Firewall errors stateful anomaly on database

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Id	20f87813-3de0-4a9f-a8c0-6aaa3187be08
Rulename	Firewall errors stateful anomaly on database
Description	This query batches of distinct SQL queries that failed with error codes that might indicate malicious attempts to gain illegitimate access to the data. When attacker attempts to scan or gain access to server protected by firewall, he will be blocked by firewall and fail with error code 40615. Thus, if we see a large number of logins with such error codes, this could indicate attempts to gain access.
Severity	Medium
Tactics	InitialAccess
Techniques	T1190
Required data connectors	AzureSql
Kind	Scheduled
Query frequency	1h
Query period	14d
Trigger threshold	0
Trigger operator	gt
Source Uri	https://github.com/Azure/Azure-Sentinel/blob/master/Solutions/Azure SQL Database solution for sentinel/Analytic Rules/Detection-ErrorsFirewallStatefulAnomalyOnDatabase.yaml
Version	1.1.0
Arm template	20f87813-3de0-4a9f-a8c0-6aaa3187be08.json

KQL

let monitoredStatementsThreshold = 1;           // Minimal number of monitored statements in the slice to trigger an anomaly.
let trainingSlicesThreshold = 5;                // The maximal amount of slices with monitored statements in the training window before anomaly detection is throttled.
let timeSliceSize = 1h;                         // The size of the single timeSlice for individual aggregation.
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 monitoredErrors = pack_array(40615);        // List of sql error codes relevant for this detection.
let processedData = materialize (
    AzureDiagnostics
    | where TimeGenerated >= ago(detectionWindow)
    | 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,
              Error = case( additional_information_s has 'error_code', toint(extract("<error_code>([0-9.]+)", 1, additional_information_s))
                    , additional_information_s has 'failure_reason', toint(extract("<failure_reason>Err ([0-9.]+)", 1, additional_information_s))
                    , 0),
              State = case( additional_information_s has 'error_state', toint(extract("<error_state>([0-9.]+)", 1, additional_information_s))
                    , additional_information_s has 'failure_reason', toint(extract("<failure_reason>Err ([0-9.]+), Level ([0-9.]+)", 2, additional_information_s))
                    , 0),
              AdditionalInfo = additional_information_s, timeSlice = floor(TimeGenerated, timeSliceSize)
    | summarize countEvents = count(), countStatements = dcount(Statement), countStatementsWithError = dcountif(Statement, Error in (monitoredErrors))
        , anyMonitoredStatement = anyif(Statement, Error in (monitoredErrors)), anyInfo = anyif(AdditionalInfo, Error in (monitoredErrors))
        by Database, ClientIp, ApplicationName, PrincipalName, timeSlice
    | extend WindowType = case( timeSlice >= ago(detectionWindow), 'detection',
                                           (ago(trainingWindow) <= timeSlice and timeSlice < ago(detectionWindow)), 'training', 'other')
    | where WindowType in ('detection', 'training'));
let trainingSet =
    processedData
    | where WindowType == 'training'
    | summarize countSlicesWithErrors = dcountif(timeSlice, countStatementsWithError >= monitoredStatementsThreshold)
        by Database;
processedData
| where WindowType == 'detection' 
| join kind = inner (trainingSet) on Database
| extend IsErrorAnomalyOnStatement = iff(((countStatementsWithError >= monitoredStatementsThreshold) and (countSlicesWithErrors <= trainingSlicesThreshold)), true, false)
    , anomalyScore = round(countStatementsWithError/monitoredStatementsThreshold, 0)
| where IsErrorAnomalyOnStatement == 'true'
| sort by anomalyScore desc, timeSlice desc

