Research context
Industrial and IIoT environments combine sensors, networks and production processes. Deviations in observed time-series or system behavior can be relevant to reliability, maintenance and security. Anomaly detection seeks to identify unusual patterns that deserve investigation, not to assert the cause automatically.
Methods and evaluation
The research interests include data-driven anomaly detection, machine-learning evaluation and explainability. Practical analysis considers data quality, operating conditions, drift, false alarms and how outputs can be understood by engineers responsible for actual systems.
Security perspective
My background in application security and automation informs interest in systems that fail safely and can be assessed reproducibly. This page describes current research direction, not published experimental results or guaranteed model performance.