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Threat Essentials - Time series anomaly for data size transferred to public internet

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Idb49a1093-cbf6-4973-89ac-2eef98f533c6
RulenameThreat Essentials - Time series anomaly for data size transferred to public internet
DescriptionIdentifies anomalous data transfer to public networks. The query leverages built-in KQL anomaly detection algorithms that detects large deviations from a baseline pattern.

A sudden increase in data transferred to unknown public networks is an indication of data exfiltration attempts and should be investigated.

The higher the score, the further it is from the baseline value.

The output is aggregated to provide summary view of unique source IP to destination IP address and port bytes sent traffic observed in the flagged anomaly hour.

The source IP addresses which were sending less than bytessentperhourthreshold have been exluded whose value can be adjusted as needed .

You may have to run queries for individual source IP addresses from SourceIPlist to determine if anything looks suspicious
SeverityMedium
TacticsExfiltration
TechniquesT1030
Required data connectorsAzureMonitor(VMInsights)
CiscoASA
PaloAltoNetworks
KindScheduled
Query frequency1d
Query period14d
Trigger threshold1
Trigger operatorgt
Source Urihttps://github.com/Azure/Azure-Sentinel/blob/master/Solutions/SecurityThreatEssentialSolution/Analytic Rules/Threat_Essentials_TimeSeriesAnomaly-MultiVendor_DataExfiltration.yaml
Version1.0.3
Arm templateb49a1093-cbf6-4973-89ac-2eef98f533c6.json
Deploy To Azure
let starttime = 14d;
let endtime = 1d;
let timeframe = 1h;
let scorethreshold = 5;
let bytessentperhourthreshold = 10;
let PrivateIPregex = @'^127\.|^10\.|^172\.1[6-9]\.|^172\.2[0-9]\.|^172\.3[0-1]\.|^192\.168\.';
let TimeSeriesData = (union isfuzzy=true
(
VMConnection
| where TimeGenerated between (startofday(ago(starttime))..startofday(ago(endtime)))
| where isnotempty(DestinationIp) and isnotempty(SourceIp)
| extend DestinationIpType = iff(DestinationIp matches regex PrivateIPregex,"private" ,"public" )
| where DestinationIpType == "public" | extend DeviceVendor = "VMConnection"
| project TimeGenerated, BytesSent, DeviceVendor
| make-series TotalBytesSent=sum(BytesSent) on TimeGenerated from startofday(ago(starttime)) to startofday(ago(endtime)) step timeframe by DeviceVendor
),
(
CommonSecurityLog
| where TimeGenerated between (startofday(ago(starttime))..startofday(ago(endtime)))
| where isnotempty(DestinationIP) and isnotempty(SourceIP)
| extend DestinationIpType = iff(DestinationIP matches regex PrivateIPregex,"private" ,"public" )
| where DestinationIpType == "public"
| project TimeGenerated, SentBytes, DeviceVendor
| make-series TotalBytesSent=sum(SentBytes) on TimeGenerated from startofday(ago(starttime)) to startofday(ago(endtime)) step timeframe by DeviceVendor
)
);
//Filter anomolies against TimeSeriesData
let TimeSeriesAlerts = materialize(TimeSeriesData
| extend (anomalies, score, baseline) = series_decompose_anomalies(TotalBytesSent, scorethreshold, -1, 'linefit')
| mv-expand TotalBytesSent to typeof(double), TimeGenerated to typeof(datetime), anomalies to typeof(double),score to typeof(double), baseline to typeof(long)
| where anomalies > 0 | extend AnomalyHour = TimeGenerated
