Edge Sentinel Neural is a compact temporal convolutional autoencoder for industrial telemetry. It trains only on normal windows from devices 0-7, selects its anomaly threshold on devices 8-9, and reports final performance on entirely unseen devices 10-11.

It consumes the reproducible telemetry generated by the sibling edge-sentinel-ml project and complements that project's supervised and Isolation Forest baselines.

The 2,860-parameter model trained on 3,946 normal windows. Its threshold was selected on devices 8-9 and then frozen:

The test confusion matrix was [[987, 2], [21, 234]] over 1,244 overlapping temporal windows. Because a window is labeled anomalous when any constituent timestamp is anomalous, these figures should not be compared directly with the row-level classical benchmark.