Palo Alto - potential beaconing detected

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Id	f0be259a-34ac-4946-aa15-ca2b115d5feb
Rulename	Palo Alto - potential beaconing detected
Description	Identifies beaconing patterns from Palo Alto Network traffic logs based on recurrent timedelta patterns. The query leverages various KQL functions to calculate time deltas and then compares it with total events observed in a day to find percentage of beaconing. This outbound beaconing pattern to untrusted public networks should be investigated for any malware callbacks or data exfiltration attempts. Reference Blog: http://www.austintaylor.io/detect/beaconing/intrusion/detection/system/command/control/flare/elastic/stack/2017/06/10/detect-beaconing-with-flare-elasticsearch-and-intrusion-detection-systems/ https://techcommunity.microsoft.com/t5/microsoft-sentinel-blog/detect-network-beaconing-via-intra-request-time-delta-patterns/ba-p/779586
Severity	Low
Tactics	CommandAndControl
Techniques	T1071 T1571
Required data connectors	CefAma
Kind	Scheduled
Query frequency	1d
Query period	2d
Trigger threshold	0
Trigger operator	gt
Source Uri	https://github.com/Azure/Azure-Sentinel/blob/master/Solutions/PaloAlto-PAN-OS/Analytic Rules/PaloAlto-NetworkBeaconing.yaml
Version	1.0.6
Arm template	f0be259a-34ac-4946-aa15-ca2b115d5feb.json

KQL

let starttime = 2d;
let endtime = 1d;
let TimeDeltaThreshold = 25;
let TotalEventsThreshold = 30;
let MostFrequentTimeDeltaThreshold = 25;
let PercentBeaconThreshold = 80;
CommonSecurityLog
| where DeviceVendor == "Palo Alto Networks" and Activity == "TRAFFIC"
| where TimeGenerated between (startofday(ago(starttime))..startofday(ago(endtime)))
| where ipv4_is_private(DestinationIP)== false
| project TimeGenerated, DeviceName, SourceUserID, SourceIP, SourcePort, DestinationIP, DestinationPort, ReceivedBytes, SentBytes
| sort by SourceIP asc,TimeGenerated asc, DestinationIP asc, DestinationPort asc
| serialize
| extend nextTimeGenerated = next(TimeGenerated, 1), nextSourceIP = next(SourceIP, 1)
| extend TimeDeltainSeconds = datetime_diff('second',nextTimeGenerated,TimeGenerated)
| where SourceIP == nextSourceIP
//Whitelisting criteria/ threshold criteria
| where TimeDeltainSeconds > TimeDeltaThreshold
| summarize count(), sum(ReceivedBytes), sum(SentBytes)
by TimeDeltainSeconds, bin(TimeGenerated, 1h), DeviceName, SourceUserID, SourceIP, DestinationIP, DestinationPort
| summarize (MostFrequentTimeDeltaCount, MostFrequentTimeDeltainSeconds) = arg_max(count_, TimeDeltainSeconds), TotalEvents=sum(count_), TotalSentBytes = sum(sum_SentBytes), TotalReceivedBytes = sum(sum_ReceivedBytes)
by bin(TimeGenerated, 1h), DeviceName, SourceUserID, SourceIP, DestinationIP, DestinationPort
| where TotalEvents > TotalEventsThreshold and MostFrequentTimeDeltaCount > MostFrequentTimeDeltaThreshold
| extend BeaconPercent = MostFrequentTimeDeltaCount/toreal(TotalEvents) * 100
| where BeaconPercent > PercentBeaconThreshold

YAML

requiredDataConnectors:
- connectorId: CefAma
  dataTypes:
  - CommonSecurityLog
OriginalUri: https://github.com/Azure/Azure-Sentinel/blob/master/Solutions/PaloAlto-PAN-OS/Analytic Rules/PaloAlto-NetworkBeaconing.yaml
triggerThreshold: 0
status: Available
relevantTechniques:
- T1071
- T1571
queryPeriod: 2d
name: Palo Alto - potential beaconing detected
entityMappings:
- entityType: Account
  fieldMappings:
  - columnName: SourceUserID
    identifier: FullName
- entityType: Host
  fieldMappings:
  - columnName: DeviceName
    identifier: FullName
- entityType: IP
  fieldMappings:
  - columnName: DestinationIP
    identifier: Address
queryFrequency: 1d
triggerOperator: gt
kind: Scheduled
description: |
  'Identifies beaconing patterns from Palo Alto Network traffic logs based on recurrent timedelta patterns.
  The query leverages various KQL functions to calculate time deltas and then compares it with total events observed in a day to find percentage of beaconing.
  This outbound beaconing pattern to untrusted public networks should be investigated for any malware callbacks or data exfiltration attempts.
  Reference Blog:
  http://www.austintaylor.io/detect/beaconing/intrusion/detection/system/command/control/flare/elastic/stack/2017/06/10/detect-beaconing-with-flare-elasticsearch-and-intrusion-detection-systems/
  https://techcommunity.microsoft.com/t5/microsoft-sentinel-blog/detect-network-beaconing-via-intra-request-time-delta-patterns/ba-p/779586'  
tactics:
- CommandAndControl
severity: Low
version: 1.0.6
query: |
  let starttime = 2d;
  let endtime = 1d;
  let TimeDeltaThreshold = 25;
  let TotalEventsThreshold = 30;
  let MostFrequentTimeDeltaThreshold = 25;
  let PercentBeaconThreshold = 80;
  CommonSecurityLog
  | where DeviceVendor == "Palo Alto Networks" and Activity == "TRAFFIC"
  | where TimeGenerated between (startofday(ago(starttime))..startofday(ago(endtime)))
  | where ipv4_is_private(DestinationIP)== false
  | project TimeGenerated, DeviceName, SourceUserID, SourceIP, SourcePort, DestinationIP, DestinationPort, ReceivedBytes, SentBytes
  | sort by SourceIP asc,TimeGenerated asc, DestinationIP asc, DestinationPort asc
  | serialize
  | extend nextTimeGenerated = next(TimeGenerated, 1), nextSourceIP = next(SourceIP, 1)
  | extend TimeDeltainSeconds = datetime_diff('second',nextTimeGenerated,TimeGenerated)
  | where SourceIP == nextSourceIP
  //Whitelisting criteria/ threshold criteria
  | where TimeDeltainSeconds > TimeDeltaThreshold
  | summarize count(), sum(ReceivedBytes), sum(SentBytes)
  by TimeDeltainSeconds, bin(TimeGenerated, 1h), DeviceName, SourceUserID, SourceIP, DestinationIP, DestinationPort
  | summarize (MostFrequentTimeDeltaCount, MostFrequentTimeDeltainSeconds) = arg_max(count_, TimeDeltainSeconds), TotalEvents=sum(count_), TotalSentBytes = sum(sum_SentBytes), TotalReceivedBytes = sum(sum_ReceivedBytes)
  by bin(TimeGenerated, 1h), DeviceName, SourceUserID, SourceIP, DestinationIP, DestinationPort
  | where TotalEvents > TotalEventsThreshold and MostFrequentTimeDeltaCount > MostFrequentTimeDeltaThreshold
  | extend BeaconPercent = MostFrequentTimeDeltaCount/toreal(TotalEvents) * 100
  | where BeaconPercent > PercentBeaconThreshold  
id: f0be259a-34ac-4946-aa15-ca2b115d5feb

ARM