| extend TotalBytesSentinMBperHour = round(((TotalBytesSent / 1024)/1024),2), baselinebytessentperHour = round(((baseline / 1024)/1024),2), score = round(score,2)
| project DeviceVendor, AnomalyHour, TimeGenerated, TotalBytesSentinMBperHour, baselinebytessentperHour, anomalies, score);
let AnomalyHours = materialize(TimeSeriesAlerts  | where TimeGenerated > ago(2d) | project TimeGenerated);
//Union of all BaseLogs aggregated per hour
let BaseLogs = (union isfuzzy=true
(
CommonSecurityLog
| where isnotempty(DestinationIP) and isnotempty(SourceIP)
| where TimeGenerated > ago(2d)
| 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
| extend DestinationIpType = iff(DestinationIP matches regex PrivateIPregex,"private" ,"public" )
| where DestinationIpType == "public"
| extend SentBytesinMB = ((SentBytes / 1024)/1024), ReceivedBytesinMB = ((ReceivedBytes / 1024)/1024)
| summarize HourlyCount = count(), TimeGeneratedMax=arg_max(TimeGenerated, *), DestinationIPList=make_set(DestinationIP, 100), DestinationPortList = make_set(DestinationPort,100), TotalSentBytesinMB = sum(SentBytesinMB), TotalReceivedBytesinMB = sum(ReceivedBytesinMB) by SourceIP, DeviceVendor, TimeGeneratedHour=bin(TimeGenerated,1h)
| where TotalSentBytesinMB > bytessentperhourthreshold
| sort by TimeGeneratedHour asc, TotalSentBytesinMB desc
| extend Rank=row_number(1, prev(TimeGeneratedHour) != TimeGeneratedHour) // Ranking the dataset per Hourly Partition
| where Rank < 10  // Selecting Top 10 records with Highest BytesSent in each Hour
| project DeviceVendor, TimeGeneratedHour, TimeGeneratedMax, SourceIP, DestinationIPList, DestinationPortList, TotalSentBytesinMB, TotalReceivedBytesinMB, Rank
),
(
VMConnection
| where isnotempty(DestinationIp) and isnotempty(SourceIp)
| where TimeGenerated > ago(2d)
| 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
| extend SourceIP = SourceIp, DestinationIP = DestinationIp
| extend DestinationIpType = iff(DestinationIp matches regex PrivateIPregex,"private" ,"public" )
| where DestinationIpType == "public" | extend DeviceVendor = "VMConnection"
| extend SentBytesinMB = ((BytesSent / 1024)/1024), ReceivedBytesinMB = ((BytesReceived / 1024)/1024)
| summarize HourlyCount = count(),TimeGeneratedMax=arg_max(TimeGenerated, *), DestinationIPList=make_set(DestinationIP, 100), DestinationPortList = make_set(DestinationPort, 100), TotalSentBytesinMB = sum(SentBytesinMB),TotalReceivedBytesinMB = sum(ReceivedBytesinMB) by SourceIP, DeviceVendor, TimeGeneratedHour=bin(TimeGenerated,1h)
| where TotalSentBytesinMB > bytessentperhourthreshold
| sort by TimeGeneratedHour asc, TotalSentBytesinMB desc
| extend Rank=row_number(1, prev(TimeGeneratedHour) != TimeGeneratedHour) // Ranking the dataset per Hourly Partition
| where Rank < 10  // Selecting Top 10 records with Highest BytesSent in each Hour
| project DeviceVendor, TimeGeneratedHour, TimeGeneratedMax, SourceIP, DestinationIPList, DestinationPortList, TotalSentBytesinMB, TotalReceivedBytesinMB, Rank
)
);
// Join against base logs to retrive records associated with the hour of anomoly
TimeSeriesAlerts
| where TimeGenerated > ago(2d)
| join (
    BaseLogs | extend AnomalyHour = TimeGeneratedHour
) on DeviceVendor, AnomalyHour | sort by score desc
| project DeviceVendor, AnomalyHour,TimeGeneratedMax, SourceIP, DestinationIPList, DestinationPortList, TotalSentBytesinMB, TotalReceivedBytesinMB, TotalBytesSentinMBperHour, baselinebytessentperHour, score, anomalies
