469 lines
18 KiB
Plaintext
469 lines
18 KiB
Plaintext
[role="xpack"]
|
|
[testenv="platinum"]
|
|
[[ml-results-resource]]
|
|
=== Results Resources
|
|
|
|
Several different result types are created for each job. You can query anomaly
|
|
results for _buckets_, _influencers_, and _records_ by using the results API.
|
|
Summarized bucket results over multiple jobs can be queried as well; those
|
|
results are called _overall buckets_.
|
|
|
|
Results are written for each `bucket_span`. The timestamp for the results is the
|
|
start of the bucket time interval.
|
|
|
|
The results include scores, which are calculated for each anomaly result type and
|
|
each bucket interval. These scores are aggregated in order to reduce noise, and
|
|
normalized in order to identify and rank the most mathematically significant
|
|
anomalies.
|
|
|
|
Bucket results provide the top level, overall view of the job and are ideal for
|
|
alerts. For example, the bucket results might indicate that at 16:05 the system
|
|
was unusual. This information is a summary of all the anomalies, pinpointing
|
|
when they occurred.
|
|
|
|
Influencer results show which entities were anomalous and when. For example,
|
|
the influencer results might indicate that at 16:05 `user_name: Bob` was unusual.
|
|
This information is a summary of all the anomalies for each entity, so there
|
|
can be a lot of these results. Once you have identified a notable bucket time,
|
|
you can look to see which entities were significant.
|
|
|
|
Record results provide details about what the individual anomaly was, when it
|
|
occurred and which entity was involved. For example, the record results might
|
|
indicate that at 16:05 Bob sent 837262434 bytes, when the typical value was
|
|
1067 bytes. Once you have identified a bucket time and perhaps a significant
|
|
entity too, you can drill through to the record results in order to investigate
|
|
the anomalous behavior.
|
|
|
|
Categorization results contain the definitions of _categories_ that have been
|
|
identified. These are only applicable for jobs that are configured to analyze
|
|
unstructured log data using categorization. These results do not contain a
|
|
timestamp or any calculated scores. For more information, see
|
|
{xpack-ref}/ml-configuring-categories.html[Categorizing Log Messages].
|
|
|
|
* <<ml-results-buckets,Buckets>>
|
|
* <<ml-results-influencers,Influencers>>
|
|
* <<ml-results-records,Records>>
|
|
* <<ml-results-categories,Categories>>
|
|
* <<ml-results-overall-buckets,Overall Buckets>>
|
|
|
|
NOTE: All of these resources and properties are informational; you cannot
|
|
change their values.
|
|
|
|
[float]
|
|
[[ml-results-buckets]]
|
|
==== Buckets
|
|
|
|
Bucket results provide the top level, overall view of the job and are best for
|
|
alerting.
|
|
|
|
Each bucket has an `anomaly_score`, which is a statistically aggregated and
|
|
normalized view of the combined anomalousness of all the record results within
|
|
each bucket.
|
|
|
|
One bucket result is written for each `bucket_span` for each job, even if it is
|
|
not considered to be anomalous. If the bucket is not anomalous, it has an
|
|
`anomaly_score` of zero.
|
|
|
|
When you identify an anomalous bucket, you can investigate further by expanding
|
|
the bucket resource to show the records as nested objects. Alternatively, you
|
|
can access the records resource directly and filter by the date range.
|
|
|
|
A bucket resource has the following properties:
|
|
|
|
`anomaly_score`::
|
|
(number) The maximum anomaly score, between 0-100, for any of the bucket
|
|
influencers. This is an overall, rate-limited score for the job. All the
|
|
anomaly records in the bucket contribute to this score. This value might be
|
|
updated as new data is analyzed.
|
|
|
|
`bucket_influencers`::
|
|
(array) An array of bucket influencer objects.
|
|
For more information, see <<ml-results-bucket-influencers,Bucket Influencers>>.
|
|
|
|
`bucket_span`::
|
|
(number) The length of the bucket in seconds.
