[role="xpack"] [[ml-configuring-detector-custom-rules]] = Customizing detectors with custom rules <> – or _job rules_ as {kib} refers to them – 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. For the full list of specification details, see the {ref}/ml-put-job.html#put-customrules[`custom_rules` object] in the create {anomaly-jobs} API. [[ml-custom-rules-scope]] == 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}. You can create a filter in **Anomaly Detection > Settings > Filter Lists** in {kib} or by using the {ref}/ml-put-filter.html[put filter API]: [source,console] ---------------------------------- PUT _ml/filters/safe_domains { "description": "Our list of safe domains", "items": ["safe.com", "trusted.com"] } ---------------------------------- // 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,console] ---------------------------------- 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" } } ---------------------------------- // 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 in the **Anomaly Detection > Settings > Filter Lists** in {kib} or by using the {ref}/ml-update-filter.html[update filter API]: [source,console] ---------------------------------- POST _ml/filters/safe_domains/_update { "add_items": ["another-safe.com"] } ---------------------------------- // 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,console] ---------------------------------- 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" } } ---------------------------------- // TEST[skip:needs-licence] Such a detector skips results when the values of all three scoped fields are included in the referenced filters. [[ml-custom-rules-conditions]] == 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 skips results where CPU utilization is less than 0.20. [source,console] ---------------------------------- 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" } } ---------------------------------- // 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 create a rule with two conditions, one for each end of the desired range. Here is an example where a count detector skips results when the count is greater than 30 and less than 50: [source,console] ---------------------------------- 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" } } ---------------------------------- // TEST[skip:needs-licence] [[ml-custom-rules-lifecycle]] == Custom rules in the lifecycle 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 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 remain unaffected. [[ml-custom-rules-filtering]] == 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. However, 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 is still modeled. On the other hand, a rule with the `skip_model_update` action means results are still created even though the model is not updated by data matched by a rule.