Rare functions
editRare functions
editThe rare functions detect values that occur rarely in time or rarely for a population.
The rare
analysis detects anomalies according to the number of distinct rare
values. This differs from freq_rare
, which detects anomalies according to the
number of times (frequency) rare values occur.
-
The
rare
andfreq_rare
functions should not be used in conjunction withexclude_frequent
. -
You cannot create forecasts for jobs that contain
rare
orfreq_rare
functions. - Shorter bucket spans (less than 1 hour, for example) are recommended when looking for rare events. The functions model whether something happens in a bucket at least once. With longer bucket spans, it is more likely that entities will be seen in a bucket and therefore they appear less rare. Picking the ideal the bucket span depends on the characteristics of the data with shorter bucket spans typically being measured in minutes, not hours.
- To model rare data, a learning period of at least 20 buckets is required for typical data.
The machine learning features include the following rare functions:
Rare
editThe rare
function detects values that occur rarely in time or rarely for a
population. It detects anomalies according to the number of distinct rare values.
This function supports the following properties:
-
by_field_name
(required) -
over_field_name
(optional) -
partition_field_name
(optional)
For more information about those properties, see Detector Configuration Objects.
Example 1: Analyzing status codes with the rare function.
{ "function" : "rare", "by_field_name" : "status" }
If you use this rare
function in a detector in your job, it detects values
that are rare in time. It models status codes that occur over time and detects
when rare status codes occur compared to the past. For example, you can detect
status codes in a web access log that have never (or rarely) occurred before.
Example 2: Analyzing status codes in a population with the rare function.
{ "function" : "rare", "by_field_name" : "status", "over_field_name" : "clientip" }
If you use this rare
function in a detector in your job, it detects values
that are rare in a population. It models status code and client IP interactions
that occur. It defines a rare status code as one that occurs for few client IP
values compared to the population. It detects client IP values that experience
one or more distinct rare status codes compared to the population. For example
in a web access log, a clientip
that experiences the highest number of
different rare status codes compared to the population is regarded as highly
anomalous. This analysis is based on the number of different status code values,
not the count of occurrences.
To define a status code as rare the machine learning features look at the number of distinct status codes that occur, not the number of times the status code occurs. If a single client IP experiences a single unique status code, this is rare, even if it occurs for that client IP in every bucket.
Freq_rare
editThe freq_rare
function detects values that occur rarely for a population.
It detects anomalies according to the number of times (frequency) that rare
values occur.
This function supports the following properties:
-
by_field_name
(required) -
over_field_name
(required) -
partition_field_name
(optional)
For more information about those properties, see Detector Configuration Objects.
Example 3: Analyzing URI values in a population with the freq_rare function.
{ "function" : "freq_rare", "by_field_name" : "uri", "over_field_name" : "clientip" }
If you use this freq_rare
function in a detector in your job, it
detects values that are frequently rare in a population. It models URI paths and
client IP interactions that occur. It defines a rare URI path as one that is
visited by few client IP values compared to the population. It detects the
client IP values that experience many interactions with rare URI paths compared
to the population. For example in a web access log, a client IP that visits
one or more rare URI paths many times compared to the population is regarded as
highly anomalous. This analysis is based on the count of interactions with rare
URI paths, not the number of different URI path values.
Defining a URI path as rare happens the same way as you can see in the case of the status codes above: the analytics consider the number of distinct values that occur and not the number of times the URI path occurs. If a single client IP visits a single unique URI path, this is rare, even if it occurs for that client IP in every bucket.