Identifying anomalous file transfer events in LCLS workflow

This short paper reports our on-going work to study and identify anomalous file transfers for a large scientific facility known as Linac Coherent Light Source (LCLS). We identify the anomalies based on the statistical models extracted from the recent observations of the file transfer events. This data-driven approach could be used in different use cases to identify unusual events. More specifically, we propose two different identification strategies based on the different properties of the observed file transfers. Because these methods capture key aspects of the two different segments of the data transfer pipeline, they are able to make accurate identifications for their respective workflow components. The current anomaly detection algorithms only make use of the file sizes as the primary feature. We anticipate that integrating more information will improve the prediction accuracy. Additional work is planned to validate the identification algorithms on more data and in different use cases

Identifying anomalous file transfer events in LCLS workflow

This short paper reports our on-going work to study and identify anomalous file transfers for a large scientific facility known as Linac Coherent Light Source (LCLS). We identify the anomalies based on the statistical models extracted from the recent observations of the file transfer events. This data-driven approach could be used in different use cases to identify unusual events. More specifically, we propose two different identification strategies based on the different properties of the observed file transfers. Because these methods capture key aspects of the two different segments of the data transfer pipeline, they are able to make accurate identifications for their respective workflow components. The current anomaly detection algorithms only make use of the file sizes as the primary feature. We anticipate that integrating more information will improve the prediction accuracy. Additional work is planned to validate the identification algorithms on more data and in different use cases