Exchange workflow MailItemsAccessed operation anomaly

let starttime = 14d;
let endtime = 1d;
let timeframe = 1h;
let scorethreshold = 1.5;
let percentthreshold = 50;
// Preparing the time series data aggregated hourly count of MailItemsAccessd Operation in the form of multi-value array to use with time series anomaly function.
let TimeSeriesData =
OfficeActivity
| where TimeGenerated  between (startofday(ago(starttime))..startofday(ago(endtime)))
| where OfficeWorkload=~ "Exchange" and Operation =~ "MailItemsAccessed" and ResultStatus =~ "Succeeded"
| project TimeGenerated, Operation, MailboxOwnerUPN
| make-series Total=count() on TimeGenerated from startofday(ago(starttime)) to startofday(ago(endtime)) step timeframe;
let TimeSeriesAlerts = TimeSeriesData
| extend (anomalies, score, baseline) = series_decompose_anomalies(Total, scorethreshold, -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 TimeGenerated, Total, baseline, anomalies, score;
// Joining the flagged outlier from the previous step with the original dataset to present contextual information
// during the anomalyhour to analysts to conduct investigation or informed decisions.
TimeSeriesAlerts | where TimeGenerated > ago(2d)
// Join against base logs since specified timeframe to retrive records associated with the hour of anomoly
| join kind=innerunique (
    OfficeActivity
    | where TimeGenerated > ago(2d)
    | extend DateHour = bin(TimeGenerated, 1h)
    | where OfficeWorkload=~ "Exchange" and Operation =~ "MailItemsAccessed" and ResultStatus =~ "Succeeded"
    | summarize HourlyCount=count(), TimeGeneratedMax = arg_max(TimeGenerated, *), IPAdressList = make_set(Client_IPAddress, 1000), SourceIPMax= arg_max(Client_IPAddress, *), ClientInfoStringList= make_set(ClientInfoString, 1000) by MailboxOwnerUPN, Logon_Type, TenantId, UserType, TimeGenerated = bin(TimeGenerated, 1h)
    | where HourlyCount > 25 // Only considering operations with more than 25 hourly count to reduce False Positivies
    | order by HourlyCount desc
) on TimeGenerated
| extend PercentofTotal = round(HourlyCount/Total, 2) * 100
| where PercentofTotal > percentthreshold // Filter Users with count of less than 5 percent of TotalEvents per Hour to remove FPs/ users with very low count of MailItemsAccessed events
| order by PercentofTotal desc
| project-reorder TimeGeneratedMax, Type, OfficeWorkload, Operation, UserId, SourceIPMax, IPAdressList, ClientInfoStringList, HourlyCount, PercentofTotal, Total, baseline, score, anomalies
| extend AccountName = tostring(split(UserId, "@")[0]), AccountUPNSuffix = tostring(split(UserId, "@")[1])

queryPeriod: 14d
version: 2.0.5
kind: Scheduled
triggerThreshold: 0
relevantTechniques:
- T1114
tags:
- Solorigate
- NOBELIUM
triggerOperator: gt
entityMappings:
- fieldMappings:
  - identifier: FullName
    columnName: UserId
  - identifier: Name
    columnName: AccountName
  - identifier: UPNSuffix
    columnName: AccountUPNSuffix
  entityType: Account
- fieldMappings:
  - identifier: Address
    columnName: Client_IPAddress
  entityType: IP
- fieldMappings:
  - identifier: Address
    columnName: SourceIPMax
  entityType: IP
query: |
  let starttime = 14d;
  let endtime = 1d;
  let timeframe = 1h;
  let scorethreshold = 1.5;
  let percentthreshold = 50;
  // Preparing the time series data aggregated hourly count of MailItemsAccessd Operation in the form of multi-value array to use with time series anomaly function.
  let TimeSeriesData =
  OfficeActivity
  | where TimeGenerated  between (startofday(ago(starttime))..startofday(ago(endtime)))
  | where OfficeWorkload=~ "Exchange" and Operation =~ "MailItemsAccessed" and ResultStatus =~ "Succeeded"
  | project TimeGenerated, Operation, MailboxOwnerUPN
  | make-series Total=count() on TimeGenerated from startofday(ago(starttime)) to startofday(ago(endtime)) step timeframe;
  let TimeSeriesAlerts = TimeSeriesData
  | extend (anomalies, score, baseline) = series_decompose_anomalies(Total, scorethreshold, -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 TimeGenerated, Total, baseline, anomalies, score;
  // Joining the flagged outlier from the previous step with the original dataset to present contextual information
  // during the anomalyhour to analysts to conduct investigation or informed decisions.
