By default, as described in Analyzing the Past and Present, anomaly detection is unsupervised and the machine learning models have no awareness of the domain of your data. As a result, machine learning jobs might identify events that are statistically significant but are uninteresting when you know the larger context. Machine learning custom rules enable you to customize anomaly detection.
Custom rules instruct anomaly detectors to change their behavior based on domain-specific knowledge that you provide. When you create a rule, you can specify conditions, scope, and actions. When the conditions of a rule are satisfied, its actions are triggered.
For example, if you have an anomaly detector that is analyzing CPU usage, you might decide you are only interested in anomalies where the CPU usage is greater than a certain threshold. You can define a rule with conditions and actions that instruct the detector to refrain from generating machine learning results when there are anomalous events related to low CPU usage. You might also decide to add a scope for the rule, such that it applies only to certain machines. The scope is defined by using machine learning filters.
Filters contain a list of values that you can use to include or exclude events from the machine learning analysis. You can use the same filter in multiple jobs.
If you are analyzing web traffic, you might create a filter that contains a list of IP addresses. For example, maybe they are IP addresses that you trust to upload data to your website or to send large amounts of data from behind your firewall. You can define the scope of a rule such that it triggers only when a specific field in your data matches one of the values in the filter. Alternatively, you can make it trigger only when the field value does not match one of the filter values. You therefore have much greater control over which anomalous events affect the machine learning model and appear in the machine learning results.
For more information, see Customizing detectors with custom rules.