Process Execution Frequency Anomaly

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), '.'))

queryPeriod: 14d
requiredDataConnectors:
- connectorId: SecurityEvents
  dataTypes:
  - SecurityEvent
- connectorId: WindowsSecurityEvents
  dataTypes:
  - SecurityEvent
triggerThreshold: 0
OriginalUri: https://github.com/Azure/Azure-Sentinel/blob/master/Solutions/Windows Security Events/Analytic Rules/TimeSeriesAnomaly-ProcessExecutions.yaml
tactics:
- Execution
triggerOperator: gt
severity: Medium
name: Process Execution Frequency Anomaly
relevantTechniques:
- T1059
query: |
  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), '.'))  
queryFrequency: 1d
id: 2c55fe7a-b06f-4029-a5b9-c54a2320d7b8
status: Available
kind: Scheduled
entityMappings:
- fieldMappings:
  - columnName: Account
    identifier: FullName
  - columnName: Name
    identifier: Name
  - columnName: NTDomain
    identifier: NTDomain
  entityType: Account
- fieldMappings:
  - columnName: Computer
    identifier: FullName
  - columnName: HostName
    identifier: HostName
  - columnName: DnsDomain
    identifier: DnsDomain
  entityType: Host
version: 1.0.6
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.'  

{
  "$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/2c55fe7a-b06f-4029-a5b9-c54a2320d7b8')]",
      "kind": "Scheduled",
      "name": "[concat(parameters('workspace'),'/Microsoft.SecurityInsights/2c55fe7a-b06f-4029-a5b9-c54a2320d7b8')]",
      "properties": {
        "alertRuleTemplateName": "2c55fe7a-b06f-4029-a5b9-c54a2320d7b8",
        "customDetails": null,
        "description": "'This detection identifies anomalous spike in frequency of executions of sensitive processes which are often leveraged as attack vectors.\nThe query leverages KQL's built-in anomaly detection algorithms to find large deviations from baseline patterns.\nSudden increases in execution frequency of sensitive processes should be further investigated for malicious activity.\nTune the values from 1.5 to 3 in series_decompose_anomalies for further outliers or based on custom threshold values for score.'\n",
        "displayName": "Process Execution Frequency Anomaly",
        "enabled": true,
        "entityMappings": [
          {
            "entityType": "Account",
            "fieldMappings": [
              {
                "columnName": "Account",
                "identifier": "FullName"
              },
              {
                "columnName": "Name",
                "identifier": "Name"
              },
              {
                "columnName": "NTDomain",
                "identifier": "NTDomain"
              }
            ]
          },
          {
            "entityType": "Host",
            "fieldMappings": [
              {
                "columnName": "Computer",
                "identifier": "FullName"
              },
              {
                "columnName": "HostName",
                "identifier": "HostName"
              },
              {
                "columnName": "DnsDomain",
                "identifier": "DnsDomain"
              }
            ]
          }
        ],
        "OriginalUri": "https://github.com/Azure/Azure-Sentinel/blob/master/Solutions/Windows Security Events/Analytic Rules/TimeSeriesAnomaly-ProcessExecutions.yaml",
        "query": "let starttime = 14d;\nlet endtime = 1d;\nlet timeframe = 1h;\nlet TotalEventsThreshold = 5;\n// Configure the list with sensitive process names \nlet ExeList = dynamic([\"powershell.exe\",\"cmd.exe\",\"wmic.exe\",\"psexec.exe\",\"cacls.exe\",\"rundll32.exe\"]);\nlet TimeSeriesData =\nSecurityEvent\n| where EventID == 4688 | extend Process = tolower(Process)\n| where TimeGenerated between (startofday(ago(starttime))..startofday(ago(endtime)))\n| where Process in~ (ExeList)\n| project TimeGenerated, Computer, AccountType, Account, Process\n| make-series Total=count() on TimeGenerated from startofday(ago(starttime)) to startofday(ago(endtime)) step timeframe by Process;\nlet TimeSeriesAlerts = materialize(TimeSeriesData\n| extend (anomalies, score, baseline) = series_decompose_anomalies(Total, 1.5, -1, 'linefit')\n| mv-expand Total to typeof(double), TimeGenerated to typeof(datetime), anomalies to typeof(double), score to typeof(double), baseline to typeof(long)\n| where anomalies > 0\n| project Process, TimeGenerated, Total, baseline, anomalies, score\n| where Total > TotalEventsThreshold);\nlet AnomalyHours = materialize(TimeSeriesAlerts  | where TimeGenerated > ago(2d) | project TimeGenerated);\nTimeSeriesAlerts\n| where TimeGenerated > ago(2d)\n| join (\nSecurityEvent\n| where TimeGenerated between (startofday(ago(starttime))..startofday(ago(endtime)))\n| extend DateHour = bin(TimeGenerated, 1h) // create a new column and round to hour\n| where DateHour in ((AnomalyHours)) //filter the dataset to only selected anomaly hours\n| where EventID == 4688 | extend Process = tolower(Process)\n| summarize CommandlineCount = count() by bin(TimeGenerated, 1h), Process, CommandLine, Computer, Account\n) on Process, TimeGenerated\n| project AnomalyHour = TimeGenerated, Computer, Account, Process, CommandLine, CommandlineCount, Total, baseline, anomalies, score\n| 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), '.'))\n",
        "queryFrequency": "P1D",
        "queryPeriod": "P14D",
        "severity": "Medium",
        "status": "Available",
        "subTechniques": [],
        "suppressionDuration": "PT1H",
        "suppressionEnabled": false,
        "tactics": [
          "Execution"
        ],
        "techniques": [
          "T1059"
        ],
        "templateVersion": "1.0.6",
        "triggerOperator": "GreaterThan",
        "triggerThreshold": 0
      },
      "type": "Microsoft.OperationalInsights/workspaces/providers/alertRules"
    }
  ]
}