IMPORTANT: No additional bug fixes or documentation updates will be released for this version. For the latest information, see the current release documentation.

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Create rule

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Create rule

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When used with API key authentication, the user’s key gets assigned to the affected rules. If the user’s key gets deleted or the user becomes inactive, the rules will stop running.

If the API key that is used for authorization has different privileges than the key that created or most recently updated the rule, the rule behavior might change.

Creates a new detection rule.

You can create the following types of rules:

Custom query: Searches the defined indices and creates an alert when a document matches the rule’s KQL query.
Event correlation: Searches the defined indices and creates an alert when results match an Event Query Language (EQL) query.
Threshold: Searches the defined indices and creates an alert when the number of times the specified field’s value meets the threshold during a single execution. When there are multiple values that meet the threshold, an alert is generated for each value.

For example, if the threshold field is source.ip and its value is 10, an alert is generated for every source IP address that appears in at least 10 of the rule’s search results. If you’re interested, see Terms Aggregation for more information.
Indicator match: Creates an alert when fields match values defined in the specified Elasticsearch index. For example, you can create an index for IP addresses and use this index to create an alert whenever an event’s destination.ip equals a value in the index. The index’s field mappings should be ECS-compliant.
Machine learning rules: Creates an alert when a machine learning job discovers an anomaly above the defined threshold (see Anomaly Detection with Machine Learning).
New terms: Generates an alert for each new term detected in source documents within a specified time range.

To create machine learning rules, you must have the appropriate license or use a cloud deployment. Additionally, for the machine learning rule to function correctly, the associated machine learning job must be running.

To retrieve machine learning job IDs, which are required to create machine learning jobs, call the Elasticsearch Get jobs API. Machine learning jobs that contain siem in the groups field can be used to create rules:

...
"job_id": "linux_anomalous_network_activity_ecs",
"job_type": "anomaly_detector",
"job_version": "7.7.0",
"groups": [
  "auditbeat",
  "process",
  "siem"
],
...

Additionally, you can set up notifications for when rules create alerts. The notifications use the Kibana Alerting and Actions framework. Each action type requires a connector. Connectors store the information required to send notifications via external systems. The following connector types are supported for rule notifications:

Slack
Email
PagerDuty
Webhook
Microsoft Teams
IBM Resilient
Jira
ServiceNow ITSM

For more information on PagerDuty fields, see Send a v2 Event.

To retrieve connector IDs, which are required to configure rule notifications, call the Kibana Find objects API with "type": "action" in the request payload.

For detailed information on Kibana actions and alerting, and additional API calls, see:

Request URL

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POST <kibana host>:<port>/api/detection_engine/rules

Request body

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A JSON object that defines the rule’s values:

Required fields for all rule types

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Name	Type	Description
description	String	The rule’s description.
name	String	The rule’s name.
risk_score	Integer	A numerical representation of the alert’s severity from 0 to 100, where: `0` - `21` represents low severity `22` - `47` represents medium severity `48` - `73` represents high severity `74` - `100` represents critical severity
severity	String	Severity level of alerts produced by the rule, which must be one of the following: `low`: Alerts that are of interest but generally not considered to be security incidents `medium`: Alerts that require investigation `high`: Alerts that require immediate investigation `critical`: Alerts that indicate it is highly likely a security incident has occurred
type	String	Data type on which the rule is based: `eql`: EQL query (see Event Query Language). `query`: query with or without additional filters. `saved_query`: saved search, identified in the `saved_id` field. `machine_learning`: rule based on a machine learning job’s anomaly scores. `threat_match`: rule that matches event values with values in the specified Elasticsearch index. `threshold`: rule based on the number of times a `query` matches the specified field. `new_terms`: rule that alerts on values that have not been seen before

Required field for query, indicator match, threshold, and new terms rules

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Name	Type	Description
query	String	Query used by the rule to create alerts. For indicator match rules, only the query’s results are used to determine whether an alert is generated.

