Response rows stateful anomaly on database

let volumeThresholdZ = 3.0;                     // Minimal threshold for the Zscore to trigger anomaly (number of standard deviations above mean). If set higher, only very significant alerts will fire.
let volumeThresholdQ = volumeThresholdZ;        // Minimal threshold for the Qscore to trigger anomaly (number of Inter-Percentile Ranges above high percentile). If set higher, only very significant alerts will fire.
let volumeThresholdHardcoded = 500;             // Minimal value for the volume metric to trigger anomaly.
let detectionWindow = 1h;                       // The size of the recent detection window for detecting anomalies.  
let trainingWindow = detectionWindow + 14d;     // The size of the training window before the detection window for learning the normal state.
let monitoredColumn = 'ResponseRows';           // The name of the column for volumetric anomalies.
let processedData = materialize (
    AzureDiagnostics
    | where TimeGenerated >= ago(trainingWindow)
    | where Category == 'SQLSecurityAuditEvents' and action_id_s has_any ("RCM", "BCM") // Keep only SQL affected rows
    | project TimeGenerated, PrincipalName = server_principal_name_s, ClientIp = client_ip_s, HostName = host_name_s, ResourceId,
              ApplicationName = application_name_s, ActionName = action_name_s, Database = strcat(LogicalServerName_s, '/', database_name_s),
              IsSuccess = succeeded_s, AffectedRows = affected_rows_d,
              ResponseRows = response_rows_d, Statement = statement_s
    | extend QuantityColumn = column_ifexists(monitoredColumn, 0)
    | extend WindowType = case( TimeGenerated >= ago(detectionWindow), 'detection',
                                           (ago(trainingWindow) <= TimeGenerated and TimeGenerated < ago(detectionWindow)), 'training', 'other')
    | where WindowType in ('detection', 'training'));
let trainingSet =
    processedData
    | where WindowType == 'training'
    | summarize AvgVal = round(avg(QuantityColumn), 2), StdVal = round(stdev(QuantityColumn), 2), N = count(),
                P99Val = round(percentile(QuantityColumn, 99), 2), P50Val = round(percentile(QuantityColumn, 50), 2)
      by Database;
processedData
| where WindowType == 'detection'
| join kind = inner (trainingSet) on Database
| extend ZScoreVal = iff(N >= 20, round(todouble(QuantityColumn - AvgVal) / todouble(StdVal + 1), 2), 0.00),
         QScoreVal = iff(N >= 20, round(todouble(QuantityColumn - P99Val) / todouble(P99Val - P50Val + 1), 2), 0.00)
| extend IsVolumeAnomalyOnVal = iff((ZScoreVal > volumeThresholdZ and QScoreVal > volumeThresholdQ and QuantityColumn > volumeThresholdHardcoded), true, false), AnomalyScore = round((ZScoreVal + QScoreVal)/2, 0)
| project TimeGenerated, Database, PrincipalName, ClientIp, HostName, ApplicationName, ActionName, Statement,
          IsSuccess, ResponseRows, AffectedRows, IsVolumeAnomalyOnVal, AnomalyScore, ResourceId
| where IsVolumeAnomalyOnVal == 'true'
| sort by AnomalyScore desc, TimeGenerated desc
| extend Name = tostring(split(PrincipalName,'@',0)[0]), UPNSuffix = tostring(split(PrincipalName,'@',1)[0])

requiredDataConnectors:
- connectorId: AzureSql
  dataTypes:
  - AzureDiagnostics
triggerOperator: gt
OriginalUri: https://github.com/Azure/Azure-Sentinel/blob/master/Solutions/Azure SQL Database solution for sentinel/Analytic Rules/Detection-VolumeResponseRowsStatefulAnomalyOnDatabase.yaml
version: 1.1.1
status: Available
queryPeriod: 14d
severity: Medium
relevantTechniques:
- T1537
- T1567
tactics:
- Exfiltration
kind: Scheduled
queryFrequency: 1h
description: |
  'Goal: To detect anomalous data exfiltration. This query detects SQL queries that accessed a large number of rows, which is significantly higher than normal for this database.
   The calculation is made inside recent time window (defined by 'detectionWindow' parameter), and the anomaly is calculated based on previous training window 
   (defined by 'trainingWindow' parameter). The user can set the minimal threshold for anomaly by changing the threshold parameters volThresholdZ and volThresholdQ (higher thresholds will detect only more severe anomalies).'  
query: |
  let volumeThresholdZ = 3.0;                     // Minimal threshold for the Zscore to trigger anomaly (number of standard deviations above mean). If set higher, only very significant alerts will fire.
  let volumeThresholdQ = volumeThresholdZ;        // Minimal threshold for the Qscore to trigger anomaly (number of Inter-Percentile Ranges above high percentile). If set higher, only very significant alerts will fire.
  let volumeThresholdHardcoded = 500;             // Minimal value for the volume metric to trigger anomaly.
  let detectionWindow = 1h;                       // The size of the recent detection window for detecting anomalies.  
