Query Details
id: c9d0e1f2-a3b4-4c5d-6e7f-8a9b0c1d2e3f
name: "SigninLogs — High-Frequency Repeated Sign-Ins (Automated Credential Abuse)"
version: 1.0.0
kind: Scheduled
description: |
Detects accounts producing 10+ successful sign-in events per hour from the same IP. At that frequency, activity is almost certainly generated by an automated tool, a script, or a C2 framework maintaining stolen token freshness. MITRE ATT&CK: T1078 (Valid Accounts), T1539 (Steal Web Session Cookie).
severity: Medium
requiredDataConnectors:
- connectorId: AzureActiveDirectory
dataTypes:
- SigninLogs
queryFrequency: PT1H
queryPeriod: PT1H
triggerOperator: gt
triggerThreshold: 0
tactics:
- CredentialAccess
- Persistence
relevantTechniques:
- T1078
- T1539
- T1528
query: |
// ---- Network Allowlist (exclude trusted IPs / CIDR / ranges) --------------
let _allow = materialize(union isfuzzy=true (print R="" | take 0), (_GetWatchlist('NetworkAllowlist') | project R = tostring(IPOrRange)) | where isnotempty(R));
let _allowCIDR = toscalar(_allow | where not(R matches regex @'^\d+\.\d+\.\d+\.\d+-\d+\.\d+\.\d+\.\d+$') | extend R = iff(R has '/', R, strcat(R, '/32')) | summarize make_list(R));
let _allowRange = toscalar(_allow | where R matches regex @'^\d+\.\d+\.\d+\.\d+-\d+\.\d+\.\d+\.\d+$' | summarize make_list(R));
let _ExcludeAllowlistedIPs = (T:(IPAddress:string)) {
T
| extend IPAddress = tostring(IPAddress)
| where array_length(_allowCIDR) == 0 or isnull(ipv4_is_in_any_range(IPAddress, _allowCIDR)) or not(ipv4_is_in_any_range(IPAddress, _allowCIDR))
| mv-apply _r = _allowRange to typeof(string) on (
extend _lo = tostring(split(_r,'-')[0]), _hi = tostring(split(_r,'-')[1])
| extend _inRange = ipv4_compare(IPAddress, _lo) >= 0 and ipv4_compare(IPAddress, _hi) <= 0
| summarize _anyInRange = max(toint(_inRange)))
| where isnull(_anyInRange) or _anyInRange == 0
| project-away _anyInRange
};
// ---------------------------------------------------------------------------
let loginMinimum = 10;
SigninLogs
| invoke _ExcludeAllowlistedIPs()
| where TimeGenerated > ago(1h)
| where ResultType == "0"
| where isnotempty(IPAddress) and isnotempty(UserPrincipalName)
| summarize
LoginCount = count(),
UniqueApps = dcount(AppDisplayName),
Apps = make_set(AppDisplayName, 10),
Locations = make_set(Location, 3),
FirstLogin = min(TimeGenerated),
LastLogin = max(TimeGenerated)
by UserPrincipalName, IPAddress
| where LoginCount >= loginMinimum
| extend
WindowMinutes = datetime_diff("minute", LastLogin, FirstLogin),
LoginFreqPerMin = round(
todouble(LoginCount) /
(todouble(datetime_diff("second", LastLogin, FirstLogin)) / 60.0 + 1.0), 2)
| project
TimeGenerated = FirstLogin,
UserPrincipalName, IPAddress,
LoginCount, UniqueApps,
LoginFreqPerMin, WindowMinutes,
Apps, Locations, LastLogin
entityMappings:
- entityType: Account
fieldMappings:
- identifier: FullName
columnName: UserPrincipalName
- entityType: IP
fieldMappings:
- identifier: Address
columnName: IPAddress
customDetails:
LoginCount: LoginCount
LoginFreqPerMin: LoginFreqPerMin
alertDetailsOverride:
alertDisplayNameFormat: "Automated Sign-Ins — {{UserPrincipalName}} from {{IPAddress}} — {{LoginCount}} logins/hr"
alertDescriptionFormat: "Account {{UserPrincipalName}} produced {{LoginCount}} successful sign-ins/hour from {{IPAddress}} — automated tooling or C2 token refresh pattern."
incidentConfiguration:
createIncident: true
groupingConfiguration:
enabled: true
reopenClosedIncident: false
lookbackDuration: PT1H
matchingMethod: AllEntities
groupByEntities:
- Account
- IP
This query is designed to detect potential automated credential abuse by identifying accounts that have more than 10 successful sign-ins per hour from the same IP address. Here's a simplified breakdown of what the query does:
Purpose: The query aims to identify suspicious sign-in activity that could indicate automated tools or scripts are being used to maintain access to an account, potentially using stolen credentials.
Data Source: It uses data from Azure Active Directory's SigninLogs.
Frequency: The query runs every hour and looks at sign-in data from the past hour.
Exclusions: It excludes sign-ins from trusted IP addresses or ranges, which are specified in a "NetworkAllowlist."
Detection Logic:
Output: The query provides details such as the user's principal name, IP address, number of logins, unique applications accessed, and locations.
Alerting: If the conditions are met, an alert is generated with a display name and description indicating the user, IP address, and number of logins per hour. It also creates an incident for further investigation.
Relevance: This query aligns with MITRE ATT&CK techniques T1078 (Valid Accounts) and T1539 (Steal Web Session Cookie), indicating its focus on credential access and persistence tactics.
Overall, this query helps security teams identify and respond to potential automated attacks on user accounts by monitoring for high-frequency sign-ins from the same IP address.

David Alonso
Released: April 20, 2026
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