Required field for threshold rules

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Name	Type	Description
threshold	Object	Defines the field and threshold value for when alerts are generated, where: `cardinality` (Array of length 1): The field on which the cardinality is applied. `cardinality.field` (string, required): The field on which to calculate and compare the cardinality. `cardinality.value` (integer, required): The threshold value from which an alert is generated based on unique number of values of `cardinality.field`. `field` (string or string[], required): The field on which the threshold is applied. If you specify an empty array (`[]`), alerts are generated when the query returns at least the number of results specified in the `value` field. `value` (integer, required): The threshold value from which an alert is generated.

Required field for saved query rules

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Name	Type	Description
saved_id	String	Kibana saved search used by the rule to create alerts.

Required field for event correlation rules

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Name	Type	Description
language	String	Must be `eql`.

Required fields for machine learning rules

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Name	Type	Description
anomaly_threshold	Integer	Anomaly score threshold above which the rule creates an alert. Valid values are from `0` to `100`.
machine_learning_job_id	String[]	Machine learning job ID(s) the rule monitors for anomaly scores.

Required fields for indicator match rules

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Name Type Description

Name	Type	Description
threat_index	String[]	Elasticsearch indices used to check which field values generate alerts.
threat_query	String	Query used to determine which fields in the Elasticsearch index are used for generating alerts.
threat_mapping	Object[]	Array of `entries` objects that define mappings between the source event fields and the values in the Elasticsearch threat index. Each `entries` object must contain these fields: `field`: field from the event indices on which the rule runs `type`: must be `mapping` `value`: field from the Elasticsearch threat index You can use Boolean `and` and `or` logic to define the conditions for when matching fields and values generate alerts. Sibling `entries` objects are evaluated using `or` logic, whereas multiple entries in a single `entries` object use `and` logic. See below for an example that uses both `and` and `or` logic.

threat_index

String[]

Elasticsearch indices used to check which field values generate alerts.

threat_query

String

Query used to determine which fields in the Elasticsearch index are used for generating alerts.

threat_mapping

Object[]

Array of entries objects that define mappings between the source event fields and the values in the Elasticsearch threat index. Each entries object must contain these fields:

field: field from the event indices on which the rule runs
type: must be mapping
value: field from the Elasticsearch threat index

You can use Boolean and and or logic to define the conditions for when matching fields and values generate alerts. Sibling entries objects are evaluated using or logic, whereas multiple entries in a single entries object use and logic. See below for an example that uses both and and or logic.

Required fields for new terms rules

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Name	Type	Description
new_terms_fields	String[]	Fields to monitor for new values. Must contain 1 field name.
history_window_start	String	Start date to use when checking if a term has been seen before. Supports relative dates – for example, `now-30d` will search the last 30 days of data when checking if a term is new. We do not recommend using absolute dates, which can cause issues with rule performance due to querying increasing amounts of data over time.

Optional fields for all rule types

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Name	Type	Description
actions	actions[]	Array defining the automated actions (notifications) taken when alerts are generated.
author	String[]	The rule’s author.
building_block_type	String	Determines if the rule acts as a building block. By default, building-block alerts are not displayed in the UI. These rules are used as a foundation for other rules that do generate alerts. Its value must be `default`. For more information, refer to About building block rules.
enabled	Boolean	Determines whether the rule is enabled. Defaults to `true`.
false_positives	String[]	String array used to describe common reasons why the rule may issue false-positive alerts. Defaults to an empty array.
from	String	Time from which data is analyzed each time the rule executes, using a date math range. For example, `now-4200s` means the rule analyzes data from 70 minutes before its start time. Defaults to `now-6m` (analyzes data from 6 minutes before the start time).
interval	String	Frequency of rule execution, using a date math range. For example, `"1h"` means the rule runs every hour. Defaults to `5m` (5 minutes).
license	String	The rule’s license.
max_signals	Integer	Maximum number of alerts the rule can create during a single execution. Defaults to `100`.
meta	Object	Placeholder for metadata about the rule. NOTE: This field is overwritten when you save changes to the rule’s settings.
note	String	Notes to help investigate alerts produced by the rule.
references	String[]	Array containing notes about or references to relevant information about the rule. Defaults to an empty array.
rule_id	String	Unique ID used to identify rules. For example, when a rule is converted from a third-party security solution. Automatically created when it is not provided.
tags	String[]	String array containing words and phrases to help categorize, filter, and search rules. Defaults to an empty array.
threat	threat[]	Object containing attack information about the type of threat the rule monitors, see ECS threat fields. Defaults to an empty array.
throttle	String	Determines how often actions are taken: `no_actions`: Never `rule`: Every time new alerts are detected `1h`: Every hour `1d`: Every day `7d`: Every week Required when `actions` are used to send notifications.
version	Integer	The rule’s version number. Defaults to `1`.

