Unusual Anomaly

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Id	d0255b5f-2a3c-4112-8744-e6757af3283a
Rulename	Unusual Anomaly
Description	Anomaly Rules generate events in the Anomalies table. This scheduled rule tries to detect Anomalies that are not usual, they could be a type of Anomaly that has recently been activated, or an infrequent type. The detected Anomaly should be reviewed, if it is relevant enough, eventually a separate scheduled Analytics Rule could be created specifically for that Anomaly Type, so an alert and/or incident is generated everytime that type of Anomaly happens.
Severity	Medium
Kind	Scheduled
Query frequency	1h
Query period	4d
Trigger threshold	0
Trigger operator	gt
Source Uri	https://github.com/Azure/Azure-Sentinel/blob/master/Detections/Anomalies/UnusualAnomaly.yaml
Version	1.0.3
Arm template	d0255b5f-2a3c-4112-8744-e6757af3283a.json

KQL

// You can leave out Anomalies that are already monitored through other Analytics Rules
//let _MonitoredRules = dynamic(["TestAlertName"]);
let query_frequency = 1h;
let query_lookback = 3d;
Anomalies
| where TimeGenerated > ago(query_frequency)
//| where not(RuleName has_any (_MonitoredRules))
| join kind = leftanti (
    Anomalies
    | where TimeGenerated between (ago(query_frequency + query_lookback)..ago(query_frequency))
    | distinct RuleName
) on RuleName
| extend Name = tostring(split(UserPrincipalName, "@")[0]), UPNSuffix = tostring(split(UserPrincipalName, "@")[1])

YAML

techniques: []
alertDetailsOverride:
  alertDisplayNameFormat: Unusual Anomaly - {{RuleName}}
  alertTacticsColumnName: Tactics
  alertDynamicProperties:
  - value: Techniques
    alertProperty: Techniques
query: |
  // You can leave out Anomalies that are already monitored through other Analytics Rules
  //let _MonitoredRules = dynamic(["TestAlertName"]);
  let query_frequency = 1h;
  let query_lookback = 3d;
  Anomalies
  | where TimeGenerated > ago(query_frequency)
  //| where not(RuleName has_any (_MonitoredRules))
  | join kind = leftanti (
      Anomalies
      | where TimeGenerated between (ago(query_frequency + query_lookback)..ago(query_frequency))
      | distinct RuleName
  ) on RuleName
  | extend Name = tostring(split(UserPrincipalName, "@")[0]), UPNSuffix = tostring(split(UserPrincipalName, "@")[1])  
requiredDataConnectors: []
OriginalUri: https://github.com/Azure/Azure-Sentinel/blob/master/Detections/Anomalies/UnusualAnomaly.yaml
kind: Scheduled
name: Unusual Anomaly
tactics: []
severity: Medium
entityMappings:
- fieldMappings:
  - identifier: FullName
    columnName: UserPrincipalName
  - identifier: Name
    columnName: Name
  - identifier: UPNSuffix
    columnName: UPNSuffix
  entityType: Account
queryFrequency: 1h
description: |
    'Anomaly Rules generate events in the Anomalies table. This scheduled rule tries to detect Anomalies that are not usual, they could be a type of Anomaly that has recently been activated, or an infrequent type. The detected Anomaly should be reviewed, if it is relevant enough, eventually a separate scheduled Analytics Rule could be created specifically for that Anomaly Type, so an alert and/or incident is generated everytime that type of Anomaly happens.'
eventGroupingSettings:
  aggregationKind: AlertPerResult
triggerThreshold: 0
triggerOperator: gt
version: 1.0.3
queryPeriod: 4d
id: d0255b5f-2a3c-4112-8744-e6757af3283a

ARM