OpenSearch/docs/reference/ml/anomaly-detection/detector-custom-rules.asciidoc

[role="xpack"]
[[ml-configuring-detector-custom-rules]]
=== Customizing detectors with custom rules

<<ml-rules,Custom rules>> enable you to change the behavior of anomaly
detectors based on domain-specific knowledge.

Custom rules describe _when_ a detector should take a certain _action_ instead
of following its default behavior. To specify the _when_ a rule uses
a `scope` and `conditions`. You can think of `scope` as the categorical
specification of a rule, while `conditions` are the numerical part.
A rule can have a scope, one or more conditions, or a combination of
scope and conditions.

Let us see how those can be configured by examples.

==== Specifying custom rule scope

Let us assume we are configuring an {anomaly-job} in order to detect DNS data
exfiltration. Our data contain fields "subdomain" and "highest_registered_domain".
We can use a detector that looks like
`high_info_content(subdomain) over highest_registered_domain`. If we run such a
job, it is possible that we discover a lot of anomalies on frequently used
domains that we have reasons to trust. As security analysts, we are not
interested in such anomalies. Ideally, we could instruct the detector to skip
results for domains that we consider safe. Using a rule with a scope allows us
to achieve this.

First, we need to create a list of our safe domains. Those lists are called 
_filters_ in {ml}. Filters can be shared across {anomaly-jobs}.

We create our filter using the {ref}/ml-put-filter.html[put filter API]:

[source,js]
----------------------------------
PUT _ml/filters/safe_domains
{
  "description": "Our list of safe domains",
  "items": ["safe.com", "trusted.com"]
}
----------------------------------
// CONSOLE
// TEST[skip:needs-licence]

Now, we can create our {anomaly-job} specifying a scope that uses the
`safe_domains`  filter for the `highest_registered_domain` field:

[source,js]
----------------------------------
PUT _ml/anomaly_detectors/dns_exfiltration_with_rule
{
  "analysis_config" : {
    "bucket_span":"5m",
    "detectors" :[{
      "function":"high_info_content",
      "field_name": "subdomain",
      "over_field_name": "highest_registered_domain",
      "custom_rules": [{
        "actions": ["skip_result"],
        "scope": {
          "highest_registered_domain": {
            "filter_id": "safe_domains",
            "filter_type": "include"
          }
        }
      }]
    }]
  },
  "data_description" : {
    "time_field":"timestamp"
  }
}
----------------------------------
// CONSOLE
// TEST[skip:needs-licence]

As time advances and we see more data and more results, we might encounter new 
domains that we want to add in the filter. We can do that by using the 
{ref}/ml-update-filter.html[update filter API]:

[source,js]
----------------------------------
POST _ml/filters/safe_domains/_update
{
  "add_items": ["another-safe.com"]
}
----------------------------------
// CONSOLE
// TEST[skip:setup:ml_filter_safe_domains]

Note that we can use any of the `partition_field_name`, `over_field_name`, or 
`by_field_name` fields in the `scope`.

In the following example we scope multiple fields:

[source,js]
----------------------------------
PUT _ml/anomaly_detectors/scoping_multiple_fields
{
  "analysis_config" : {
    "bucket_span":"5m",
    "detectors" :[{
      "function":"count",
      "partition_field_name": "my_partition",
      "over_field_name": "my_over",
      "by_field_name": "my_by",
      "custom_rules": [{
        "actions": ["skip_result"],
        "scope": {
          "my_partition": {
            "filter_id": "filter_1"
          },
          "my_over": {
            "filter_id": "filter_2"
          },
          "my_by": {
            "filter_id": "filter_3"
          }
        }
      }]
    }]
  },
  "data_description" : {
    "time_field":"timestamp"
  }
}
----------------------------------
// CONSOLE
// TEST[skip:needs-licence]

Such a detector will skip results when the values of all 3 scoped fields
are included in the referenced filters.

==== Specifying custom rule conditions

Imagine a detector that looks for anomalies in CPU utilization.
Given a machine that is idle for long enough, small movement in CPU could
result in anomalous results where the `actual` value is quite small, for 
example, 0.02. Given our knowledge about how CPU utilization behaves we might 
determine that anomalies with such small actual values are not interesting for 
investigation.

Let us now configure an {anomaly-job} with a rule that will skip results where
CPU utilization is less than 0.20.

[source,js]
----------------------------------
PUT _ml/anomaly_detectors/cpu_with_rule
{
  "analysis_config" : {
    "bucket_span":"5m",
    "detectors" :[{
      "function":"high_mean",
      "field_name": "cpu_utilization",
      "custom_rules": [{
        "actions": ["skip_result"],
        "conditions": [
          {
            "applies_to": "actual",
            "operator": "lt",
            "value": 0.20
          }
        ]
      }]
    }]
  },
  "data_description" : {
    "time_field":"timestamp"
  }
}
----------------------------------
// CONSOLE
// TEST[skip:needs-licence]

When there are multiple conditions they are combined with a logical `and`.
This is useful when we want the rule to apply to a range. We simply create
a rule with two conditions, one for each end of the desired range.

Here is an example where a count detector will skip results when the count
is greater than 30 and less than 50:

[source,js]
----------------------------------
PUT _ml/anomaly_detectors/rule_with_range
{
  "analysis_config" : {
    "bucket_span":"5m",
    "detectors" :[{
      "function":"count",
      "custom_rules": [{
        "actions": ["skip_result"],
        "conditions": [
          {
            "applies_to": "actual",
            "operator": "gt",
            "value": 30
          },
          {
            "applies_to": "actual",
            "operator": "lt",
            "value": 50
          }
        ]
      }]
    }]
  },
  "data_description" : {
    "time_field":"timestamp"
  }
}
----------------------------------
// CONSOLE
// TEST[skip:needs-licence]

==== Custom rules in the life-cycle of a job

Custom rules only affect results created after the rules were applied.
Let us imagine that we have configured an {anomaly-job} and it has been running
for some time. After observing its results we decide that we can employ
rules in order to get rid of some uninteresting results. We can use
the {ref}/ml-update-job.html[update {anomaly-job} API] to do so. However, the
rule we added will only be in effect for any results created from the moment we
added  the rule onwards. Past results will remain unaffected.

==== Using custom rules vs. filtering data

It might appear like using rules is just another way of filtering the data
that feeds into an {anomaly-job}. For example, a rule that skips results when
the partition field value is in a filter sounds equivalent to having a query
that filters out such documents. But it is not. There is a fundamental
difference. When the data is filtered before reaching a job it is as if they
never existed for the job. With rules, the data still reaches the job and
affects its behavior (depending on the rule actions).

For example, a rule with the `skip_result` action means all data will still
be modeled. On the other hand, a rule with the `skip_model_update` action means
results will still be created even though the model will not be updated by
data matched by a rule.