Optional fields for query, indicator match, event correlation, and new terms rules

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Name	Type	Description
exceptions_list	Object[]	Array of exception containers, which define exceptions that prevent the rule from generating alerts even when its other criteria are met. The object has these fields: `id` (string, required): ID of the exception container. `list_id` (string, required): List ID of the exception container. `namespace_type` (string required): Determines whether the exceptions are valid in only the rule’s Kibana space (`single`) or in all Kibana spaces (`agnostic`). `type` (string, required): The exception type, which must be either a detection rule exception (`detection`) or an endpoint exception (`endpoint`).

Optional fields for indicator match rules

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Name	Type	Description
threat_filters	Object[]	Query and filter context array used to filter documents from the Elasticsearch index containing the threat values.
threat_indicator_path	String	Much like an ingest processor, users can use this field to define where their threat indicator can be found on their indicator documents. Defaults to `threatintel.indicator`.

Optional fields for query, indicator match, threshold, and new terms rules

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Name	Type	Description
language	String	Determines the query language, which must be `kuery` or `lucene`. Defaults to `kuery`.

Optional fields for event correlation, query, threshold, and new terms rules

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Name	Type	Description
filters	Object[]	The query and filter context array used to define the conditions for when alerts are created from events. Defaults to an empty array.
index	String[]	Indices on which the rule functions. Defaults to the Security Solution indices defined on the Kibana Advanced Settings page (Kibana → Stack Management → Advanced Settings → `securitySolution:defaultIndex`). Event correlation rules have a limitation that prevents them from querying multiple indices from different clusters (local and remote). To enable this, a user with the `superuser` role must modify the EQL rules that are configured to use cross-cluster search. This updates the rule’s API key to use `superuser` privileges and allows the rule to use cross-cluster search.
risk_score_mapping	Object[]	Overrides generated alerts' `risk_score` with a value from the source event: `field` (string, required): Source event field used to override the default `risk_score`. This field must be an integer. `operator` (string, required): Must be `equals`. `value`(string, required): Must be an empty string (`""`).
rule_name_override	String	Sets which field in the source event is used to populate the alert’s `signal.rule.name` value (in the UI, this value is displayed on the Rules page in the Rule column). When unspecified, the rule’s `name` value is used. The source field must be a string data type.
severity_mapping	Object[]	Overrides generated alerts' `severity` with values from the source event: `field` (string, required): Source event field used to override the default `severity`. `operator` (string, required): Must be `equals`. `severity` (string, required): Mapped severity value, must be `low`, `medium`, `high`, or `critical`. `value`(string, required): Field value used to determine the `severity`.
timestamp_override	String	Sets the time field used to query indices. When unspecified, rules query the `@timestamp` field. The source field must be an Elasticsearch date data type.

Optional fields for event correlation rules

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Name	Type	Description
event_category_field	String	Contains the event classification, such as `process`, `file`, or `network`. This field is typically mapped as a field type in the keyword family. Defaults to the `event.category` ECS field.
tiebreaker_field	String	Sets a secondary field for sorting events (in ascending, lexicographic order) if they have the same timestamp.
timestamp_field	String	Contains the event timestamp used for sorting a sequence of events. This is different from `timestamp_override`, which is used for querying events within a range. Defaults to the `@timestamp` ECS field.