|
|
This value matches the `bucket_span` that is specified in the job.
|
|
|
|
`event_count`::
|
|
(number) The number of input data records processed in this bucket.
|
|
|
|
`initial_anomaly_score`::
|
|
(number) The maximum `anomaly_score` for any of the bucket influencers.
|
|
This is the initial value that was calculated at the time the bucket was
|
|
processed.
|
|
|
|
`is_interim`::
|
|
(boolean) If true, this is an interim result. In other words, the bucket
|
|
results are calculated based on partial input data.
|
|
|
|
`job_id`::
|
|
(string) The unique identifier for the job that these results belong to.
|
|
|
|
`processing_time_ms`::
|
|
(number) The amount of time, in milliseconds, that it took to analyze the
|
|
bucket contents and calculate results.
|
|
|
|
`result_type`::
|
|
(string) Internal. This value is always set to `bucket`.
|
|
|
|
`timestamp`::
|
|
(date) The start time of the bucket. This timestamp uniquely identifies the
|
|
bucket. +
|
|
|
|
NOTE: Events that occur exactly at the timestamp of the bucket are included in
|
|
the results for the bucket.
|
|
|
|
|
|
[float]
|
|
[[ml-results-bucket-influencers]]
|
|
==== Bucket Influencers
|
|
|
|
Bucket influencer results are available as nested objects contained within
|
|
bucket results. These results are an aggregation for each type of influencer.
|
|
For example, if both `client_ip` and `user_name` were specified as influencers,
|
|
then you would be able to determine when the `client_ip` or `user_name` values
|
|
were collectively anomalous.
|
|
|
|
There is a built-in bucket influencer called `bucket_time` which is always
|
|
available. This bucket influencer is the aggregation of all records in the
|
|
bucket; it is not just limited to a type of influencer.
|
|
|
|
NOTE: A bucket influencer is a type of influencer. For example, `client_ip` or
|
|
`user_name` can be bucket influencers, whereas `192.168.88.2` and `Bob` are
|
|
influencers.
|
|
|
|
An bucket influencer object has the following properties:
|
|
|
|
`anomaly_score`::
|
|
(number) A normalized score between 0-100, which is calculated for each bucket
|
|
influencer. This score might be updated as newer data is analyzed.
|
|
|
|
`bucket_span`::
|
|
(number) The length of the bucket in seconds. This value matches the `bucket_span`
|
|
that is specified in the job.
|
|
|
|
`initial_anomaly_score`::
|
|
(number) The score between 0-100 for each bucket influencer. This score is
|
|
the initial value that was calculated at the time the bucket was processed.
|
|
|
|
`influencer_field_name`::
|
|
(string) The field name of the influencer. For example `client_ip` or
|
|
`user_name`.
|
|
|
|
`influencer_field_value`::
|
|
(string) The field value of the influencer. For example `192.168.88.2` or
|
|
`Bob`.
|
|
|
|
`is_interim`::
|
|
(boolean) If true, this is an interim result. In other words, the bucket
|
|
influencer results are calculated based on partial input data.
|
|
|
|
`job_id`::
|
|
(string) The unique identifier for the job that these results belong to.
|
|
|
|
`probability`::
|
|
(number) The probability that the bucket has this behavior, in the range 0
|
|
to 1. For example, 0.0000109783. This value can be held to a high precision
|
|
of over 300 decimal places, so the `anomaly_score` is provided as a
|
|
human-readable and friendly interpretation of this.
|
|
|
|
`raw_anomaly_score`::
|
|
(number) Internal.
|
|
|
|
`result_type`::
|
|
(string) Internal. This value is always set to `bucket_influencer`.
|
|
|
|
`timestamp`::
|
|
(date) The start time of the bucket for which these results were calculated.
|
|
|
|
[float]
|
|
[[ml-results-influencers]]
|
|
==== Influencers
|
|
|
|
Influencers are the entities that have contributed to, or are to blame for,
|
|
the anomalies. Influencer results are available only if an
|
|
`influencer_field_name` is specified in the job configuration.
