Customizing detectors with custom rules
editCustomizing detectors with custom rules
editCustom 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
editLet us assume we are configuring an anomaly detection 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 machine learning. Filters can be shared across anomaly detection jobs.
We create our filter using the put filter API:
PUT _ml/filters/safe_domains { "description": "Our list of safe domains", "items": ["safe.com", "trusted.com"] }
Now, we can create our anomaly detection job specifying a scope that uses the
safe_domains
filter for the highest_registered_domain
field:
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" } }
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 update filter API:
POST _ml/filters/safe_domains/_update { "add_items": ["another-safe.com"] }
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:
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" } }
Such a detector will skip results when the values of all 3 scoped fields are included in the referenced filters.
Specifying custom rule conditions
editImagine 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 detection job with a rule that will skip results where CPU utilization is less than 0.20.
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" } }
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:
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" } }
Custom rules in the life-cycle of a job
editCustom rules only affect results created after the rules were applied. Let us imagine that we have configured an anomaly detection 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 update anomaly detection 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
editIt might appear like using rules is just another way of filtering the data that feeds into an anomaly detection 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.