Brute force attack against an Entra-authenticated Windows device

Status: available
Severity: medium
Time window: 20m
Group by: Anomalies, Baseline, FailureToSuccessDiff, Score, UserDisplayName, UserPrincipalName
Source: github.com/Azure/Azure-Sentinel

'Identifies evidence of brute force activity against Windows devices authenticated via Entra ID (including Entra-joined, hybrid-joined, and Windows 365 Cloud PCs) by detecting multiple authentication failures followed by a successful authentication within a defined time window.'

MITRE ATT&CK coverage

Tactic	Techniques
Credential Access	`T1110` Brute Force

Rule body kusto

id: 3fbc20a4-04c4-464e-8fcb-6667f53e4987
name: Brute force attack against an Entra-authenticated Windows device
description: |
  'Identifies evidence of brute force activity against Windows devices authenticated via Entra ID (including Entra-joined, hybrid-joined, and Windows 365 Cloud PCs) by detecting multiple authentication failures followed by a successful authentication within a defined time window.'
severity: Medium
requiredDataConnectors:
  - connectorId: AzureActiveDirectory
    dataTypes:
      - SigninLogs
queryFrequency: 1d
queryPeriod: 1d
triggerOperator: gt
triggerThreshold: 0
status: Available
tactics:
  - CredentialAccess
relevantTechniques:
  - T1110
query: |
  let authenticationWindow = 20m;
  let sensitivity = 2.5;
  SigninLogs
  | where AppDisplayName =~ "Windows Sign In"
  | extend FailureOrSuccess = iff(ResultType in ("0", "50125", "50140", "70043", "70044"), "Success", "Failure")
  | summarize FailureCount = countif(FailureOrSuccess=="Failure"), SuccessCount = countif(FailureOrSuccess=="Success"), IPAddresses = make_set(IPAddress,1000)
                            by bin(TimeGenerated, authenticationWindow), UserDisplayName, UserPrincipalName
  | extend FailureSuccessDiff = FailureCount - SuccessCount
  | where FailureSuccessDiff > 0
  | summarize Diff = make_list(FailureSuccessDiff, 10000), TimeStamp = make_list(TimeGenerated, 10000) by UserDisplayName, UserPrincipalName//, tostring(IPAddresses)
  | extend (Anomalies, Score, Baseline) = series_decompose_anomalies(Diff, sensitivity, -1, 'linefit')  
  | mv-expand Diff to typeof(double), TimeStamp to typeof(datetime), Anomalies to typeof(double), Score to typeof(double), Baseline to typeof(long)
  | where Anomalies > 0
  | summarize by UserDisplayName, UserPrincipalName, Anomalies, Score, Baseline, FailureToSuccessDiff = Diff
  | join kind=leftouter (
        SigninLogs
        | where AppDisplayName =~ "Windows Sign In"
        | extend OS = DeviceDetail.operatingSystem, Browser = DeviceDetail.browser
        | extend StatusCode = tostring(Status.errorCode), StatusDetails = tostring(Status.additionalDetails)
        | extend State = tostring(LocationDetails.state), City = tostring(LocationDetails.city)
        | summarize StartTime = min(TimeGenerated), 
                    EndTime = max(TimeGenerated), 
                    IPAddresses = make_set(IPAddress,100), 
                    OS = make_set(OS,20), 
                    Browser = make_set(Browser,20), 
                    City = make_set(City,100), 
                    ResultType = make_set(ResultType,100)
                by UserDisplayName, UserPrincipalName, UserId, AppDisplayName
    ) on UserDisplayName, UserPrincipalName
  | project-away UserDisplayName1, UserPrincipalName1
  | extend IPAddressFirst = tostring(IPAddresses[0])
  | extend Name = tostring(split(UserPrincipalName,'@',0)[0]), UPNSuffix = tostring(split(UserPrincipalName,'@',1)[0])
entityMappings:
  - entityType: Account
    fieldMappings:
      - identifier: FullName
        columnName: UserPrincipalName
      - identifier: Name
        columnName: Name
      - identifier: UPNSuffix
        columnName: UPNSuffix
  - entityType: Account
    fieldMappings:
      - identifier: AadUserId
        columnName: UserId
  - entityType: IP
    fieldMappings:
      - identifier: Address
        columnName: IPAddressFirst    
version: 2.0.2
kind: Scheduled

Stages and Predicates

Parameters

let authenticationWindow = 20m;
let sensitivity = 2.5;

Stage 1: `source`

SigninLogs

Stage 2: `where`

| where AppDisplayName =~ "Windows Sign In"

