Response rows stateful anomaly on database

id: 9851c360-5fd5-4bae-a117-b66d8476bf5e
name: Response rows stateful anomaly on database
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).'
severity: Medium
requiredDataConnectors:
  - connectorId: AzureSql
    dataTypes:
      - AzureDiagnostics
queryFrequency: 1h
queryPeriod: 14d
triggerOperator: gt
triggerThreshold: 0
status: Available
tactics:
  - Collection
relevantTechniques:
  - T1213.006 
tags:
  - SQL
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)
    | where IsVolumeAnomalyOnVal == 'true'
    | project TimeGenerated, Database, PrincipalName, ClientIp, HostName, ApplicationName, ActionName, Statement,
              IsSuccess, ResponseRows, AffectedRows, IsVolumeAnomalyOnVal, AnomalyScore, ResourceId
    | extend Name = tostring(split(PrincipalName,'@',0)[0]), UPNSuffix = tostring(split(PrincipalName,'@',1)[0])
entityMappings:
  - entityType: Account
    fieldMappings:
      - identifier: Name
        columnName: Name
      - identifier: UPNSuffix
        columnName: UPNSuffix
  - entityType: IP
    fieldMappings:
      - identifier: Address
        columnName: ClientIp
  - entityType: Host
    fieldMappings:
      - identifier: HostName
        columnName: HostName
  - entityType: CloudApplication
    fieldMappings:
      - identifier: Name
        columnName: ApplicationName
  - entityType: AzureResource
    fieldMappings:
      - identifier: ResourceId
        columnName: ResourceId
alertDetailsOverride:
  alertDisplayNameFormat: 'Response rows anomaly on database {{Database}}'
  alertDescriptionFormat: |
    'An anomaly was detected on database {{Database}} with {{ResponseRows}} response rows in a statement, which is significantly higher than normal for this database. Investigate the database activity for potential malicious data changes or deletions.'
version: 1.1.3
kind: Scheduled