  let trainingWindow = detectionWindow + 14d;     // The size of the training window before the detection window for learning the normal state.
  let monitoredColumn = 'ResponseRows';           // The name of the column for volumetric anomalies.
  let processedData = materialize (
      AzureDiagnostics
      | where TimeGenerated >= ago(trainingWindow)
      | where Category == 'SQLSecurityAuditEvents' and action_id_s has_any ("RCM", "BCM") // Keep only SQL affected rows
      | project TimeGenerated, PrincipalName = server_principal_name_s, ClientIp = client_ip_s, HostName = host_name_s, ResourceId,
                ApplicationName = application_name_s, ActionName = action_name_s, Database = strcat(LogicalServerName_s, '/', database_name_s),
                IsSuccess = succeeded_s, AffectedRows = affected_rows_d,
                ResponseRows = response_rows_d, Statement = statement_s
      | extend QuantityColumn = column_ifexists(monitoredColumn, 0)
      | extend WindowType = case( TimeGenerated >= ago(detectionWindow), 'detection',
                                             (ago(trainingWindow) <= TimeGenerated and TimeGenerated < ago(detectionWindow)), 'training', 'other')
      | where WindowType in ('detection', 'training'));
  let trainingSet =
      processedData
      | where WindowType == 'training'
      | summarize AvgVal = round(avg(QuantityColumn), 2), StdVal = round(stdev(QuantityColumn), 2), N = count(),
                  P99Val = round(percentile(QuantityColumn, 99), 2), P50Val = round(percentile(QuantityColumn, 50), 2)
        by Database;
  processedData
  | where WindowType == 'detection'
  | join kind = inner (trainingSet) on Database
  | extend ZScoreVal = iff(N >= 20, round(todouble(QuantityColumn - AvgVal) / todouble(StdVal + 1), 2), 0.00),
           QScoreVal = iff(N >= 20, round(todouble(QuantityColumn - P99Val) / todouble(P99Val - P50Val + 1), 2), 0.00)
  | extend IsVolumeAnomalyOnVal = iff((ZScoreVal > volumeThresholdZ and QScoreVal > volumeThresholdQ and QuantityColumn > volumeThresholdHardcoded), true, false), AnomalyScore = round((ZScoreVal + QScoreVal)/2, 0)
  | project TimeGenerated, Database, PrincipalName, ClientIp, HostName, ApplicationName, ActionName, Statement,
            IsSuccess, ResponseRows, AffectedRows, IsVolumeAnomalyOnVal, AnomalyScore, ResourceId
  | where IsVolumeAnomalyOnVal == 'true'
  | sort by AnomalyScore desc, TimeGenerated desc
  | extend Name = tostring(split(PrincipalName,'@',0)[0]), UPNSuffix = tostring(split(PrincipalName,'@',1)[0])  
id: 9851c360-5fd5-4bae-a117-b66d8476bf5e
tags:
- SQL
triggerThreshold: 0
entityMappings:
- fieldMappings:
  - identifier: Name
    columnName: Name
  - identifier: UPNSuffix
    columnName: UPNSuffix
  entityType: Account
- fieldMappings:
  - identifier: Address
    columnName: ClientIp
  entityType: IP
- fieldMappings:
  - identifier: HostName
    columnName: HostName
  entityType: Host
- fieldMappings:
  - identifier: Name
    columnName: ApplicationName
  entityType: CloudApplication
- fieldMappings:
  - identifier: ResourceId
    columnName: ResourceId
  entityType: AzureResource
name: Response rows stateful anomaly on database

{
  "$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentTemplate.json#",
  "contentVersion": "1.0.0.0",
  "parameters": {
    "workspace": {
      "type": "String"
    }
  },
  "resources": [
    {
      "apiVersion": "2023-02-01-preview",
      "id": "[concat(resourceId('Microsoft.OperationalInsights/workspaces/providers', parameters('workspace'), 'Microsoft.SecurityInsights'),'/alertRules/9851c360-5fd5-4bae-a117-b66d8476bf5e')]",
      "kind": "Scheduled",
      "name": "[concat(parameters('workspace'),'/Microsoft.SecurityInsights/9851c360-5fd5-4bae-a117-b66d8476bf5e')]",
      "properties": {
        "alertRuleTemplateName": "9851c360-5fd5-4bae-a117-b66d8476bf5e",
        "customDetails": null,
        "description": "'Goal: To detect anomalous data exfiltration. This query detects SQL queries that accessed a large number of rows, which is significantly higher than normal for this database.\n The calculation is made inside recent time window (defined by 'detectionWindow' parameter), and the anomaly is calculated based on previous training window \n (defined by 'trainingWindow' parameter). The user can set the minimal threshold for anomaly by changing the threshold parameters volThresholdZ and volThresholdQ (higher thresholds will detect only more severe anomalies).'\n",
        "displayName": "Response rows stateful anomaly on database",
        "enabled": true,
        "entityMappings": [
          {
            "entityType": "Account",
            "fieldMappings": [
              {
                "columnName": "Name",
                "identifier": "Name"
              },
              {
                "columnName": "UPNSuffix",
                "identifier": "UPNSuffix"
              }