`actions` schema

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All fields are required:

Name	Type	Description
action_type_id	String	The connector type used for sending notifications, can be: `.slack` `.email` `.pagerduty` `.webhook`
group	String	Optionally groups actions by use cases. Use `default` for alert notifications.
id	String	The connector ID.
params	Object	Object containing the allowed connector fields, which varies according to the connector type: For Slack: `message` (string, required): The notification message. For email: `to`, `cc`, `bcc` (string): Email addresses to which the notifications are sent. At least one field must have a value. `subject` (string, optional): Email subject line. `message` (string, required): Email body text. For Webhook: `body` (string, required): JSON payload. For PagerDuty: `severity` (string, required): Severity of on the alert notification, can be: `Critical`, `Error`, `Warning` or `Info`. `eventAction` (string, required): Event action type, which can be `trigger`, `resolve`, or `acknowledge`. `dedupKey` (string, optional): Groups alert notifications with the same PagerDuty alert. `timestamp` (DateTime, optional): ISO-8601 format timestamp. `component` (string, optional): Source machine component responsible for the event, for example `security-solution`. `group` (string, optional): Enables logical grouping of service components. `source` (string, optional): The affected system. Defaults to the Kibana saved object ID of the action. `summary` (string, options): Summary of the event. Defaults to `No summary provided`. Maximum length is 1024 characters. `class` (string, optional): Value indicating the class/type of the event.

All text fields (such as message fields) can contain placeholders for rule and alert details:

{{state.signals_count}}: Number of alerts detected
{{context.alerts}}: Array of detected alerts
{{{context.results_link}}}: URL to the alerts in Kibana
{{context.rule.anomaly_threshold}}: Anomaly threshold score above which alerts are generated (machine learning rules only)
{{context.rule.description}}: Rule description
{{context.rule.false_positives}}: Rule false positives
{{context.rule.filters}}: Rule filters (query rules only)
{{context.rule.id}}: Unique rule ID returned after creating the rule
{{context.rule.index}}: Indices rule runs on (query rules only)
{{context.rule.language}}: Rule query language (query rules only)
{{context.rule.machine_learning_job_id}}: ID of associated machine learning job (machine learning rules only)
{{context.rule.max_signals}}: Maximum allowed number of alerts per rule execution
{{context.rule.name}}: Rule name
{{context.rule.query}}: Rule query (query rules only)
{{context.rule.references}}: Rule references
{{context.rule.risk_score}}: Rule risk score
{{context.rule.rule_id}}: Generated or user-defined rule ID that can be used as an identifier across systems
{{context.rule.saved_id}}: Saved search ID
{{context.rule.severity}}: Rule severity
{{context.rule.threat}}: Rule threat framework
{{context.rule.threshold}}: Rule threshold values (threshold rules only)
{{context.rule.timeline_id}}: Associated timeline ID
{{context.rule.timeline_title}}: Associated timeline name
{{context.rule.type}}: Rule type
{{context.rule.version}}: Rule version

`threat` schema

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All fields are required:

Name	Type	Description
framework	String	Relevant attack framework.
tactic	Object	Object containing information on the attack type: `id` - string, required `name` - string, required `reference` - string, required
technique	Array	Array containing information on the attack techniques (optional): `id` - string, required `name` - string, required `reference` - string, required `subtechnique` - Array, optional
subtechnique	Array	Array containing more specific information on the attack technique: `id` - string, required `name` - string, required `reference` - string, required

Only threats described using the MITRE ATT&CK^TM framework are displayed in the UI (Manage → Rules → Rule name).

Example requests

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Example 1

Query rule that searches for processes started by MS Office:

POST api/detection_engine/rules
{
  "rule_id": "process_started_by_ms_office_program",
  "risk_score": 50,
  "description": "Process started by MS Office program - possible payload",
  "interval": "1h", 
  "name": "MS Office child process",
  "severity": "low",
  "tags": [
   "child process",
   "ms office"
   ],
  "type": "query",
  "from": "now-70m", 
  "query": "process.parent.name:EXCEL.EXE or process.parent.name:MSPUB.EXE or process.parent.name:OUTLOOK.EXE or process.parent.name:POWERPNT.EXE or process.parent.name:VISIO.EXE or process.parent.name:WINWORD.EXE",
  "language": "kuery",
  "filters": [
     {
      "query": {
         "match": {
            "event.action": {
               "query": "Process Create (rule: ProcessCreate)",
               "type": "phrase"
            }
         }
      }
     }
  ],
  "enabled": false
}

	The rule runs every hour.
	When the rule runs it analyzes data from 70 minutes before its start time.