YAML

name: Firewall errors stateful anomaly on database
query: |
  let monitoredStatementsThreshold = 1;           // Minimal number of monitored statements in the slice to trigger an anomaly.
  let trainingSlicesThreshold = 5;                // The maximal amount of slices with monitored statements in the training window before anomaly detection is throttled.
  let timeSliceSize = 1h;                         // The size of the single timeSlice for individual aggregation.
  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 monitoredErrors = pack_array(40615);        // List of sql error codes relevant for this detection.
  let processedData = materialize (
      AzureDiagnostics
      | where TimeGenerated >= ago(detectionWindow)
      | 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,
                Error = case( additional_information_s has 'error_code', toint(extract("<error_code>([0-9.]+)", 1, additional_information_s))
                      , additional_information_s has 'failure_reason', toint(extract("<failure_reason>Err ([0-9.]+)", 1, additional_information_s))
                      , 0),
                State = case( additional_information_s has 'error_state', toint(extract("<error_state>([0-9.]+)", 1, additional_information_s))
                      , additional_information_s has 'failure_reason', toint(extract("<failure_reason>Err ([0-9.]+), Level ([0-9.]+)", 2, additional_information_s))
                      , 0),
                AdditionalInfo = additional_information_s, timeSlice = floor(TimeGenerated, timeSliceSize)
      | summarize countEvents = count(), countStatements = dcount(Statement), countStatementsWithError = dcountif(Statement, Error in (monitoredErrors))
          , anyMonitoredStatement = anyif(Statement, Error in (monitoredErrors)), anyInfo = anyif(AdditionalInfo, Error in (monitoredErrors))
          by Database, ClientIp, ApplicationName, PrincipalName, timeSlice
      | extend WindowType = case( timeSlice >= ago(detectionWindow), 'detection',
                                             (ago(trainingWindow) <= timeSlice and timeSlice < ago(detectionWindow)), 'training', 'other')
      | where WindowType in ('detection', 'training'));
  let trainingSet =
      processedData
      | where WindowType == 'training'
      | summarize countSlicesWithErrors = dcountif(timeSlice, countStatementsWithError >= monitoredStatementsThreshold)
          by Database;
  processedData
  | where WindowType == 'detection' 
  | join kind = inner (trainingSet) on Database
  | extend IsErrorAnomalyOnStatement = iff(((countStatementsWithError >= monitoredStatementsThreshold) and (countSlicesWithErrors <= trainingSlicesThreshold)), true, false)
      , anomalyScore = round(countStatementsWithError/monitoredStatementsThreshold, 0)
  | where IsErrorAnomalyOnStatement == 'true'
  | sort by anomalyScore desc, timeSlice desc  
OriginalUri: https://github.com/Azure/Azure-Sentinel/blob/master/Solutions/Azure SQL Database solution for sentinel/Analytic Rules/Detection-ErrorsFirewallStatefulAnomalyOnDatabase.yaml
queryFrequency: 1h
triggerThreshold: 0
requiredDataConnectors:
- dataTypes:
  - AzureDiagnostics
  connectorId: AzureSql
version: 1.1.0
status: Available
queryPeriod: 14d
id: 20f87813-3de0-4a9f-a8c0-6aaa3187be08
triggerOperator: gt
entityMappings:
- fieldMappings:
  - identifier: Name
    columnName: PrincipalName
  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
tags:
- SQL
relevantTechniques:
- T1190
severity: Medium
description: |
    'This query batches of distinct SQL queries that failed with error codes that might indicate malicious attempts to gain illegitimate access to the data. When attacker attempts to scan or gain access to server protected by firewall, he will be blocked by firewall and fail with error code 40615. Thus, if we see a large number of logins with such error codes, this could indicate attempts to gain access.'
kind: Scheduled
tactics:
- InitialAccess