  TimeSeriesAlerts | where TimeGenerated > ago(2d)
  // Join against base logs since specified timeframe to retrive records associated with the hour of anomoly
  | join kind=innerunique (
      OfficeActivity
      | where TimeGenerated > ago(2d)
      | extend DateHour = bin(TimeGenerated, 1h)
      | where OfficeWorkload=~ "Exchange" and Operation =~ "MailItemsAccessed" and ResultStatus =~ "Succeeded"
      | summarize HourlyCount=count(), TimeGeneratedMax = arg_max(TimeGenerated, *), IPAdressList = make_set(Client_IPAddress, 1000), SourceIPMax= arg_max(Client_IPAddress, *), ClientInfoStringList= make_set(ClientInfoString, 1000) by MailboxOwnerUPN, Logon_Type, TenantId, UserType, TimeGenerated = bin(TimeGenerated, 1h)
      | where HourlyCount > 25 // Only considering operations with more than 25 hourly count to reduce False Positivies
      | order by HourlyCount desc
  ) on TimeGenerated
  | extend PercentofTotal = round(HourlyCount/Total, 2) * 100
  | where PercentofTotal > percentthreshold // Filter Users with count of less than 5 percent of TotalEvents per Hour to remove FPs/ users with very low count of MailItemsAccessed events
  | order by PercentofTotal desc
  | project-reorder TimeGeneratedMax, Type, OfficeWorkload, Operation, UserId, SourceIPMax, IPAdressList, ClientInfoStringList, HourlyCount, PercentofTotal, Total, baseline, score, anomalies
  | extend AccountName = tostring(split(UserId, "@")[0]), AccountUPNSuffix = tostring(split(UserId, "@")[1])  
name: Exchange workflow MailItemsAccessed operation anomaly
queryFrequency: 1d
requiredDataConnectors:
- connectorId: Office365
  dataTypes:
  - OfficeActivity (Exchange)
description: |
  'Identifies anomalous increases in Exchange mail items accessed operations.
  The query leverages KQL built-in anomaly detection algorithms to find large deviations from baseline patterns.
  Sudden increases in execution frequency of sensitive actions should be further investigated for malicious activity.
  Manually change scorethreshold from 1.5 to 3 or higher to reduce the noise based on outliers flagged from the query criteria.
  Read more about MailItemsAccessed- https://docs.microsoft.com/microsoft-365/compliance/advanced-audit?view=o365-worldwide#mailitemsaccessed'  
status: Available
id: b4ceb583-4c44-4555-8ecf-39f572e827ba
OriginalUri: https://github.com/Azure/Azure-Sentinel/blob/master/Solutions/Microsoft 365/Analytic Rules/MailItemsAccessedTimeSeries.yaml
tactics:
- Collection
severity: Medium

{
  "$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentTemplate.json#",
  "contentVersion": "1.0.0.0",
  "parameters": {
    "workspace": {
      "type": "String"
    }
  },
  "resources": [
    {
      "apiVersion": "2023-02-01-preview",
      "id": "[concat(resourceId('Microsoft.OperationalInsights/workspaces/providers', parameters('workspace'), 'Microsoft.SecurityInsights'),'/alertRules/b4ceb583-4c44-4555-8ecf-39f572e827ba')]",
      "kind": "Scheduled",
      "name": "[concat(parameters('workspace'),'/Microsoft.SecurityInsights/b4ceb583-4c44-4555-8ecf-39f572e827ba')]",
      "properties": {
        "alertRuleTemplateName": "b4ceb583-4c44-4555-8ecf-39f572e827ba",
        "customDetails": null,
        "description": "'Identifies anomalous increases in Exchange mail items accessed operations.\nThe query leverages KQL built-in anomaly detection algorithms to find large deviations from baseline patterns.\nSudden increases in execution frequency of sensitive actions should be further investigated for malicious activity.\nManually change scorethreshold from 1.5 to 3 or higher to reduce the noise based on outliers flagged from the query criteria.\nRead more about MailItemsAccessed- https://docs.microsoft.com/microsoft-365/compliance/advanced-audit?view=o365-worldwide#mailitemsaccessed'\n",
        "displayName": "Exchange workflow MailItemsAccessed operation anomaly",
        "enabled": true,
        "entityMappings": [
          {
            "entityType": "Account",
            "fieldMappings": [
              {
                "columnName": "UserId",
                "identifier": "FullName"