If the rule starts to run at 15:00, it analyzes data from 13:50 until 15:00. When it runs next, at 16:00, it will analyze data from 14:50 until 16:00.

Example 2

Threshold rule that detects multiple failed login attempts to a Windows host from the same external source IP address, and maps the severity value to custom source event fields:

POST api/detection_engine/rules
{
  "description": "Detects when there are 20 or more failed login attempts from the same IP address with a 2 minute time frame.",
  "enabled": true,
  "exceptions_list": [ 
    {
      "id": "int-ips",
      "namespace_type": "single",
      "type": "detection"
    }
  ],
  "from": "now-180s",
  "index": [
    "winlogbeat-*"
  ],
  "interval": "2m",
  "name": "Windows server prml-19",
  "query": "host.name:prml-19 and event.category:authentication and event.outcome:failure",
  "risk_score": 30,
  "rule_id": "liv-win-ser-logins",
  "severity": "low",
  "severity_mapping": [ 
    {
      "field": "source.geo.city_name",
      "operator": "equals",
      "severity": "low",
      "value": "Manchester"
    },
    {
      "field": "source.geo.city_name",
      "operator": "equals",
      "severity": "medium",
      "value": "London"
    },
    {
      "field": "source.geo.city_name",
      "operator": "equals",
      "severity": "high",
      "value": "Birmingham"
    },
    {
      "field": "source.geo.city_name",
      "operator": "equals",
      "severity": "critical",
      "value": "Wallingford"
    }
  ],
  "tags": [
    "Brute force"
  ],
  "threshold": { 
    "field": "source.ip",
    "value": 20
  },
  "type": "threshold"
}

	Exception list container used to exclude internal IP addresses.
	Alert severity levels are mapped according to the defined field values.
	Alerts are generated when the same source IP address is discovered in at least 20 results.

Example 3

Machine learning rule that creates alerts, and sends Slack notifications, when the linux_anomalous_network_activity_ecs machine learning job discovers anomalies with a threshold of 70 or above:

POST api/detection_engine/rules
{
  "anomaly_threshold": 70,
  "rule_id": "ml_linux_network_high_threshold",
  "risk_score": 70,
  "machine_learning_job_id": "linux_anomalous_network_activity_ecs",
  "description": "Generates alerts when the job discovers anomalies over 70",
  "interval": "5m",
  "name": "Anomalous Linux network activity",
  "note": "Shut down the internet.",
  "severity": "high",
  "tags": [
   "machine learning",
   "Linux"
   ],
  "type": "machine_learning",
  "from": "now-6m",
  "enabled": true,
  "throttle": "rule",
  "actions": [
    {
      "action_type_id": ".slack",
      "group": "default",
      "id": "5ad22cd5-5e6e-4c6c-a81a-54b626a4cec5",
      "params": {
        "message": "Urgent: {{context.rule.description}}"
      }
    }
  ]
}

Example 4

Event correlation rule that creates alerts when the Windows rundll32.exe process makes unusual network connections:

POST api/detection_engine/rules
{
  "rule_id": "eql-outbound-rundll32-connections",
  "risk_score": 21,
  "description": "Unusual rundll32.exe network connection",
  "name": "rundll32.exe network connection",
  "severity": "low",
  "tags": [
   "EQL",
   "Windows",
   "rundll32.exe"
   ],
  "type": "eql",
  "language": "eql",
  "query": "sequence by process.entity_id with maxspan=2h [process where event.type in (\"start\", \"process_started\") and (process.name == \"rundll32.exe\" or process.pe.original_file_name == \"rundll32.exe\") and ((process.args == \"rundll32.exe\" and process.args_count == 1) or (process.args != \"rundll32.exe\" and process.args_count == 0))] [network where event.type == \"connection\" and (process.name == \"rundll32.exe\" or process.pe.original_file_name == \"rundll32.exe\")]"
}

Example 5

Indicator match rule that creates an alert when one of the following is true:

The event’s destination IP address and port number matches destination IP and port values in the threat_index index.
The event’s source IP address matches a host IP address value in the threat_index index.

