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insider-threat-detection

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A comprehensive machine learning and deep learning pipeline for detecting insider threats using the CERT r4.1 dataset. This project combines unsupervised anomaly detection, supervised machine learning, and advanced deep learning architectures to identify anomalous user behavior in enterprise environments.

  • Updated Oct 11, 2025
  • Jupyter Notebook

Patent-aligned cybersecurity prototype implementing dynamic trust-based adaptive access control using credential integrity, competence evidence, behavioral risk, and event-driven trust recomputation.

  • Updated Apr 11, 2026
  • Python

An end-to-end AI system for detecting insider threats using a hybrid machine learning approach (Isolation Forest + XGBoost). Features a high-performance ETL pipeline using DuckDB, real-time inference via FastAPI, and integrated Explainable AI (SHAP) for transparent risk assessment on the CERT R4.2 dataset.

  • Updated Dec 7, 2025
  • Jupyter Notebook

*This simulation captures core, widely observed attacker behaviors aligned with common enterprise intrusion patterns. From brute-force access to obfuscated execution, persistence, recon, and privilege assessment, each step reflects actions that threat actors commonly execute after compromising a host.

  • Updated Jul 10, 2025
  • Jupyter Notebook

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