@@ -32,33 +32,34 @@ PUT _xpack/ml/anomaly_detectors/population
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{
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"description" : "Population analysis",
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"analysis_config" : {
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- "bucket_span":"10m ",
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+ "bucket_span":"15m ",
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"influencers": [
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- "username "
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+ "clientip "
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],
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"detectors": [
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{
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"function": "mean",
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- "field_name": "bytesSent ",
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- "over_field_name": "username " <1>
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+ "field_name": "bytes ",
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+ "over_field_name": "clientip " <1>
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}
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]
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},
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"data_description" : {
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- "time_field":"@ timestamp",
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+ "time_field":"timestamp",
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"time_format": "epoch_ms"
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}
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}
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----------------------------------
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//CONSOLE
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// TEST[skip:needs-licence]
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- <1> This `over_field_name` property indicates that the metrics for each user (
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- as identified by their `username` value ) are analyzed relative to other users
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+ <1> This `over_field_name` property indicates that the metrics for each client (
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+ as identified by their IP address ) are analyzed relative to other clients
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in each bucket.
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If your data is stored in {es}, you can use the population job wizard in {kib}
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- to create a job with these same properties. For example, the population job
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- wizard provides the following job settings:
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+ to create a job with these same properties. For example, if you add the sample
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+ web logs in {kib}, you can use the following job settings in the population job
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+ wizard:
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[role="screenshot"]
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image::images/ml-population-job.jpg["Job settings in the population job wizard]
@@ -81,6 +82,6 @@ details about the anomalies:
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[role="screenshot"]
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image::images/ml-population-anomaly.jpg["Anomaly details for a specific user"]
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- In this example, the user identified as `antonette` sent a high volume of bytes
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- on the date and time shown. This event is anomalous because the mean is two times
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- higher than the expected behavior of the population.
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+ In this example, the client IP address `29.64.62.83` received a high volume of
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+ bytes on the date and time shown. This event is anomalous because the mean is
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+ three times higher than the expected behavior of the population.
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