POST api/detection_engine/rules
{
    "type": "threat_match",
    "index": [
        "packetbeat-*"
    ],
    "query": "destination.ip:* or host.ip:*",
    "threat_index": [
        "ip-threat-list" 
    ],
    "threat_query": "*:*", 
    "threat_mapping": [
        {
            "entries": [ 
                {
                    "field": "destination.ip",
                    "type": "mapping",
                    "value": "destination.ip"
                },
                {
                    "field": "destination.port",
                    "type": "mapping",
                    "value": "destination.port"
                }
            ]
        },
        {
            "entries": [ 
                {
                    "field": "source.ip",
                    "type": "mapping",
                    "value": "host.ip"
                }
            ]
        }
    ],
    "risk_score": 50,
    "severity": "medium",
    "name": "Bad IP threat match",
    "description": "Checks for bad IP addresses listed in the ip-threat-list index"
}

	The Elasticsearch index used for matching threat values.
	Query defining which threat index fields are used for matching values. In this example, all values from the `ip-threat-list` index are used.
	Multiple objects in a single `entries` element are evaluated using `and` logic. In this example, both the event’s `destination.ip` and `destination.port` values must match the corresponding field values in the `ip-threat-list`.
	Sibling `entries` are evaluated using `or` logic. An alert is generated when at least one `entries` object evaluates to `true`.

Example 6

New terms rule that creates alerts a new IP address is detected for a user:

POST api/detection_engine/rules
{
  "risk_score": 21,
  "description": "Detects a user associated with a new IP address",
  "name": "New User IP Detected",
  "severity": "medium",
  "type": "new_terms",
  "language": "kuery",
  "query": "*",
  "new_terms_fields": ["user.id"],
  "history_window_start": "now-30d",
  "index": ["auditbeat*"]
}

Response code

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200: Indicates a successful call.

Response payload

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A JSON object that includes a unique ID, the time the rule was created, and its version number. If the request payload did not include a rule_id field, a unique rule ID is also generated.

Example response for a query rule:

{
  "created_at": "2020-04-07T14:51:09.755Z",
  "updated_at": "2020-04-07T14:51:09.970Z",
  "created_by": "elastic",
  "description": "Process started by MS Office program - possible payload",
  "enabled": false,
  "false_positives": [],
  "from": "now-70m",
  "id": "6541b99a-dee9-4f6d-a86d-dbd1869d73b1",
  "immutable": false,
  "interval": "1h",
  "rule_id": "process_started_by_ms_office_program",
  "max_signals": 100,
  "risk_score": 50,
  "name": "MS Office child process",
  "references": [],
  "severity": "low",
  "updated_by": "elastic",
  "tags": [
    "child process",
    "ms office"
  ],
  "to": "now",
  "type": "query",
  "threat": [],
  "version": 1,
  "actions": [],
  "filters": [
    {
      "query": {
        "match": {
          "event.action": {
            "query": "Process Create (rule: ProcessCreate)",
            "type": "phrase"
          }
        }
      }
    }
  ],
  "throttle": "no_actions",
  "query": "process.parent.name:EXCEL.EXE or process.parent.name:MSPUB.EXE or process.parent.name:OUTLOOK.EXE or process.parent.name:POWERPNT.EXE or process.parent.name:VISIO.EXE or process.parent.name:WINWORD.EXE",
  "language": "kuery",
  "related_integrations": [],       
  "required_fields": [],            
  "setup": ""                       
}

[dev] This functionality is in development and may be changed or removed completely in a future release. These features are unsupported and not subject to the support SLA of official GA features. These fields are under development and their usage may change: related_integrations, required_fields, and setup.