              },
              {
                "columnName": "AccountName",
                "identifier": "Name"
              },
              {
                "columnName": "AccountUPNSuffix",
                "identifier": "UPNSuffix"
              }
            ]
          },
          {
            "entityType": "IP",
            "fieldMappings": [
              {
                "columnName": "Client_IPAddress",
                "identifier": "Address"
              }
            ]
          },
          {
            "entityType": "IP",
            "fieldMappings": [
              {
                "columnName": "SourceIPMax",
                "identifier": "Address"
              }
            ]
          }
        ],
        "OriginalUri": "https://github.com/Azure/Azure-Sentinel/blob/master/Solutions/Microsoft 365/Analytic Rules/MailItemsAccessedTimeSeries.yaml",
        "query": "let starttime = 14d;\nlet endtime = 1d;\nlet timeframe = 1h;\nlet scorethreshold = 1.5;\nlet percentthreshold = 50;\n// Preparing the time series data aggregated hourly count of MailItemsAccessd Operation in the form of multi-value array to use with time series anomaly function.\nlet TimeSeriesData =\nOfficeActivity\n| where TimeGenerated  between (startofday(ago(starttime))..startofday(ago(endtime)))\n| where OfficeWorkload=~ \"Exchange\" and Operation =~ \"MailItemsAccessed\" and ResultStatus =~ \"Succeeded\"\n| project TimeGenerated, Operation, MailboxOwnerUPN\n| make-series Total=count() on TimeGenerated from startofday(ago(starttime)) to startofday(ago(endtime)) step timeframe;\nlet TimeSeriesAlerts = TimeSeriesData\n| extend (anomalies, score, baseline) = series_decompose_anomalies(Total, scorethreshold, -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 TimeGenerated, Total, baseline, anomalies, score;\n// Joining the flagged outlier from the previous step with the original dataset to present contextual information\n// during the anomalyhour to analysts to conduct investigation or informed decisions.\nTimeSeriesAlerts | where TimeGenerated > ago(2d)\n// Join against base logs since specified timeframe to retrive records associated with the hour of anomoly\n| join kind=innerunique (\n    OfficeActivity\n    | where TimeGenerated > ago(2d)\n    | extend DateHour = bin(TimeGenerated, 1h)\n    | where OfficeWorkload=~ \"Exchange\" and Operation =~ \"MailItemsAccessed\" and ResultStatus =~ \"Succeeded\"\n    | summarize HourlyCount=count(), TimeGeneratedMax = arg_max(TimeGenerated, *), IPAdressList = make_set(Client_IPAddress, 1000), SourceIPMax= arg_max(Client_IPAddress, *), ClientInfoStringList= make_set(ClientInfoString, 1000) by MailboxOwnerUPN, Logon_Type, TenantId, UserType, TimeGenerated = bin(TimeGenerated, 1h)\n    | where HourlyCount > 25 // Only considering operations with more than 25 hourly count to reduce False Positivies\n    | order by HourlyCount desc\n) on TimeGenerated\n| extend PercentofTotal = round(HourlyCount/Total, 2) * 100\n| where PercentofTotal > percentthreshold // Filter Users with count of less than 5 percent of TotalEvents per Hour to remove FPs/ users with very low count of MailItemsAccessed events\n| order by PercentofTotal desc\n| project-reorder TimeGeneratedMax, Type, OfficeWorkload, Operation, UserId, SourceIPMax, IPAdressList, ClientInfoStringList, HourlyCount, PercentofTotal, Total, baseline, score, anomalies\n| extend AccountName = tostring(split(UserId, \"@\")[0]), AccountUPNSuffix = tostring(split(UserId, \"@\")[1])\n",
        "queryFrequency": "P1D",
        "queryPeriod": "P14D",
        "severity": "Medium",
        "status": "Available",
        "suppressionDuration": "PT1H",
        "suppressionEnabled": false,
        "tactics": [
          "Collection"
        ],
        "tags": [
          "Solorigate",
          "NOBELIUM"
        ],
        "techniques": [
          "T1114"
        ],
        "templateVersion": "2.0.5",
        "triggerOperator": "GreaterThan",
        "triggerThreshold": 0
      },
      "type": "Microsoft.OperationalInsights/workspaces/providers/alertRules"
    }
  ]
}