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Process Execution Frequency Anomaly

Description

'This detection identifies anomalous spike in frequency of executions of sensitive processes which are often leveraged as attack vectors. The query leverages KQL's built-in anomaly detection algorithms to find large deviations from baseline patterns. Sudden increases in execution frequency of sensitive processes should be further investigated for malicious activity. Tune the values from 1.5 to 3 in series_decompose_anomalies for further outliers or based on custom threshold values for score.'
Rule type
Scheduled
Version
1.0.6
Declared status
Available
Query frequency
1d
Query period
14d
Trigger
gt 0

Declared MITRE coverage

Declared sources

Metadata from the source file. No dependencies inferred from KQL.

Connectors

Data types

KQL query

Original query, unchanged.

let starttime = 14d;
let endtime = 1d;
let timeframe = 1h;
let TotalEventsThreshold = 5;
// Configure the list with sensitive process names 
let ExeList = dynamic(["powershell.exe","cmd.exe","wmic.exe","psexec.exe","cacls.exe","rundll32.exe"]);
let TimeSeriesData =
SecurityEvent
| where EventID == 4688 | extend Process = tolower(Process)
| where TimeGenerated between (startofday(ago(starttime))..startofday(ago(endtime)))
| where Process in~ (ExeList)
| project TimeGenerated, Computer, AccountType, Account, Process
| make-series Total=count() on TimeGenerated from startofday(ago(starttime)) to startofday(ago(endtime)) step timeframe by Process;
let TimeSeriesAlerts = materialize(TimeSeriesData
| extend (anomalies, score, baseline) = series_decompose_anomalies(Total, 1.5, -1, 'linefit')
| mv-expand Total to typeof(double), TimeGenerated to typeof(datetime), anomalies to typeof(double), score to typeof(double), baseline to typeof(long)
| where anomalies > 0
| project Process, TimeGenerated, Total, baseline, anomalies, score
| where Total > TotalEventsThreshold);
let AnomalyHours = materialize(TimeSeriesAlerts  | where TimeGenerated > ago(2d) | project TimeGenerated);
TimeSeriesAlerts
| where TimeGenerated > ago(2d)
| join (
SecurityEvent
| where TimeGenerated between (startofday(ago(starttime))..startofday(ago(endtime)))
| extend DateHour = bin(TimeGenerated, 1h) // create a new column and round to hour
| where DateHour in ((AnomalyHours)) //filter the dataset to only selected anomaly hours
| where EventID == 4688 | extend Process = tolower(Process)
| summarize CommandlineCount = count() by bin(TimeGenerated, 1h), Process, CommandLine, Computer, Account
) on Process, TimeGenerated
| project AnomalyHour = TimeGenerated, Computer, Account, Process, CommandLine, CommandlineCount, Total, baseline, anomalies, score
| extend timestamp = AnomalyHour, NTDomain = split(Account, '\\', 0)[0], Name = split(Account, '\\', 1)[0], HostName = tostring(split(Computer, '.', 0)[0]), DnsDomain = tostring(strcat_array(array_slice(split(Computer, '.'), 1, -1), '.'))

Declared entities

AccountHost

Related content

Links established from declared identifiers and solution manifests.

Source provenance

GitHub

Displayed values come from files in Azure/Azure-Sentinel. They describe the published template, not your workspace configuration.

Source identifier
2c55fe7a-b06f-4029-a5b9-c54a2320d7b8
Additional source files 3Solutions/Windows Security Events/Analytic Rules/TimeSeriesAnomaly-ProcessExecutions.yamlsource ↗Detections/SecurityEvent/TimeSeriesAnomaly-ProcessExecutions.yamlmigration-note ↗Solutions/Windows Security Events/Data/Solution_Windows Security Events.jsonsolution-membership ↗
GSTEP / CATALOG TRACKING

Added to catalog : 16 Sept 2026 · 05:49 UTC
Last change observed : 16 Sept 2026 · 05:49 UTC

GSTEP sync dates, separate from the source content’s publication dates.