| summarize EventCount = count(), StartTimeUtc= min(TimeGeneratedMax), EndTimeUtc= max(TimeGeneratedMax), SourceIPMax= arg_max(SourceIP,*), TotalBytesSentinMB = sum(TotalSentBytesinMB), TotalBytesReceivedinMB = sum(TotalReceivedBytesinMB), SourceIPList = make_set(SourceIP, 100), DestinationIPList = make_set(DestinationIPList, 100) by AnomalyHour,TotalBytesSentinMBperHour, baselinebytessentperHour, score, anomalies
| project DeviceVendor, AnomalyHour, StartTimeUtc, EndTimeUtc, SourceIPMax, SourceIPList, DestinationIPList, DestinationPortList, TotalBytesSentinMB, TotalBytesReceivedinMB, TotalBytesSentinMBperHour, baselinebytessentperHour, score, anomalies, EventCount
queryPeriod: 14d
version: 1.0.3
kind: Scheduled
triggerThreshold: 1
relevantTechniques:
- T1030
tags:
- DEV-0537
triggerOperator: gt
entityMappings:
- fieldMappings:
  - identifier: Address
    columnName: SourceIPMax
  entityType: IP
query: |
  let starttime = 14d;
  let endtime = 1d;
  let timeframe = 1h;
  let scorethreshold = 5;
  let bytessentperhourthreshold = 10;
  let PrivateIPregex = @'^127\.|^10\.|^172\.1[6-9]\.|^172\.2[0-9]\.|^172\.3[0-1]\.|^192\.168\.';
  let TimeSeriesData = (union isfuzzy=true
  (
  VMConnection
  | where TimeGenerated between (startofday(ago(starttime))..startofday(ago(endtime)))
  | where isnotempty(DestinationIp) and isnotempty(SourceIp)
  | extend DestinationIpType = iff(DestinationIp matches regex PrivateIPregex,"private" ,"public" )
  | where DestinationIpType == "public" | extend DeviceVendor = "VMConnection"
  | project TimeGenerated, BytesSent, DeviceVendor
  | make-series TotalBytesSent=sum(BytesSent) on TimeGenerated from startofday(ago(starttime)) to startofday(ago(endtime)) step timeframe by DeviceVendor
  ),
  (
  CommonSecurityLog
  | where TimeGenerated between (startofday(ago(starttime))..startofday(ago(endtime)))
  | where isnotempty(DestinationIP) and isnotempty(SourceIP)
  | extend DestinationIpType = iff(DestinationIP matches regex PrivateIPregex,"private" ,"public" )
  | where DestinationIpType == "public"
  | project TimeGenerated, SentBytes, DeviceVendor
  | make-series TotalBytesSent=sum(SentBytes) on TimeGenerated from startofday(ago(starttime)) to startofday(ago(endtime)) step timeframe by DeviceVendor
  )
  );
  //Filter anomolies against TimeSeriesData
  let TimeSeriesAlerts = materialize(TimeSeriesData
  | extend (anomalies, score, baseline) = series_decompose_anomalies(TotalBytesSent, scorethreshold, -1, 'linefit')
  | mv-expand TotalBytesSent to typeof(double), TimeGenerated to typeof(datetime), anomalies to typeof(double),score to typeof(double), baseline to typeof(long)
  | where anomalies > 0 | extend AnomalyHour = TimeGenerated
  | extend TotalBytesSentinMBperHour = round(((TotalBytesSent / 1024)/1024),2), baselinebytessentperHour = round(((baseline / 1024)/1024),2), score = round(score,2)
  | project DeviceVendor, AnomalyHour, TimeGenerated, TotalBytesSentinMBperHour, baselinebytessentperHour, anomalies, score);
  let AnomalyHours = materialize(TimeSeriesAlerts  | where TimeGenerated > ago(2d) | project TimeGenerated);
  //Union of all BaseLogs aggregated per hour
  let BaseLogs = (union isfuzzy=true
  (
  CommonSecurityLog
  | where isnotempty(DestinationIP) and isnotempty(SourceIP)
