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1. secml-unsupervised-anomaly-detection secml-unsupervised-anomaly-detection Public

   Unsupervised anomaly detection model trained on process level endpoint telemetry (BETH dataset) and Isolation Forests to study malicious events detection, false positives, and SOC implementation.

   Jupyter Notebook

2. secml-adversarial-ml-attacks secml-adversarial-ml-attacks Public

   Builds and evaluates adversarial ML attacks (data poisoning, targeted misclassification, and model extraction) and discusses defensive tradeoffs for real deployments.

   Jupyter Notebook

3. secml-model-drift-detection secml-model-drift-detection Public

   Detects concept and model drift in DNS traffic using ML, analyzes attack recall collapse, engages alarm for threshold drop, and compares retraining feasibility in a SOC detection environment.

   Jupyter Notebook

4. secml-llm-secure-coding-review secml-llm-secure-coding-review Public

   Iterative LLM-assisted code review on a CLI program, tracking how prompts change code quality, robustness, and security posture across versions.

   Python

5. secml-llm-prompt-rag-attacks secml-llm-prompt-rag-attacks Public

   Evaluates LLM safety failure modes across prompt attacks, context overflow, and RAG poisoning.

   Jupyter Notebook