ARM

{
  "$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentTemplate.json#",
  "contentVersion": "1.0.0.0",
  "parameters": {
    "workspace": {
      "type": "String"
    }
  },
  "resources": [
    {
      "id": "[concat(resourceId('Microsoft.OperationalInsights/workspaces/providers', parameters('workspace'), 'Microsoft.SecurityInsights'),'/alertRules/20f87813-3de0-4a9f-a8c0-6aaa3187be08')]",
      "name": "[concat(parameters('workspace'),'/Microsoft.SecurityInsights/20f87813-3de0-4a9f-a8c0-6aaa3187be08')]",
      "type": "Microsoft.OperationalInsights/workspaces/providers/alertRules",
      "kind": "Scheduled",
      "apiVersion": "2022-11-01",
      "properties": {
        "displayName": "Firewall errors stateful anomaly on database",
        "description": "'This query batches of distinct SQL queries that failed with error codes that might indicate malicious attempts to gain illegitimate access to the data. When attacker attempts to scan or gain access to server protected by firewall, he will be blocked by firewall and fail with error code 40615. Thus, if we see a large number of logins with such error codes, this could indicate attempts to gain access.'\n",
        "severity": "Medium",
        "enabled": true,
        "query": "let monitoredStatementsThreshold = 1;           // Minimal number of monitored statements in the slice to trigger an anomaly.\nlet trainingSlicesThreshold = 5;                // The maximal amount of slices with monitored statements in the training window before anomaly detection is throttled.\nlet timeSliceSize = 1h;                         // The size of the single timeSlice for individual aggregation.\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 monitoredErrors = pack_array(40615);        // List of sql error codes relevant for this detection.\nlet processedData = materialize (\n    AzureDiagnostics\n    | where TimeGenerated >= ago(detectionWindow)\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              Error = case( additional_information_s has 'error_code', toint(extract(\"<error_code>([0-9.]+)\", 1, additional_information_s))\n                    , additional_information_s has 'failure_reason', toint(extract(\"<failure_reason>Err ([0-9.]+)\", 1, additional_information_s))\n                    , 0),\n              State = case( additional_information_s has 'error_state', toint(extract(\"<error_state>([0-9.]+)\", 1, additional_information_s))\n                    , additional_information_s has 'failure_reason', toint(extract(\"<failure_reason>Err ([0-9.]+), Level ([0-9.]+)\", 2, additional_information_s))\n                    , 0),\n              AdditionalInfo = additional_information_s, timeSlice = floor(TimeGenerated, timeSliceSize)\n    | summarize countEvents = count(), countStatements = dcount(Statement), countStatementsWithError = dcountif(Statement, Error in (monitoredErrors))\n        , anyMonitoredStatement = anyif(Statement, Error in (monitoredErrors)), anyInfo = anyif(AdditionalInfo, Error in (monitoredErrors))\n        by Database, ClientIp, ApplicationName, PrincipalName, timeSlice\n    | extend WindowType = case( timeSlice >= ago(detectionWindow), 'detection',\n                                           (ago(trainingWindow) <= timeSlice and timeSlice < ago(detectionWindow)), 'training', 'other')\n    | where WindowType in ('detection', 'training'));\nlet trainingSet =\n    processedData\n    | where WindowType == 'training'\n    | summarize countSlicesWithErrors = dcountif(timeSlice, countStatementsWithError >= monitoredStatementsThreshold)\n        by Database;\nprocessedData\n| where WindowType == 'detection' \n| join kind = inner (trainingSet) on Database\n| extend IsErrorAnomalyOnStatement = iff(((countStatementsWithError >= monitoredStatementsThreshold) and (countSlicesWithErrors <= trainingSlicesThreshold)), true, false)\n    , anomalyScore = round(countStatementsWithError/monitoredStatementsThreshold, 0)\n| where IsErrorAnomalyOnStatement == 'true'\n| sort by anomalyScore desc, timeSlice desc\n",
        "queryFrequency": "PT1H",
        "queryPeriod": "P14D",
        "triggerOperator": "GreaterThan",
        "triggerThreshold": 0,
        "suppressionDuration": "PT1H",
        "suppressionEnabled": false,
        "tactics": [
          "InitialAccess"
        ],
        "techniques": [
          "T1190"
        ],
        "alertRuleTemplateName": "20f87813-3de0-4a9f-a8c0-6aaa3187be08",
        "customDetails": null,
        "entityMappings": [
          {
            "fieldMappings": [
              {
                "identifier": "Name",
                "columnName": "PrincipalName"
              }
            ],
            "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"
          }
        ],
        "tags": [
          "SQL"
        ],
        "status": "Available",
        "templateVersion": "1.1.0",
        "OriginalUri": "https://github.com/Azure/Azure-Sentinel/blob/master/Solutions/Azure SQL Database solution for sentinel/Analytic Rules/Detection-ErrorsFirewallStatefulAnomalyOnDatabase.yaml"
      }
    }
  ]
}