|
|
|
|
Influencers are given an `influencer_score`, which is calculated based on the
|
|
anomalies that have occurred in each bucket interval. For jobs with more than
|
|
one detector, this gives a powerful view of the most anomalous entities.
|
|
|
|
For example, if you are analyzing unusual bytes sent and unusual domains
|
|
visited and you specified `user_name` as the influencer, then an
|
|
`influencer_score` for each anomalous user name is written per bucket. For
|
|
example, if `user_name: Bob` had an `influencer_score` greater than 75, then
|
|
`Bob` would be considered very anomalous during this time interval in one or
|
|
both of those areas (unusual bytes sent or unusual domains visited).
|
|
|
|
One influencer result is written per bucket for each influencer that is
|
|
considered anomalous.
|
|
|
|
When you identify an influencer with a high score, you can investigate further
|
|
by accessing the records resource for that bucket and enumerating the anomaly
|
|
records that contain the influencer.
|
|
|
|
An influencer object has the following properties:
|
|
|
|
`bucket_span`::
|
|
(number) The length of the bucket in seconds. This value matches the `bucket_span`
|
|
that is specified in the job.
|
|
|
|
`influencer_score`::
|
|
(number) A normalized score between 0-100, which is based on the probability
|
|
of the influencer in this bucket aggregated across detectors. Unlike
|
|
`initial_influencer_score`, this value will be updated by a re-normalization
|
|
process as new data is analyzed.
|
|
|
|
`initial_influencer_score`::
|
|
(number) A normalized score between 0-100, which is based on the probability
|
|
of the influencer aggregated across detectors. This is the initial value that
|
|
was calculated at the time the bucket was processed.
|
|
|
|
`influencer_field_name`::
|
|
(string) The field name of the influencer.
|
|
|
|
`influencer_field_value`::
|
|
(string) The entity that influenced, contributed to, or was to blame for the
|
|
anomaly.
|
|
|
|
`is_interim`::
|
|
(boolean) If true, this is an interim result. In other words, the influencer
|
|
results are calculated based on partial input data.
|
|
|
|
`job_id`::
|
|
(string) The unique identifier for the job that these results belong to.
|
|
|
|
`probability`::
|
|
(number) The probability that the influencer has this behavior, in the range
|
|
0 to 1. For example, 0.0000109783. This value can be held to a high precision
|
|
of over 300 decimal places, so the `influencer_score` is provided as a
|
|
human-readable and friendly interpretation of this.
|
|
// For example, 0.03 means 3%. This value is held to a high precision of over
|
|
//300 decimal places. In scientific notation, a value of 3.24E-300 is highly
|
|
//unlikely and therefore highly anomalous.
|
|
|
|
`result_type`::
|
|
(string) Internal. This value is always set to `influencer`.
|
|
|
|
`timestamp`::
|
|
(date) The start time of the bucket for which these results were calculated.
|
|
|
|
NOTE: Additional influencer properties are added, depending on the fields being
|
|
analyzed. For example, if it's analyzing `user_name` as an influencer, then a
|
|
field `user_name` is added to the result document. This information enables you to
|
|
filter the anomaly results more easily.
|
|
|
|
|
|
[float]
|
|
[[ml-results-records]]
|
|
==== Records
|
|
|
|
Records contain the detailed analytical results. They describe the anomalous
|
|
activity that has been identified in the input data based on the detector
|
|
configuration.
|
|
|
|
For example, if you are looking for unusually large data transfers, an anomaly
|
|
record can identify the source IP address, the destination, the time window
|
|
during which it occurred, the expected and actual size of the transfer, and the
|
|
probability of this occurrence.
|
|
|
|
There can be many anomaly records depending on the characteristics and size of
|
|
the input data. In practice, there are often too many to be able to manually
|
|
process them. The {xpackml} features therefore perform a sophisticated
|
|
aggregation of the anomaly records into buckets.