Example response for a machine learning job rule:

{
  "created_at": "2020-04-07T14:45:15.679Z",
  "updated_at": "2020-04-07T14:45:15.892Z",
  "created_by": "elastic",
  "description": "Generates alerts when the job discovers anomalies over 70",
  "enabled": true,
  "false_positives": [],
  "from": "now-6m",
  "id": "83876f66-3a57-4a99-bf37-416494c80f3b",
  "immutable": false,
  "interval": "5m",
  "rule_id": "ml_linux_network_high_threshold",
  "max_signals": 100,
  "risk_score": 70,
  "name": "Anomalous Linux network activity",
  "references": [],
  "severity": "high",
  "updated_by": "elastic",
  "tags": [
    "machine learning",
    "Linux"
  ],
  "to": "now",
  "type": "machine_learning",
  "threat": [],
  "version": 1,
  "actions": [
    {
      "action_type_id": ".slack",
      "group": "default",
      "id": "5ad22cd5-5e6e-4c6c-a81a-54b626a4cec5",
      "params": {
        "message": "Urgent: {{context.rule.description}}"
      }
    }
  ],
  "throttle": "rule",
  "note": "Shut down the internet.",
  "status": "going to run",
  "status_date": "2020-04-07T14:45:21.685Z",
  "anomaly_threshold": 70,
  "machine_learning_job_id": "linux_anomalous_network_activity_ecs",
  "related_integrations": [],       
  "required_fields": [],            
  "setup": ""                       
}

Example response for a threshold rule:

{
  "author": [],
  "created_at": "2020-07-22T10:27:23.486Z",
  "updated_at": "2020-07-22T10:27:23.673Z",
  "created_by": "elastic",
  "description": "Detects when there are 20 or more failed login attempts from the same IP address with a 2 minute time frame.",
  "enabled": true,
  "false_positives": [],
  "from": "now-180s",
  "id": "15dbde26-b627-4d74-bb1f-a5e0ed9e4993",
  "immutable": false,
  "interval": "2m",
  "rule_id": "liv-win-ser-logins",
  "max_signals": 100,
  "risk_score": 30,
  "risk_score_mapping": [],
  "name": "Windows server prml-19",
  "references": [],
  "severity": "low",
  "severity_mapping": [
    {
      "field": "source.geo.city_name",
      "operator": "equals",
      "severity": "low",
      "value": "Manchester"
    },
    {
      "field": "source.geo.city_name",
      "operator": "equals",
      "severity": "medium",
      "value": "London"
    },
    {
      "field": "source.geo.city_name",
      "operator": "equals",
      "severity": "high",
      "value": "Birmingham"
    },
    {
      "field": "source.geo.city_name",
      "operator": "equals",
      "severity": "critical",
      "value": "Wallingford"
    }
  ],
  "updated_by": "elastic",
  "tags": [
    "Brute force"
  ],
  "to": "now",
  "type": "threshold",
  "threat": [],
  "version": 1,
  "exceptions_list": [
    {
      "id": "int-ips",
      "namespace_type": "single",
      "type": "detection"
    }
  ],
  "actions": [],
  "index": [
    "winlogbeat-*"
  ],
  "throttle": "no_actions",
  "query": "host.name:prml-19 and event.category:authentication and event.outcome:failure",
  "language": "kuery",
  "threshold": {
    "field": "source.ip",
    "value": 20
  },
  "related_integrations": [],       
  "required_fields": [],            
  "setup": ""                       
}

Example response for an EQL rule:

{
  "author": [],
  "created_at": "2020-10-05T09:06:16.392Z",
  "updated_at": "2020-10-05T09:06:16.403Z",
  "created_by": "elastic",
  "description": "Unusual rundll32.exe network connection",
  "enabled": true,
  "false_positives": [],
  "from": "now-6m",
  "id": "93808cae-b05b-4dc9-8479-73574b50f8b1",
  "immutable": false,
  "interval": "5m",
  "rule_id": "eql-outbound-rundll32-connections",
  "max_signals": 100,
  "risk_score": 21,
  "risk_score_mapping": [],
  "name": "rundll32.exe network connection",
  "references": [],
  "severity": "low",
  "severity_mapping": [],
  "updated_by": "elastic",
  "tags": [
    "EQL",
    "Windows",
    "rundll32.exe"
  ],
  "to": "now",
  "type": "eql",
  "threat": [],
  "version": 1,
  "exceptions_list": [],
  "actions": [],
  "throttle": "no_actions",
  "query": "sequence by process.entity_id with maxspan=2h [process where event.type in (\"start\", \"process_started\") and (process.name == \"rundll32.exe\" or process.pe.original_file_name == \"rundll32.exe\") and ((process.args == \"rundll32.exe\" and process.args_count == 1) or (process.args != \"rundll32.exe\" and process.args_count == 0))] [network where event.type == \"connection\" and (process.name == \"rundll32.exe\" or process.pe.original_file_name == \"rundll32.exe\")]",
  "language": "eql",
  "related_integrations": [],       
  "required_fields": [],            
  "setup": ""                       
}

