anomalies
Description
Use the anomalies
command to look for events or field values that are unusual or unexpected.
The anomalies
command assigns an unexpectedness score to each event and places that score in a new field named unexpectedness
. Whether the event is considered anomalous or not depends on a threshold
value. The threshold
value is compared to the unexpectedness score. The event is considered unexpected or anomalous if the unexpectedness score is greater than the threshold
value.
After you use the anomalies
command in a search, look at the Interesting Fields list in the Search & Reporting window. Select the unexpectedness
field to see information about the values in your events.
The unexpectedness score of an event is calculated based on the similarity of that event (X) to a set of previous events (P).
The formula for unexpectedness is:
unexpectedness = [s(P and X) - s(P)] / [s(P) + s(X)]
In this formula, s( )
is a metric of how similar or uniform the data is. This formula provides a measure of how much adding X affects the similarity of the set of events. The formula also normalizes the results for the differing event sizes.
Use current Splunk machine learning (ML) tools to take advantage of the latest algorithms and get the most powerful results. See About the Splunk Machine Learning Toolkit in the Splunk Machine Learning Toolkit.
Syntax
The required syntax is in bold.
- anomalies
- [threshold=<num>]
- [labelonly=<bool>]
- [normalize=<bool>]
- [maxvalues=<num>]
- [field=<field>]
- [denylist=<filename>]
- [denylistthreshold=<num>]
- [by-clause]
Optional arguments
- threshold
- Syntax: threshold=<num>
- Description: A number to represent the upper limit of expected or normal events. If unexpectedness calculated for an event is greater than this threshold limit, the event is considered unexpected or anomalous.
- Default: 0.01
- labelonly
- Syntax: labelonly=<bool>
- Description: Specifies if you want the output result set to include all events or only the events that are above the threshold value. The
unexpectedness
field is appended to all events. Iflabelonly=true
, no events are removed. Iflabelonly=false
, events that have a unexpectedness score less than the threshold are removed from the output result set. - Default: false
- normalize
- Syntax: normalize=<bool>
- Description: Specifies whether or not to normalize numeric text in the fields. All characters in the field from 0 to 9 are considered identical for purposes of the algorithm. The placement and quantity of the numbers remains significant. When a field contains numeric data that should not be normalized but treated as categories, set
normalize=false
. - Default: true
- maxvalues
- Syntax: maxvalues=<num>
- Description: Specifies the size of the sliding set of previous events to include when determining the unexpectedness of a field value. By default the calculation uses the previous 100 events for the comparison. If the current event number is 1000, the calculation uses the values in events 900 to 999 in the calculation. If the current event number is 1500, the calculation uses the values in events 1400 to 1499 in the calculation. You can specify a number between 10 and 10000. Increasing the value of
maxvalues
increases the total CPU cost per event linearly. Large values have very long search runtimes. - Default: 100
- field
- Syntax: field=<field>
- Description: The field to analyze when determining the unexpectedness of an event.
- Default:
_raw
- denylist
- Syntax: denylist=<filename>
- Description: The name of a CSV file that contains a list of events that are expected and should be ignored. Any incoming event that is similar to an event in the denylist is treated as not anomalous, or expected, and given an unexpectedness score of 0.0. The CSV file must be located in the
$SPLUNK_HOME/var/run/splunk/csv
directory on the search head. If you have Splunk Cloud Platform and want to configure a denylist file, file a Support ticket.
- denylistthreshold
- Syntax: denylistthreshold=<num>
- Description: Specifies a similarity score threshold for matching incoming events to denylisted events. If the incoming event has a similarity score above the
denylistthreshold
, the event is marked as unexpected. - Default: 0.05
- by-clause
- Syntax: by <fieldlist>
- Description: Use to specify a list of fields to segregate the results for anomaly detection. For each combination of values for the specified fields, the events with those values are treated entirely separately.
Examples
1. Specify a denylist file of the events to ignore
The following example shows the interesting events, ignoring any events in the denylist 'boringevents'. Sort the event list in descending order, with highest value in the unexpectedness field listed first.
... | anomalies denylist=boringevents | sort -unexpectedness
2. Find anomalies in transactions
This example uses transactions to find regions of time that look unusual.
... | transaction maxpause=2s | anomalies
3. Identify anomalies by source
Look for anomalies in each source separately. A pattern in one source does not affect that it is anomalous in another source.
... | anomalies by source
4. Specify a threshold when identifying anomalies
This example shows how to tune a search for anomalies using the threshold
value.
Start with a search that uses the default threshold
value.
index=_internal | anomalies BY group | search group=*
This search looks at events in the _internal
index and calculates an unexpectedness
score for sets of events that have the same group
value.
- The sliding set of events that are used to calculate the
unexpectedness
score for each uniquegroup
value includes only the events that have the samegroup
value. - The
search
command is used to show events that only include thegroup
field.
The unexpectedness
and group
fields appear in the list of Interesting fields. Click on the field name and then click Yes to move the field to the Selected fields list. The fields are moved and also appear in the search results. Your results should look something like the following image.
The key-value pairs in the first event include group=pipeline
, name=indexerpipe
, processor=indexer
, cpu_seconds=0.022
, and so forth.
With the default threshold
, which is 0.01, you can see that some of these events might be very similar. The next search increases the threshold
a little:
index=_internal | anomalies threshold=0.03 by group | search group=*
With the higher threshold
value, the timestamps and key-value pairs show more distinction between each of the events.
Also, you might not want to hide the events that are not anomalous. Instead, you can add another field to your events that tells you whether or not the event is interesting to you. One way to do this is with the eval
command:
index=_internal | anomalies threshold=0.03 labelonly=true by group | search group=* | eval threshold=0.03 | eval score=if(unexpectedness>=threshold, "anomalous", "boring")
This search uses labelonly=true
so that the boring events are still retained in the results list. The eval
command is used to define a field named threshold
and set it to the threshold value. This has to be done explicitly because the threshold
attribute of the anomalies
command is not a field.
The second eval
command is used to define another new field, score
, that is either "anomalous" or "boring" based on how the unexpectedness
compares to the threshold
value. The following image shows a snapshot of the results.
See also
analyzefields | anomalousvalue |
This documentation applies to the following versions of Splunk Cloud Platform™: 9.2.2406 (latest FedRAMP release), 8.2.2112, 8.2.2202, 9.0.2205, 8.2.2201, 8.2.2203, 9.0.2208, 9.0.2209, 9.0.2303, 9.0.2305, 9.1.2308, 9.1.2312, 9.2.2403
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