verification.
    • Demonstrated a unified procedure for building and analyzing intrusion detection systems.
    • Enabled graph-based ranking of suspicious nodes and flows.

    Conclusions:

    • The proposed method provides a unified procedure for building and analyzing intrusion detection systems.
    • Achieved explicit control over data partitioning, feature transformation, model behavior, and forensic decision-making.
    • Facilitates reproducible forensic and cyber defense studies through artifact-backed outputs.