Analytic rule catalog
Execution attempts stateful anomaly on database
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| Id | 3367fd5e-44b3-4746-a9a5-dc15c8202490 |
| Rulename | Execution attempts stateful anomaly on database |
| Description | This query detects batches of distinct SQL queries that execute (or attempt to) commands that could indicate potential security issues - such as attempts to execute shell commands (e.g. for running illegitimate code). |
| Severity | Medium |
| Tactics | Execution |
| Techniques | T1059 T1059.001 T1059.003 |
| 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%20SQL%20Database%20solution%20for%20sentinel/Analytic%20Rules/Detection-HotwordsExecutionStatefulAnomalyOnDatabase.yaml |
| Version | 1.1.2 |
| Arm template | 3367fd5e-44b3-4746-a9a5-dc15c8202490.json |
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 hotwords = pack_array('xp_cmdshell', 'ps.exe', 'powershell', 'cmd.exe', 'msiexec', '<script>'); // List of monitored hot words.
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,
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)
| extend hasHotword = iff(Statement has_any (hotwords), 1, 0)
| summarize countEvents = count(), countStatements = dcount(Statement)
, countStatementsWithHotwords = dcountif(Statement, hasHotword == 1)
, countFailedStatementsWithHotwords = dcountif(Statement, (hasHotword == 1) and (Error > 0))
, countSuccessfulStatementsWithHotwords = dcountif(Statement, ((hasHotword == 1)) and (Error == 0))
, anyMonitoredStatement = anyif(Statement, (hasHotword == 1))
, anySuccessfulMonitoredStatement = anyif(Statement, (hasHotword == 1) and (Error == 0))
, anyInfo = anyif(AdditionalInfo, hasHotword == 1)
, hotWord = anyif(extract(strcat_array(hotwords, '|'), 0, tolower(Statement)), hasHotword == 1)
by Database, ClientIp, ApplicationName, PrincipalName, timeSlice,HostName,ResourceId
| 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 countSlicesWithHotwords = dcountif(timeSlice, countStatementsWithHotwords >= monitoredStatementsThreshold)
by Database;
processedData
| where WindowType == 'detection'
| join kind = inner (trainingSet) on Database
| extend IsHotwordAnomalyOnStatement = iff(((countStatementsWithHotwords >= monitoredStatementsThreshold) and (countSlicesWithHotwords <= trainingSlicesThreshold)), true, false)
, anomalyScore = round(countStatementsWithHotwords/monitoredStatementsThreshold, 0)
| where IsHotwordAnomalyOnStatement == 'true'
| project TimeGenerated = timeSlice, Database, ClientIp, ApplicationName, PrincipalName, HostName, ResourceId, countEvents, countStatements, countStatementsWithHotwords, anyMonitoredStatement, anyInfo, anomalyScore, hotWord
| extend Name = tostring(split(PrincipalName,'@',0)[0]), UPNSuffix = tostring(split(PrincipalName,'@',1)[0])
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
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 hotwords = pack_array('xp_cmdshell', 'ps.exe', 'powershell', 'cmd.exe', 'msiexec', '<script>'); // List of monitored hot words.
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,
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)
| extend hasHotword = iff(Statement has_any (hotwords), 1, 0)
| summarize countEvents = count(), countStatements = dcount(Statement)
, countStatementsWithHotwords = dcountif(Statement, hasHotword == 1)
, countFailedStatementsWithHotwords = dcountif(Statement, (hasHotword == 1) and (Error > 0))
, countSuccessfulStatementsWithHotwords = dcountif(Statement, ((hasHotword == 1)) and (Error == 0))
, anyMonitoredStatement = anyif(Statement, (hasHotword == 1))
, anySuccessfulMonitoredStatement = anyif(Statement, (hasHotword == 1) and (Error == 0))
, anyInfo = anyif(AdditionalInfo, hasHotword == 1)
, hotWord = anyif(extract(strcat_array(hotwords, '|'), 0, tolower(Statement)), hasHotword == 1)
by Database, ClientIp, ApplicationName, PrincipalName, timeSlice,HostName,ResourceId
| 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 countSlicesWithHotwords = dcountif(timeSlice, countStatementsWithHotwords >= monitoredStatementsThreshold)
by Database;
processedData
| where WindowType == 'detection'
| join kind = inner (trainingSet) on Database
| extend IsHotwordAnomalyOnStatement = iff(((countStatementsWithHotwords >= monitoredStatementsThreshold) and (countSlicesWithHotwords <= trainingSlicesThreshold)), true, false)
, anomalyScore = round(countStatementsWithHotwords/monitoredStatementsThreshold, 0)
| where IsHotwordAnomalyOnStatement == 'true'
| project TimeGenerated = timeSlice, Database, ClientIp, ApplicationName, PrincipalName, HostName, ResourceId, countEvents, countStatements, countStatementsWithHotwords, anyMonitoredStatement, anyInfo, anomalyScore, hotWord
| extend Name = tostring(split(PrincipalName,'@',0)[0]), UPNSuffix = tostring(split(PrincipalName,'@',1)[0])
id: 3367fd5e-44b3-4746-a9a5-dc15c8202490
queryFrequency: 1h
alertDetailsOverride:
alertDisplayNameFormat: Execution attempts stateful anomaly on database {{Database}}
alertDescriptionFormat: An anomaly was detected on database {{Database}} with {{countStatementsWithHotwords}} statements with monitored hot words (xp_cmdshell, powershell, cmd.exe etc.) in the last hour. Investigate the database activity for potential malicious attempts to execute shell commands or run illegitimate code.