  | where TimeGenerated > ago(2d)
  | 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
  | extend DestinationIpType = iff(DestinationIP matches regex PrivateIPregex,"private" ,"public" )
  | where DestinationIpType == "public"
  | extend SentBytesinMB = ((SentBytes / 1024)/1024), ReceivedBytesinMB = ((ReceivedBytes / 1024)/1024)
  | summarize HourlyCount = count(), TimeGeneratedMax=arg_max(TimeGenerated, *), DestinationIPList=make_set(DestinationIP, 100), DestinationPortList = make_set(DestinationPort,100), TotalSentBytesinMB = sum(SentBytesinMB), TotalReceivedBytesinMB = sum(ReceivedBytesinMB) by SourceIP, DeviceVendor, TimeGeneratedHour=bin(TimeGenerated,1h)
  | where TotalSentBytesinMB > bytessentperhourthreshold
  | sort by TimeGeneratedHour asc, TotalSentBytesinMB desc
  | extend Rank=row_number(1, prev(TimeGeneratedHour) != TimeGeneratedHour) // Ranking the dataset per Hourly Partition
  | where Rank < 10  // Selecting Top 10 records with Highest BytesSent in each Hour
  | project DeviceVendor, TimeGeneratedHour, TimeGeneratedMax, SourceIP, DestinationIPList, DestinationPortList, TotalSentBytesinMB, TotalReceivedBytesinMB, Rank
  ),
  (
  VMConnection
  | where isnotempty(DestinationIp) and isnotempty(SourceIp)
  | where TimeGenerated > ago(2d)
  | 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
  | extend SourceIP = SourceIp, DestinationIP = DestinationIp
  | extend DestinationIpType = iff(DestinationIp matches regex PrivateIPregex,"private" ,"public" )
  | where DestinationIpType == "public" | extend DeviceVendor = "VMConnection"
  | extend SentBytesinMB = ((BytesSent / 1024)/1024), ReceivedBytesinMB = ((BytesReceived / 1024)/1024)
  | summarize HourlyCount = count(),TimeGeneratedMax=arg_max(TimeGenerated, *), DestinationIPList=make_set(DestinationIP, 100), DestinationPortList = make_set(DestinationPort, 100), TotalSentBytesinMB = sum(SentBytesinMB),TotalReceivedBytesinMB = sum(ReceivedBytesinMB) by SourceIP, DeviceVendor, TimeGeneratedHour=bin(TimeGenerated,1h)
  | where TotalSentBytesinMB > bytessentperhourthreshold
  | sort by TimeGeneratedHour asc, TotalSentBytesinMB desc
  | extend Rank=row_number(1, prev(TimeGeneratedHour) != TimeGeneratedHour) // Ranking the dataset per Hourly Partition
  | where Rank < 10  // Selecting Top 10 records with Highest BytesSent in each Hour
  | project DeviceVendor, TimeGeneratedHour, TimeGeneratedMax, SourceIP, DestinationIPList, DestinationPortList, TotalSentBytesinMB, TotalReceivedBytesinMB, Rank
  )
  );
  // Join against base logs to retrive records associated with the hour of anomoly
  TimeSeriesAlerts
  | where TimeGenerated > ago(2d)
  | join (
      BaseLogs | extend AnomalyHour = TimeGeneratedHour
  ) on DeviceVendor, AnomalyHour | sort by score desc
  | project DeviceVendor, AnomalyHour,TimeGeneratedMax, SourceIP, DestinationIPList, DestinationPortList, TotalSentBytesinMB, TotalReceivedBytesinMB, TotalBytesSentinMBperHour, baselinebytessentperHour, score, anomalies
  | summarize EventCount = count(), StartTimeUtc= min(TimeGeneratedMax), EndTimeUtc= max(TimeGeneratedMax), SourceIPMax= arg_max(SourceIP,*), TotalBytesSentinMB = sum(TotalSentBytesinMB), TotalBytesReceivedinMB = sum(TotalReceivedBytesinMB), SourceIPList = make_set(SourceIP, 100), DestinationIPList = make_set(DestinationIPList, 100) by AnomalyHour,TotalBytesSentinMBperHour, baselinebytessentperHour, score, anomalies