Example response for an indicator match rule:

{
  "author": [],
  "created_at": "2020-10-06T07:07:58.227Z",
  "updated_at": "2020-10-06T07:07:58.237Z",
  "created_by": "elastic",
  "description": "Checks for bad IP addresses listed in the ip-threat-list index",
  "enabled": true,
  "false_positives": [],
  "from": "now-6m",
  "id": "d5daa13f-81fb-4b13-be2f-31011e1d9ae1",
  "immutable": false,
  "interval": "5m",
  "rule_id": "608501e4-c768-4f64-9326-cec55b5d439b",
  "max_signals": 100,
  "risk_score": 50,
  "risk_score_mapping": [],
  "name": "Bad IP threat match",
  "references": [],
  "severity": "medium",
  "severity_mapping": [],
  "updated_by": "elastic",
  "tags": [],
  "to": "now",
  "type": "threat_match",
  "threat": [],
  "version": 1,
  "exceptions_list": [],
  "actions": [],
  "index": [
    "packetbeat-*"
  ],
  "throttle": "no_actions",
  "query": "destination.ip:* or host.ip:*",
  "language": "kuery",
  "threat_query": "*:*",
  "threat_index": [
    "ip-threat-list"
  ],
  "threat_mapping": [
    {
      "entries": [
        {
          "field": "destination.ip",
          "type": "mapping",
          "value": "destination.ip"
        },
        {
          "field": "destination.port",
          "type": "mapping",
          "value": "destination.port"
        }
      ]
    },
    {
      "entries": [
        {
          "field": "source.ip",
          "type": "mapping",
          "value": "host.ip"
        }
      ]
    }
  ],
  "related_integrations": [],       
  "required_fields": [],            
  "setup": ""                       
}

Example response for a new terms rule:

{
  "author": [],
  "created_at": "2020-10-06T07:07:58.227Z",
  "updated_at": "2020-10-06T07:07:58.237Z",
  "created_by": "elastic",
  "description": "Detects a user associated with a new IP address",
  "enabled": true,
  "false_positives": [],
  "from": "now-6m",
  "id": "eb7225c0-566b-11ee-8b4f-bbf3afdeb9f4",
  "immutable": false,
  "interval": "5m",
  "rule_id": "c6f5d0bc-7be9-47d4-b2f3-073d22641e30",
  "max_signals": 100,
  "risk_score": 21,
  "risk_score_mapping": [],
  "name": "New User IP Detected",
  "references": [],
  "severity": "medium",
  "severity_mapping": [],
  "updated_by": "elastic",
  "tags": [],
  "to": "now",
  "type": "new_terms",
  "threat": [],
  "version": 1,
  "exceptions_list": [],
  "index": [
    "auditbeat*"
  ],
  "query": "*",
  "language": "kuery",
  "new_terms_fields": ["user.id"],
  "history_window_start": "now-30d",
  "related_integrations": [],       
  "required_fields": [],            
  "setup": ""                       
}

« Detections API Get rule »

Create rule

Create rule

Request URL

Request body

Required fields for all rule types

Required field for query, indicator match, threshold, and new terms rules

Required field for threshold rules

Required field for saved query rules

Required field for event correlation rules

Required fields for machine learning rules

Required fields for indicator match rules

Required fields for new terms rules

Optional fields for all rule types

Optional fields for query, indicator match, event correlation, and new terms rules

Optional fields for indicator match rules

Optional fields for query, indicator match, threshold, and new terms rules

Optional fields for event correlation, query, threshold, and new terms rules

Optional fields for event correlation rules

actions schema

threat schema

Example requests

Response code

Response payload

`actions` schema

`threat` schema