            ]
          },
          {
            "entityType": "IP",
            "fieldMappings": [
              {
                "columnName": "ClientIp",
                "identifier": "Address"
              }
            ]
          },
          {
            "entityType": "Host",
            "fieldMappings": [
              {
                "columnName": "HostName",
                "identifier": "HostName"
              }
            ]
          },
          {
            "entityType": "CloudApplication",
            "fieldMappings": [
              {
                "columnName": "ApplicationName",
                "identifier": "Name"
              }
            ]
          },
          {
            "entityType": "AzureResource",
            "fieldMappings": [
              {
                "columnName": "ResourceId",
                "identifier": "ResourceId"
              }
            ]
          }
        ],
        "OriginalUri": "https://github.com/Azure/Azure-Sentinel/blob/master/Solutions/Azure SQL Database solution for sentinel/Analytic Rules/Detection-VolumeResponseRowsStatefulAnomalyOnDatabase.yaml",
        "query": "let volumeThresholdZ = 3.0;                     // Minimal threshold for the Zscore to trigger anomaly (number of standard deviations above mean). If set higher, only very significant alerts will fire.\nlet volumeThresholdQ = volumeThresholdZ;        // Minimal threshold for the Qscore to trigger anomaly (number of Inter-Percentile Ranges above high percentile). If set higher, only very significant alerts will fire.\nlet volumeThresholdHardcoded = 500;             // Minimal value for the volume metric to trigger anomaly.\nlet detectionWindow = 1h;                       // The size of the recent detection window for detecting anomalies.  \nlet trainingWindow = detectionWindow + 14d;     // The size of the training window before the detection window for learning the normal state.\nlet monitoredColumn = 'ResponseRows';           // The name of the column for volumetric anomalies.\nlet processedData = materialize (\n    AzureDiagnostics\n    | where TimeGenerated >= ago(trainingWindow)\n    | where Category == 'SQLSecurityAuditEvents' and action_id_s has_any (\"RCM\", \"BCM\") // Keep only SQL affected rows\n    | project TimeGenerated, PrincipalName = server_principal_name_s, ClientIp = client_ip_s, HostName = host_name_s, ResourceId,\n              ApplicationName = application_name_s, ActionName = action_name_s, Database = strcat(LogicalServerName_s, '/', database_name_s),\n              IsSuccess = succeeded_s, AffectedRows = affected_rows_d,\n              ResponseRows = response_rows_d, Statement = statement_s\n    | extend QuantityColumn = column_ifexists(monitoredColumn, 0)\n    | extend WindowType = case( TimeGenerated >= ago(detectionWindow), 'detection',\n                                           (ago(trainingWindow) <= TimeGenerated and TimeGenerated < ago(detectionWindow)), 'training', 'other')\n    | where WindowType in ('detection', 'training'));\nlet trainingSet =\n    processedData\n    | where WindowType == 'training'\n    | summarize AvgVal = round(avg(QuantityColumn), 2), StdVal = round(stdev(QuantityColumn), 2), N = count(),\n                P99Val = round(percentile(QuantityColumn, 99), 2), P50Val = round(percentile(QuantityColumn, 50), 2)\n      by Database;\nprocessedData\n| where WindowType == 'detection'\n| join kind = inner (trainingSet) on Database\n| extend ZScoreVal = iff(N >= 20, round(todouble(QuantityColumn - AvgVal) / todouble(StdVal + 1), 2), 0.00),\n         QScoreVal = iff(N >= 20, round(todouble(QuantityColumn - P99Val) / todouble(P99Val - P50Val + 1), 2), 0.00)\n| extend IsVolumeAnomalyOnVal = iff((ZScoreVal > volumeThresholdZ and QScoreVal > volumeThresholdQ and QuantityColumn > volumeThresholdHardcoded), true, false), AnomalyScore = round((ZScoreVal + QScoreVal)/2, 0)\n| project TimeGenerated, Database, PrincipalName, ClientIp, HostName, ApplicationName, ActionName, Statement,\n          IsSuccess, ResponseRows, AffectedRows, IsVolumeAnomalyOnVal, AnomalyScore, ResourceId\n| where IsVolumeAnomalyOnVal == 'true'\n| sort by AnomalyScore desc, TimeGenerated desc\n| extend Name = tostring(split(PrincipalName,'@',0)[0]), UPNSuffix = tostring(split(PrincipalName,'@',1)[0])\n",
        "queryFrequency": "PT1H",
        "queryPeriod": "P14D",
        "severity": "Medium",
        "status": "Available",
        "suppressionDuration": "PT1H",
        "suppressionEnabled": false,
        "tactics": [
          "Exfiltration"
        ],
        "tags": [
          "SQL"
        ],
        "techniques": [
          "T1537",
          "T1567"
        ],
        "templateVersion": "1.1.1",
        "triggerOperator": "GreaterThan",
        "triggerThreshold": 0
      },
      "type": "Microsoft.OperationalInsights/workspaces/providers/alertRules"
    }
  ]
}