  | project DeviceVendor, AnomalyHour, StartTimeUtc, EndTimeUtc, SourceIPMax, SourceIPList, DestinationIPList, DestinationPortList, TotalBytesSentinMB, TotalBytesReceivedinMB, TotalBytesSentinMBperHour, baselinebytessentperHour, score, anomalies, EventCount  
name: Threat Essentials - Time series anomaly for data size transferred to public internet
queryFrequency: 1d
requiredDataConnectors:
- connectorId: CiscoASA
  dataTypes:
  - CommonSecurityLog
- connectorId: PaloAltoNetworks
  dataTypes:
  - CommonSecurityLog
- connectorId: AzureMonitor(VMInsights)
  dataTypes:
  - VMConnection
description: |
  'Identifies anomalous data transfer to public networks. The query leverages built-in KQL anomaly detection algorithms that detects large deviations from a baseline pattern.
  A sudden increase in data transferred to unknown public networks is an indication of data exfiltration attempts and should be investigated.
  The higher the score, the further it is from the baseline value.
  The output is aggregated to provide summary view of unique source IP to destination IP address and port bytes sent traffic observed in the flagged anomaly hour.
  The source IP addresses which were sending less than bytessentperhourthreshold have been exluded whose value can be adjusted as needed .
  You may have to run queries for individual source IP addresses from SourceIPlist to determine if anything looks suspicious'  
status: Available
id: b49a1093-cbf6-4973-89ac-2eef98f533c6
OriginalUri: https://github.com/Azure/Azure-Sentinel/blob/master/Solutions/SecurityThreatEssentialSolution/Analytic Rules/Threat_Essentials_TimeSeriesAnomaly-MultiVendor_DataExfiltration.yaml
tactics:
- Exfiltration
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/b49a1093-cbf6-4973-89ac-2eef98f533c6')]",
      "kind": "Scheduled",
      "name": "[concat(parameters('workspace'),'/Microsoft.SecurityInsights/b49a1093-cbf6-4973-89ac-2eef98f533c6')]",
      "properties": {
        "alertRuleTemplateName": "b49a1093-cbf6-4973-89ac-2eef98f533c6",
        "customDetails": null,
        "description": "'Identifies anomalous data transfer to public networks. The query leverages built-in KQL anomaly detection algorithms that detects large deviations from a baseline pattern.\nA sudden increase in data transferred to unknown public networks is an indication of data exfiltration attempts and should be investigated.\nThe higher the score, the further it is from the baseline value.\nThe output is aggregated to provide summary view of unique source IP to destination IP address and port bytes sent traffic observed in the flagged anomaly hour.\nThe source IP addresses which were sending less than bytessentperhourthreshold have been exluded whose value can be adjusted as needed .\nYou may have to run queries for individual source IP addresses from SourceIPlist to determine if anything looks suspicious'\n",
        "displayName": "Threat Essentials - Time series anomaly for data size transferred to public internet",
        "enabled": true,
        "entityMappings": [
          {
            "entityType": "IP",
            "fieldMappings": [
              {
                "columnName": "SourceIPMax",
                "identifier": "Address"
              }
            ]
          }
        ],
