Predictive Policing for Machines: Detecting Industrial IoT Failures Before They Happen
![The comparative analysis of anomaly detection methods-tested against the KDDCup99, IoTID20, and WUSTL-IIoT datasets-demonstrates that performance, as measured by the Area Under the Curve [latex] AUC [/latex], fluctuates with the volume of records processed, highlighting the critical need for drift adaptation in maintaining reliable anomaly identification across evolving data streams.](https://arxiv.org/html/2601.03085v1/Figures/WUSTL.png)
A new method leverages machine learning to proactively identify anomalies in real-time data streams from industrial IoT devices, minimizing downtime and maximizing efficiency.






