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RIS citation export for TH2AO01: Log Anomaly Detection on EuXFEL Nodes

TY  - CONF
AU  - Sulc, A.
AU  - Eichler, A.
AU  - Wilksen, T.
ED  - Schaa, Volker RW
ED  - Götz, Andy
ED  - Venter, Johan
ED  - White, Karen
ED  - Robichon, Marie
ED  - Rowland, Vivienne
TI  - Log Anomaly Detection on EuXFEL Nodes
J2  - Proc. of ICALEPCS2023, Cape Town, South Africa, 09-13 October 2023
CY  - Cape Town, South Africa
T2  - International Conference on Accelerator and Large Experimental Physics Control Systems
T3  - 19
LA  - english
AB  - This article introduces a method to detect anomalies in the log data generated by control system nodes at the European XFEL accelerator. The primary aim of this proposed method is to offer operators a comprehensive understanding of the availability, status, and problems specific to each node. This information is vital for ensuring the smooth operation. The sequential nature of logs and the absence of a rich text corpus that is specific to our nodes pose a significant limitation for traditional and learning-based approaches for anomaly detection. To overcome this limitation, we propose a method that uses word embedding and models individual nodes as a sequence of these vectors that commonly co-occur, using a Hidden Markov Model (HMM). We score individual log entries by computing a probability ratio between the probability of the full log sequence including the new entry and the probability of just the previous log entries, without the new entry. This ratio indicates how probable the sequence becomes when the new entry is added. The proposed approach can detect anomalies by scoring and ranking log entries from EuXFEL nodes where entries that receive high scores are potential anomalies that do not fit the routine of the node. This method provides a warning system to alert operators about these irregular log events that may indicate issues. 
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 1126
EP  - 1133
KW  - FEL
KW  - network
KW  - embedded
KW  - GUI
KW  - monitoring
DA  - 2024/02
PY  - 2024
SN  - 2226-0358
SN  - 978-3-95450-238-7
DO  - doi:10.18429/JACoW-ICALEPCS2023-TH2AO01
UR  - https://jacow.org/icalepcs2023/papers/th2ao01.pdf
ER  -