        "OriginalUri": "https://github.com/Azure/Azure-Sentinel/blob/master/Solutions/SecurityThreatEssentialSolution/Analytic Rules/Threat_Essentials_TimeSeriesAnomaly-MultiVendor_DataExfiltration.yaml",
        "query": "let starttime = 14d;\nlet endtime = 1d;\nlet timeframe = 1h;\nlet scorethreshold = 5;\nlet bytessentperhourthreshold = 10;\nlet PrivateIPregex = @'^127\\.|^10\\.|^172\\.1[6-9]\\.|^172\\.2[0-9]\\.|^172\\.3[0-1]\\.|^192\\.168\\.';\nlet TimeSeriesData = (union isfuzzy=true\n(\nVMConnection\n| where TimeGenerated between (startofday(ago(starttime))..startofday(ago(endtime)))\n| where isnotempty(DestinationIp) and isnotempty(SourceIp)\n| extend DestinationIpType = iff(DestinationIp matches regex PrivateIPregex,\"private\" ,\"public\" )\n| where DestinationIpType == \"public\" | extend DeviceVendor = \"VMConnection\"\n| project TimeGenerated, BytesSent, DeviceVendor\n| make-series TotalBytesSent=sum(BytesSent) on TimeGenerated from startofday(ago(starttime)) to startofday(ago(endtime)) step timeframe by DeviceVendor\n),\n(\nCommonSecurityLog\n| where TimeGenerated between (startofday(ago(starttime))..startofday(ago(endtime)))\n| where isnotempty(DestinationIP) and isnotempty(SourceIP)\n| extend DestinationIpType = iff(DestinationIP matches regex PrivateIPregex,\"private\" ,\"public\" )\n| where DestinationIpType == \"public\"\n| project TimeGenerated, SentBytes, DeviceVendor\n| make-series TotalBytesSent=sum(SentBytes) on TimeGenerated from startofday(ago(starttime)) to startofday(ago(endtime)) step timeframe by DeviceVendor\n)\n);\n//Filter anomolies against TimeSeriesData\nlet TimeSeriesAlerts = materialize(TimeSeriesData\n| extend (anomalies, score, baseline) = series_decompose_anomalies(TotalBytesSent, scorethreshold, -1, 'linefit')\n| mv-expand TotalBytesSent to typeof(double), TimeGenerated to typeof(datetime), anomalies to typeof(double),score to typeof(double), baseline to typeof(long)\n| where anomalies > 0 | extend AnomalyHour = TimeGenerated\n| extend TotalBytesSentinMBperHour = round(((TotalBytesSent / 1024)/1024),2), baselinebytessentperHour = round(((baseline / 1024)/1024),2), score = round(score,2)\n| project DeviceVendor, AnomalyHour, TimeGenerated, TotalBytesSentinMBperHour, baselinebytessentperHour, anomalies, score);\nlet AnomalyHours = materialize(TimeSeriesAlerts  | where TimeGenerated > ago(2d) | project TimeGenerated);\n//Union of all BaseLogs aggregated per hour\nlet BaseLogs = (union isfuzzy=true\n(\nCommonSecurityLog\n| where isnotempty(DestinationIP) and isnotempty(SourceIP)\n| where TimeGenerated > ago(2d)\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| extend DestinationIpType = iff(DestinationIP matches regex PrivateIPregex,\"private\" ,\"public\" )\n| where DestinationIpType == \"public\"\n| extend SentBytesinMB = ((SentBytes / 1024)/1024), ReceivedBytesinMB = ((ReceivedBytes / 1024)/1024)\n| summarize HourlyCount = count(), TimeGeneratedMax=arg_max(TimeGenerated, *), DestinationIPList=make_set(DestinationIP, 100), DestinationPortList = make_set(DestinationPort,100), TotalSentBytesinMB = sum(SentBytesinMB), TotalReceivedBytesinMB = sum(ReceivedBytesinMB) by SourceIP, DeviceVendor, TimeGeneratedHour=bin(TimeGenerated,1h)\n| where TotalSentBytesinMB > bytessentperhourthreshold\n| sort by TimeGeneratedHour asc, TotalSentBytesinMB desc\n| extend Rank=row_number(1, prev(TimeGeneratedHour) != TimeGeneratedHour) // Ranking