Anomaly Sign In Event from an IP

Severity: medium
Time window: 1h
Group by: IPAddress
Author: Juanse
Source: github.com/Azure/Azure-Sentinel

'Identifies sign-in anomalies from an IP in the last hour, targeting multiple users where the password is correct after multiple attempts'

MITRE ATT&CK coverage

Tactic	Techniques
Initial Access	`T1078` Valid Accounts

Rule body kusto

id: 9c1e9381-79dd-4ddf-9570-b73a1dc59fe0
name: Anomaly Sign In Event from an IP
description: |
  'Identifies sign-in anomalies from an IP in the last hour, targeting multiple users where the password is correct after multiple attempts'
severity: Medium
requiredDataConnectors:
  - connectorId: AzureActiveDirectory
    dataTypes:
      - SigninLogs 
queryFrequency: 1h
queryPeriod: 1h
triggerOperator: gt
triggerThreshold: 0
tactics:
  - InitialAccess
relevantTechniques:
  - T1078
query: |
  let LookBack = 1h;
  let Data = (
  SigninLogs
  | where TimeGenerated >= ago(LookBack)
  | where parse_json(NetworkLocationDetails)[0].networkType != "trustedNamedLocation" // Excludes known tagged networks
  // Counts the number of sign in events in the last hour every 15 minutes by IP
  | make-series EventCounts = count() on TimeGenerated from ago(LookBack) to now() step 15m by IPAddress 
  );
  let AnomalyAlert = (
  Data
  | extend (Anomalies, Score, Baseline) = series_decompose_anomalies(EventCounts,1.5,-1,'linefit')
  | mv-expand EventCounts,TimeGenerated,Anomalies to typeof(double),Baseline to typeof(long),Score to typeof(double)
  | where Anomalies > 0
  );
  AnomalyAlert
  | join kind = inner (SigninLogs
  | where TimeGenerated between (ago(LookBack) .. now())
  | where parse_json(NetworkLocationDetails)[0].networkType != "trustedNamedLocation"
  | extend PasswordResult = tostring(parse_json(AuthenticationDetails).authenticationStepResultDetail)
  | summarize UserCount = dcount(UserPrincipalName), UserList = make_set(UserPrincipalName), AppName = make_set(AppDisplayName), PasswordResult = make_list(PasswordResult) by IPAddress) on IPAddress
  | where PasswordResult has "Correct Password"
  | where UserCount > 1 // looks for events targeting more than one user.
entityMappings:
  - entityType: IP
    fieldMappings:
      - identifier: Address
        columnName: IPAddress
customDetails:
  Score: Score
  Baseline: Baseline
  UserCount: UserCount
  AppName: AppName
  PasswordResult: PasswordResult
  UserList: UserList
version: 1.0.1
kind: Scheduled
metadata:
    source:
        kind: Community
    author:
        name: Juanse
    support:
        tier: Community
    categories:
        domains: [ "Identity" ]

Stages and Predicates

Parameters

let LookBack = 1h;

The stages below define let AnomalyAlert (the rule's main pipeline source).

Stage 1: `source`

SigninLogs

Stage 2: `where`

| where TimeGenerated >= ago(LookBack)

Stage 3: `where`

| where parse_json(NetworkLocationDetails)[0].networkType != "trustedNamedLocation"

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

Stage 4: `summarize`

| make-series EventCounts = count() on TimeGenerated from ago(LookBack) to now() step 15m by IPAddress

Stage 5: `extend`

| extend (Anomalies, Score, Baseline) = series_decompose_anomalies(EventCounts,1.5,-1,'linefit')

Stage 6: `mv-expand`

| mv-expand EventCounts,TimeGenerated,Anomalies to typeof(double),Baseline to typeof(long),Score to typeof(double)

Stage 7: `where`

| where Anomalies > 0

The stages below run on AnomalyAlert (the outer pipeline).

Stage 8: `join`

AnomalyAlert
| join kind = inner (SigninLogs
| where TimeGenerated between (ago(LookBack) .. now())
| where parse_json(NetworkLocationDetails)[0].networkType != "trustedNamedLocation"
| extend PasswordResult = tostring(parse_json(AuthenticationDetails).authenticationStepResultDetail)
| summarize UserCount = dcount(UserPrincipalName), UserList = make_set(UserPrincipalName), AppName = make_set(AppDisplayName), PasswordResult = make_list(PasswordResult) by IPAddress) on IPAddress

Stage 9: `where`

| where PasswordResult has "Correct Password"

Stage 10: `where`

| where UserCount > 1

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`
`PasswordResult`	match	`Correct Password` transforms: `term`
`UserCount`	gt	`1` transforms: `cased`
`networkType`	ne	`trustedNamedLocation` 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
`EventCounts`	`summarize`
`IPAddress`	`summarize`
`Anomalies`	`extend`
`Baseline`	`extend`
`Score`	`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)

Anomaly Sign In Event from an IP

MITRE ATT&CK coverage

Rule body kusto

Stages and Predicates

Parameters

Stage 1: `source`

Stage 2: `where`

Stage 3: `where`

Stage 4: `summarize`

Stage 5: `extend`

Stage 6: `mv-expand`

Stage 7: `where`

Stage 8: `join`

Stage 9: `where`

Stage 10: `where`

Indicators

Output fields

Keyboard shortcuts

Search operators

Anomaly Sign In Event from an IP

MITRE ATT&CK coverage

Rule body kusto

Stages and Predicates

Parameters

Stage 1: source

Stage 2: where

Stage 3: where

Stage 4: summarize

Stage 5: extend

Stage 6: mv-expand

Stage 7: where

Stage 8: join

Stage 9: where

Stage 10: where

Indicators

Output fields

Stage 1: `source`

Stage 2: `where`

Stage 3: `where`

Stage 4: `summarize`

Stage 5: `extend`

Stage 6: `mv-expand`

Stage 7: `where`

Stage 8: `join`

Stage 9: `where`

Stage 10: `where`