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Logbased Anomaly Detection with Deep Learning: How Far Are We? VanHoang LeHongyu ZhangThe University of Newcastle NSW, Australia vanhoang.le@uon.edu.auThe University of Newcastle NSW, Australia hongyu.zhang@newcastle.edu.auarXiv:2202.04301v2
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Identify the log data sources that will be used for anomaly detection
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Clean and preprocess the log data to remove any noise or irrelevant information
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Choose a suitable algorithm or method for detecting anomalies in the log data
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Set up thresholds or rules for flagging anomalies in the log data
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IT security teams looking to detect potential security threats
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Log-based anomaly detection is a method of identifying abnormal patterns or events in log data that deviate from the expected behavior.
Organizations or individuals who want to monitor their systems for any abnormal activities or security threats may choose to implement log-based anomaly detection.
Log-based anomaly detection is typically implemented through software tools or services that analyze log data in real-time or in batches, looking for any unusual patterns or events.
The purpose of log-based anomaly detection is to help organizations identify and respond to potential security threats or abnormal activities in their systems.
The information reported on log-based anomaly detection may vary depending on the specific requirements or goals of the organization, but typically it includes details about any detected anomalies, their severity, and any recommended actions.
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