the dataset per Hourly Partition\n| where Rank < 10  // Selecting Top 10 records with Highest BytesSent in each Hour\n| project DeviceVendor, TimeGeneratedHour, TimeGeneratedMax, SourceIP, DestinationIPList, DestinationPortList, TotalSentBytesinMB, TotalReceivedBytesinMB, Rank\n),\n(\nVMConnection\n| where isnotempty(DestinationIp) and isnotempty(SourceIp)\n| where TimeGenerated > ago(2d)\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| extend SourceIP = SourceIp, DestinationIP = DestinationIp\n| extend DestinationIpType = iff(DestinationIp matches regex PrivateIPregex,\"private\" ,\"public\" )\n| where DestinationIpType == \"public\" | extend DeviceVendor = \"VMConnection\"\n| extend SentBytesinMB = ((BytesSent / 1024)/1024), ReceivedBytesinMB = ((BytesReceived / 1024)/1024)\n| summarize HourlyCount = count(),TimeGeneratedMax=arg_max(TimeGenerated, *), DestinationIPList=make_set(DestinationIP, 100), DestinationPortList = make_set(DestinationPort, 100), TotalSentBytesinMB = sum(SentBytesinMB),TotalReceivedBytesinMB = sum(ReceivedBytesinMB) by SourceIP, DeviceVendor, TimeGeneratedHour=bin(TimeGenerated,1h)\n| where TotalSentBytesinMB > bytessentperhourthreshold\n| sort by TimeGeneratedHour asc, TotalSentBytesinMB desc\n| extend Rank=row_number(1, prev(TimeGeneratedHour) != TimeGeneratedHour) // Ranking the dataset per Hourly Partition\n| where Rank < 10  // Selecting Top 10 records with Highest BytesSent in each Hour\n| project DeviceVendor, TimeGeneratedHour, TimeGeneratedMax, SourceIP, DestinationIPList, DestinationPortList, TotalSentBytesinMB, TotalReceivedBytesinMB, Rank\n)\n);\n// Join against base logs to retrive records associated with the hour of anomoly\nTimeSeriesAlerts\n| where TimeGenerated > ago(2d)\n| join (\n    BaseLogs | extend AnomalyHour = TimeGeneratedHour\n) on DeviceVendor, AnomalyHour | sort by score desc\n| project DeviceVendor, AnomalyHour,TimeGeneratedMax, SourceIP, DestinationIPList, DestinationPortList, TotalSentBytesinMB, TotalReceivedBytesinMB, TotalBytesSentinMBperHour, baselinebytessentperHour, score, anomalies\n| summarize EventCount = count(), StartTimeUtc= min(TimeGeneratedMax), EndTimeUtc= max(TimeGeneratedMax), SourceIPMax= arg_max(SourceIP,*), TotalBytesSentinMB = sum(TotalSentBytesinMB), TotalBytesReceivedinMB = sum(TotalReceivedBytesinMB), SourceIPList = make_set(SourceIP, 100), DestinationIPList = make_set(DestinationIPList, 100) by AnomalyHour,TotalBytesSentinMBperHour, baselinebytessentperHour, score, anomalies\n| project DeviceVendor, AnomalyHour, StartTimeUtc, EndTimeUtc, SourceIPMax, SourceIPList, DestinationIPList, DestinationPortList, TotalBytesSentinMB, TotalBytesReceivedinMB, TotalBytesSentinMBperHour, baselinebytessentperHour, score, anomalies, EventCount\n",
        "queryFrequency": "P1D",
        "queryPeriod": "P14D",
        "severity": "Medium",
        "status": "Available",
        "suppressionDuration": "PT1H",
        "suppressionEnabled": false,
        "tactics": [
          "Exfiltration"
        ],
        "tags": [
          "DEV-0537"
        ],
        "techniques": [
          "T1030"
        ],
        "templateVersion": "1.0.3",
        "triggerOperator": "GreaterThan",
        "triggerThreshold": 1
      },
      "type": "Microsoft.OperationalInsights/workspaces/providers/alertRules"
    }
  ]
}