|
|
|
|
The number of record results depends on the number of anomalies found in each
|
|
bucket, which relates to the number of time series being modeled and the number of
|
|
detectors.
|
|
|
|
A record object has the following properties:
|
|
|
|
`actual`::
|
|
(array) The actual value for the bucket.
|
|
|
|
`bucket_span`::
|
|
(number) The length of the bucket in seconds.
|
|
This value matches the `bucket_span` that is specified in the job.
|
|
|
|
`by_field_name`::
|
|
(string) The name of the analyzed field. This value is present only if
|
|
it is specified in the detector. For example, `client_ip`.
|
|
|
|
`by_field_value`::
|
|
(string) The value of `by_field_name`. This value is present only if
|
|
it is specified in the detector. For example, `192.168.66.2`.
|
|
|
|
`causes`::
|
|
(array) For population analysis, an over field must be specified in the
|
|
detector. This property contains an array of anomaly records that are the
|
|
causes for the anomaly that has been identified for the over field. If no
|
|
over fields exist, this field is not present. This sub-resource contains
|
|
the most anomalous records for the `over_field_name`. For scalability reasons,
|
|
a maximum of the 10 most significant causes of the anomaly are returned. As
|
|
part of the core analytical modeling, these low-level anomaly records are
|
|
aggregated for their parent over field record. The causes resource contains
|
|
similar elements to the record resource, namely `actual`, `typical`,
|
|
`*_field_name` and `*_field_value`. Probability and scores are not applicable
|
|
to causes.
|
|
|
|
`detector_index`::
|
|
(number) A unique identifier for the detector.
|
|
|
|
`field_name`::
|
|
(string) Certain functions require a field to operate on, for example, `sum()`.
|
|
For those functions, this value is the name of the field to be analyzed.
|
|
|
|
`function`::
|
|
(string) The function in which the anomaly occurs, as specified in the
|
|
detector configuration. For example, `max`.
|
|
|
|
`function_description`::
|
|
(string) The description of the function in which the anomaly occurs, as
|
|
specified in the detector configuration.
|
|
|
|
`influencers`::
|
|
(array) If `influencers` was specified in the detector configuration, then
|
|
this array contains influencers that contributed to or were to blame for an
|
|
anomaly.
|
|
|
|
`initial_record_score`::
|
|
(number) A normalized score between 0-100, which is based on the
|
|
probability of the anomalousness of this record. This is the initial value
|
|
that was calculated at the time the bucket was processed.
|
|
|
|
`is_interim`::
|
|
(boolean) If true, this is an interim result. In other words, the anomaly
|
|
record is calculated based on partial input data.
|
|
|
|
`job_id`::
|
|
(string) The unique identifier for the job that these results belong to.
|
|
|
|
`over_field_name`::
|
|
(string) The name of the over field that was used in the analysis. This value
|
|
is present only if it was specified in the detector. Over fields are used
|
|
in population analysis. For example, `user`.
|
|
|
|
`over_field_value`::
|
|
(string) The value of `over_field_name`. This value is present only if it
|
|
was specified in the detector. For example, `Bob`.
|
|
|
|
`partition_field_name`::
|
|
(string) The name of the partition field that was used in the analysis. This
|
|
value is present only if it was specified in the detector. For example,
|
|
`region`.
|
|
|
|
`partition_field_value`::
|
|
(string) The value of `partition_field_name`. This value is present only if
|
|
it was specified in the detector. For example, `us-east-1`.
|
|
|
|
`probability`::
|
|
(number) The probability of the individual anomaly occurring, in the range
|
|
0 to 1. For example, 0.0000772031. This value can be held to a high precision
|
|
of over 300 decimal places, so the `record_score` is provided as a
|
|
human-readable and friendly interpretation of this.
|
|
//In scientific notation, a value of 3.24E-300 is highly unlikely and therefore
|
|
//highly anomalous.
|
|
|
|
`multi_bucket_impact`::
|
|
(number) an indication of how strongly an anomaly is multi bucket or single bucket.