Stage 3: `extend`

| extend FailureOrSuccess = iff(ResultType in ("0", "50125", "50140", "70043", "70044"), "Success", "Failure")

FailureOrSuccess =

ifResultType in (0, 50125, 50140, 70043, 70044)"Success"

else"Failure"

Stage 4: `summarize`

| summarize FailureCount = countif(FailureOrSuccess=="Failure"), SuccessCount = countif(FailureOrSuccess=="Success"), IPAddresses = make_set(IPAddress,1000)
                          by bin(TimeGenerated, authenticationWindow), UserDisplayName, UserPrincipalName

Stage 5: `extend`

| extend FailureSuccessDiff = FailureCount - SuccessCount

Stage 6: `where`

| where FailureSuccessDiff > 0

Stage 7: `summarize`

| summarize Diff = make_list(FailureSuccessDiff, 10000), TimeStamp = make_list(TimeGenerated, 10000) by UserDisplayName, UserPrincipalName

The stages below score time-series anomalies (make-series, series_decompose_anomalies).

Stage 8: `extend`

| extend (Anomalies, Score, Baseline) = series_decompose_anomalies(Diff, sensitivity, -1, 'linefit')

Stage 9: `mv-expand`

| mv-expand Diff to typeof(double), TimeStamp to typeof(datetime), Anomalies to typeof(double), Score to typeof(double), Baseline to typeof(long)

Stage 10: `where`

| where Anomalies > 0

Stage 11: `summarize`

| summarize by UserDisplayName, UserPrincipalName, Anomalies, Score, Baseline, FailureToSuccessDiff = Diff

Stage 12: `join`

| join kind=leftouter (
      SigninLogs
      | where AppDisplayName =~ "Windows Sign In"
      | extend OS = DeviceDetail.operatingSystem, Browser = DeviceDetail.browser
      | extend StatusCode = tostring(Status.errorCode), StatusDetails = tostring(Status.additionalDetails)
      | extend State = tostring(LocationDetails.state), City = tostring(LocationDetails.city)
      | summarize StartTime = min(TimeGenerated), 
                  EndTime = max(TimeGenerated), 
                  IPAddresses = make_set(IPAddress,100), 
                  OS = make_set(OS,20), 
                  Browser = make_set(Browser,20), 
                  City = make_set(City,100), 
                  ResultType = make_set(ResultType,100)
              by UserDisplayName, UserPrincipalName, UserId, AppDisplayName
  ) on UserDisplayName, UserPrincipalName

Stage 13: `project-away`

| project-away UserDisplayName1, UserPrincipalName1

Stage 14: `extend`

| extend IPAddressFirst = tostring(IPAddresses[0])

Stage 15: `extend`

| extend Name = tostring(split(UserPrincipalName,'@',0)[0]), UPNSuffix = tostring(split(UserPrincipalName,'@',1)[0])

Indicators

Each row is a field, operator, and value that the rule matches. The corpus column counts how many other rules in the catalog look for the same combination: high numbers point to widely-used, community-vetted indicators. Blank or 1 shows that the indicator is specific to this rule.

Field	Kind	Values
`Anomalies`	gt	`0` transforms: `cased`
`AppDisplayName`	eq	`Windows Sign In`
`FailureSuccessDiff`	gt	`0` transforms: `cased`

Output fields

Fields the rule emits when it matches. Chronicle authors list these in the outcome block; they appear on the detection and $risk_score drives alerting. Sentinel / Defender XDR rules build them up through project / summarize / extend stages. Sentinel maps these into alert fields via entityMappings and customDetails; Defender XDR custom detections surface them as alert fields directly.

Field	Source
`Anomalies`	`summarize`
`Baseline`	`summarize`
`FailureToSuccessDiff`	`summarize`
`Score`	`summarize`
`UserDisplayName`	`summarize`
`UserPrincipalName`	`summarize`
`IPAddressFirst`	`extend`
`Name`	`extend`
`UPNSuffix`	`extend`

`j` / `k`	Scroll down / up
`d` / `u`	Half-page down / up
`gg` / `G`	Top / bottom
`h` / `l`	History back / forward
`f`	Follow link (`Shift` = new tab)
`/`	Focus search
`?`	Toggle this help
`↑` / `↓`	Navigate search results
`Enter`	Open highlighted result
`Esc`	Close results / dialog