status: Available
OriginalUri: https://github.com/Azure/Azure-Sentinel/blob/master/Solutions/Azure%20SQL%20Database%20solution%20for%20sentinel/Analytic%20Rules/Detection-HotwordsExecutionStatefulAnomalyOnDatabase.yaml
version: 1.1.2
severity: Medium
relevantTechniques:
- T1059
- T1059.001
- T1059.003
name: Execution attempts stateful anomaly on database
tags:
- SQL
kind: Scheduled
tactics:
- Execution
requiredDataConnectors:
- dataTypes:
- AzureDiagnostics
connectorId: AzureSql
description: |
'This query detects batches of distinct SQL queries that execute (or attempt to) commands that could indicate potential security issues - such as attempts to execute shell commands (e.g. for running illegitimate code).'
customDetails:
HotWords: hotWord
triggerOperator: gt
triggerThreshold: 0
queryPeriod: 14d
{
"$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentTemplate.json#",
"contentVersion": "1.0.0.0",
"parameters": {
"workspace": {
"type": "String"
}
},
"resources": [
{
"apiVersion": "2024-01-01-preview",
"id": "[concat(resourceId('Microsoft.OperationalInsights/workspaces/providers', parameters('workspace'), 'Microsoft.SecurityInsights'),'/alertRules/3367fd5e-44b3-4746-a9a5-dc15c8202490')]",
"kind": "Scheduled",
"name": "[concat(parameters('workspace'),'/Microsoft.SecurityInsights/3367fd5e-44b3-4746-a9a5-dc15c8202490')]",
"properties": {
"alertDetailsOverride": {
"alertDescriptionFormat": "An anomaly was detected on database {{Database}} with {{countStatementsWithHotwords}} statements with monitored hot words (xp_cmdshell, powershell, cmd.exe etc.) in the last hour. Investigate the database activity for potential malicious attempts to execute shell commands or run illegitimate code.",
"alertDisplayNameFormat": "Execution attempts stateful anomaly on database {{Database}}"
},
"alertRuleTemplateName": "3367fd5e-44b3-4746-a9a5-dc15c8202490",
"customDetails": {
"HotWords": "hotWord"
},
"description": "'This query detects batches of distinct SQL queries that execute (or attempt to) commands that could indicate potential security issues - such as attempts to execute shell commands (e.g. for running illegitimate code).'\n",
"displayName": "Execution attempts 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%20SQL%20Database%20solution%20for%20sentinel/Analytic%20Rules/Detection-HotwordsExecutionStatefulAnomalyOnDatabase.yaml",
"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 hotwords = pack_array('xp_cmdshell', 'ps.exe', 'powershell', 'cmd.exe', 'msiexec', '<script>'); // List of monitored hot words.\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 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 | extend hasHotword = iff(Statement has_any (hotwords), 1, 0)\n | summarize countEvents = count(), countStatements = dcount(Statement)\n , countStatementsWithHotwords = dcountif(Statement, hasHotword == 1)\n , countFailedStatementsWithHotwords = dcountif(Statement, (hasHotword == 1) and (Error > 0))\n , countSuccessfulStatementsWithHotwords = dcountif(Statement, ((hasHotword == 1)) and (Error == 0))\n , anyMonitoredStatement = anyif(Statement, (hasHotword == 1))\n , anySuccessfulMonitoredStatement = anyif(Statement, (hasHotword == 1) and (Error == 0))\n , anyInfo = anyif(AdditionalInfo, hasHotword == 1)\n , hotWord = anyif(extract(strcat_array(hotwords, '|'), 0, tolower(Statement)), hasHotword == 1)\n by Database, ClientIp, ApplicationName, PrincipalName, timeSlice,HostName,ResourceId \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 countSlicesWithHotwords = dcountif(timeSlice, countStatementsWithHotwords >= monitoredStatementsThreshold)\n by Database;\nprocessedData\n| where WindowType == 'detection' \n| join kind = inner (trainingSet) on Database\n| extend IsHotwordAnomalyOnStatement = iff(((countStatementsWithHotwords >= monitoredStatementsThreshold) and (countSlicesWithHotwords <= trainingSlicesThreshold)), true, false)\n , anomalyScore = round(countStatementsWithHotwords/monitoredStatementsThreshold, 0)\n| where IsHotwordAnomalyOnStatement == 'true'\n| project TimeGenerated = timeSlice, Database, ClientIp, ApplicationName, PrincipalName, HostName, ResourceId, countEvents, countStatements, countStatementsWithHotwords, anyMonitoredStatement, anyInfo, anomalyScore, hotWord\n| extend Name = tostring(split(PrincipalName,'@',0)[0]), UPNSuffix = tostring(split(PrincipalName,'@',1)[0])\n",
"queryFrequency": "PT1H",
"queryPeriod": "P14D",
"severity": "Medium",
"status": "Available",
"subTechniques": [
"T1059.001",
"T1059.003"
],
"suppressionDuration": "PT1H",
"suppressionEnabled": false,
"tactics": [
"Execution"
],
"tags": [
"SQL"
],
"techniques": [
"T1059"
],
"templateVersion": "1.1.2",
"triggerOperator": "GreaterThan",
"triggerThreshold": 0
},
"type": "Microsoft.OperationalInsights/workspaces/providers/alertRules"
}
]
}