|
|
The value is on a scale of -5 to +5 where -5 means the anomaly is purely single
|
|
bucket and +5 means the anomaly is purely multi bucket.
|
|
|
|
`record_score`::
|
|
(number) A normalized score between 0-100, which is based on the probability
|
|
of the anomalousness of this record. Unlike `initial_record_score`, this
|
|
value will be updated by a re-normalization process as new data is analyzed.
|
|
|
|
`result_type`::
|
|
(string) Internal. This is always set to `record`.
|
|
|
|
`timestamp`::
|
|
(date) The start time of the bucket for which these results were calculated.
|
|
|
|
`typical`::
|
|
(array) The typical value for the bucket, according to analytical modeling.
|
|
|
|
NOTE: Additional record properties are added, depending on the fields being
|
|
analyzed. For example, if it's analyzing `hostname` as a _by field_, then a field
|
|
`hostname` is added to the result document. This information enables you to
|
|
filter the anomaly results more easily.
|
|
|
|
|
|
[float]
|
|
[[ml-results-categories]]
|
|
==== Categories
|
|
|
|
When `categorization_field_name` is specified in the job configuration, it is
|
|
possible to view the definitions of the resulting categories. A category
|
|
definition describes the common terms matched and contains examples of matched
|
|
values.
|
|
|
|
The anomaly results from a categorization analysis are available as bucket,
|
|
influencer, and record results. For example, the results might indicate that
|
|
at 16:45 there was an unusual count of log message category 11. You can then
|
|
examine the description and examples of that category.
|
|
|
|
A category resource has the following properties:
|
|
|
|
`category_id`::
|
|
(unsigned integer) A unique identifier for the category.
|
|
|
|
`examples`::
|
|
(array) A list of examples of actual values that matched the category.
|
|
|
|
`grok_pattern`::
|
|
experimental[] (string) A Grok pattern that could be used in Logstash or an
|
|
Ingest Pipeline to extract fields from messages that match the category. This
|
|
field is experimental and may be changed or removed in a future release. The
|
|
Grok patterns that are found are not optimal, but are often a good starting
|
|
point for manual tweaking.
|
|
|
|
`job_id`::
|
|
(string) The unique identifier for the job that these results belong to.
|
|
|
|
`max_matching_length`::
|
|
(unsigned integer) The maximum length of the fields that matched the category.
|
|
The value is increased by 10% to enable matching for similar fields that have
|
|
not been analyzed.
|
|
|
|
`regex`::
|
|
(string) A regular expression that is used to search for values that match the
|
|
category.
|
|
|
|
`terms`::
|
|
(string) A space separated list of the common tokens that are matched in
|
|
values of the category.
|
|
|
|
[float]
|
|
[[ml-results-overall-buckets]]
|
|
==== Overall Buckets
|
|
|
|
Overall buckets provide a summary of bucket results over multiple jobs.
|
|
Their `bucket_span` equals the longest `bucket_span` of the jobs in question.
|
|
The `overall_score` is the `top_n` average of the max `anomaly_score` per job
|
|
within the overall bucket time interval.
|
|
This means that you can fine-tune the `overall_score` so that it is more
|
|
or less sensitive to the number of jobs that detect an anomaly at the same time.
|
|
|
|
An overall bucket resource has the following properties:
|
|
|
|
`timestamp`::
|
|
(date) The start time of the overall bucket.
|
|
|
|
`bucket_span`::
|
|
(number) The length of the bucket in seconds. Matches the `bucket_span`
|
|
of the job with the longest one.
|
|
|
|
`overall_score`::
|
|
(number) The `top_n` average of the max bucket `anomaly_score` per job.
|
|
|
|
`jobs`::
|
|
(array) An array of objects that contain the `max_anomaly_score` per `job_id`.
|
|
|
|
`is_interim`::
|
|
(boolean) If true, this is an interim result. In other words, the anomaly
|
|
record is calculated based on partial input data.
|
|
|
|
`result_type`::
|
|
(string) Internal. This is always set to `overall_bucket`.
|