`type:`	`events` / `rules` / `providers`
`vendor:`	`sigma` / `elastic` / `splunk` / `kusto` / `chronicle` (vendor name alone also works: `sigma:`, `kql:`, `secops:`…)
`tactic:`	TA-id, slug, or name: `credential_access`, `TA0006`
`technique:`	technique or sub-technique ID: `T1003`, `T1003.001` (alias `tech:`)
`severity:`	`critical` / `high` / `medium` / `low` / `informational` (alias `sev:`)
`risk_score`	Numeric comparison on the Elastic risk score (0 to 100): `risk_score>50`, `risk_score<=20`, `risk_score=99` (alias `risk`; Elastic rules only)
`stages:`	Rules with exactly N pipeline stages
`correlation:`	`single_event` / `sequence` / `alternatives` / `alternatives_cross_log` / `all_required` / `correlated`
`with:`	Co-occurrence event-id; stacks (`with:4624 with:4769`) to require all, while a comma list in one occurrence (`with:4624,4769`) is an either-or group. Implies multi-event
`like:`	Structural neighbors of a rule slug (equivalents + subsumption stricter / broader): `like:comsvcs_lsass_memory_dump-splunk-sysmon`
`groupby:`	Entity-grouping substring match against `group_by_keys`: `groupby:user`, `groupby:host`
`uses:`	Rules whose predicate tree touches the field (any kind, any value): `uses:CommandLine`
`excludes:`	Rules with top-level `not()` clauses on the field (FP whitelists): `excludes:ParentImage`
`field:` / `value:`	Predicate search; narrows rule cards to those with a matching leaf and drives the indicator tier. Unquoted = substring, wildcards allowed (`value:mimikatz`)
`indicator:`	Shorthand for `field:F value:V`: `indicator:Image=*\powershell.exe`
`kind:`	Filter by predicate kind. Narrows rule cards to those carrying a matching predicate leaf (`vendor:elastic kind:cidr_match`) and drives the indicator tier: `contains` / `starts_with` / `ends_with` / `regex` / `cidr` / `eq` / `in` … (operator aliases `op:`/`match:`)
`has:` / `no:`	`sample`, `field`, `notes`, `refs`, `trace`, `thirdparty`, `rule`, `pattern`, `timewindow`, `threshold`, `newterms`, `sigma`/`elastic`/`splunk`/`kusto`/`chronicle`
`-op:val`	Exclude matches; works on most operators but not `type:`/`like:`/`has:`/`no:` (use `no:<flag>` to exclude a rule flag): `tactic:execution -vendor:splunk`. Standalone `-kind:`/`-field:`/`-value:` drop every rule carrying a matching predicate leaf (`type:rules -kind:is_null`)
`field:"…"` / `value:"…"`	Quoted value = anchored exact match (also allows spaces): `value:"net user"`
`a,b`	Comma = OR inside one operator (`vendor:sigma,elastic`, `severity:high,critical`); repeating a facet merges the same way. `field:`/`value:` never split (literal commas)
`vendors:` / `stage:`	Singular and plural spellings fold to the canonical operator and value: `tactics:` = `tactic:`, `type:event` = `type:events`, `correlation:sequences` = `correlation:sequence`, `has:thresholds` = `has:threshold`
`"quoted phrase"`	Exact-match a multi-word phrase (free text)

Brute force attack against an Entra-authenticated Windows device

MITRE ATT&CK coverage

Rule body kusto

Stages and Predicates

Parameters

Stage 1: `source`

Stage 2: `where`

Stage 3: `extend`

Stage 4: `summarize`

Stage 5: `extend`

Stage 6: `where`

Stage 7: `summarize`

Stage 8: `extend`

Stage 9: `mv-expand`

Stage 10: `where`

Stage 11: `summarize`

Stage 12: `join`

Stage 13: `project-away`

Stage 14: `extend`

Stage 15: `extend`

Indicators

Output fields

Keyboard shortcuts

Search operators

Brute force attack against an Entra-authenticated Windows device

MITRE ATT&CK coverage

Rule body kusto

Stages and Predicates

Parameters

Stage 1: source

Stage 2: where

Stage 3: extend

Stage 4: summarize

Stage 5: extend

Stage 6: where

Stage 7: summarize

Stage 8: extend

Stage 9: mv-expand

Stage 10: where

Stage 11: summarize

Stage 12: join

Stage 13: project-away

Stage 14: extend

Stage 15: extend

Indicators

Output fields

Stage 1: `source`

Stage 2: `where`

Stage 3: `extend`

Stage 4: `summarize`

Stage 5: `extend`

Stage 6: `where`

Stage 7: `summarize`

Stage 8: `extend`

Stage 9: `mv-expand`

Stage 10: `where`

Stage 11: `summarize`

Stage 12: `join`

Stage 13: `project-away`

Stage 14: `extend`

Stage 15: `extend`