| Model | Precision | Recall | F1-score | |-------|-----------|--------|-----------| | IF | 0.72 | 0.68 | 0.70 | | LSTM-AE| 0.85 | 0.81 | 0.83 | | XGBoost| 0.88 | 0.85 | 0.86 | | | 0.95 | 0.93 | 0.94 |
: https://github.com/example/wid-api-benchmark (fictional) Final Note If "wid api anninc992i" actually refers to something real (e.g., an internal error code, a specific library, or a class from a course), please provide more context. I would be happy to write a genuine, non-fictional paper instead. wid api anninc992i
: Industrial API, anomaly detection, intermittent connectivity, TCN, benchmark. 1. Introduction Modern ICS field devices communicate via REST or MQTT over unreliable networks. Existing anomaly detection datasets (e.g., SWaT, WADI) focus on process sensor data, not API-level logs. The string "anninc992i" in our benchmark name denotes Annular Neural Network with Intermittent Connectivity , a proposed architecture for edge-based API monitoring. | Model | Precision | Recall | F1-score