# Fortinet - Beacon pattern detected

Id | 3255ec41-6bd6-4f35-84b1-c032b18bbfcb |

Rulename | Fortinet - Beacon pattern detected |

Description | Identifies patterns in the time deltas of contacts between internal and external IPs in Fortinet network data that are consistent with beaconing. Accounts for randomness (jitter) and seasonality such as working hours that may have been introduced into the beacon pattern. The lookback is set to 1d, the minimum granularity in time deltas is set to 60 seconds and the minimum number of beacons required to emit a detection is set to 4. Increase the lookback period to capture beacons with larger periodicities. The jitter tolerance is set to 0.2 - This means we account for an overall 20% deviation from the infered beacon periodicity. Seasonality is dealt with automatically using series_outliers. Note: In large environments it may be necessary to reduce the lookback period to get fast query times. |

Severity | Low |

Tactics | CommandAndControl |

Techniques | T1071 T1571 |

Required data connectors | Fortinet |

Kind | Scheduled |

Query frequency | 1d |

Query period | 1d |

Trigger threshold | 0 |

Trigger operator | gt |

Source Uri | https://github.com/Azure/Azure-Sentinel/blob/master/Detections/CommonSecurityLog/Fortinet-NetworkBeaconPattern.yaml |

Version | 1.0.3 |

Arm template | 3255ec41-6bd6-4f35-84b1-c032b18bbfcb.json |

```
let starttime = 1d;
let TimeDeltaThresholdInSeconds = 60; // we ignore beacons diffs that fall below this threshold
let TotalBeaconsThreshold = 4; // minimum number of beacons required in a session to surface a row
let JitterTolerance = 0.2; // tolerance to jitter, e.g. - 0.2 = 20% jitter is tolerated either side of the periodicity
CommonSecurityLog
| where DeviceVendor == "Fortinet"
| where TimeGenerated > ago(starttime)
// eliminate bad data
| where isnotempty(SourceIP) and isnotempty(DestinationIP) and SourceIP != "0.0.0.0"
// filter out deny, close, rst and SNMP to reduce data volume
| where DeviceAction !in ("close", "client-rst", "server-rst", "deny") and DestinationPort != 161
// map input fields
| project TimeGenerated , SourceIP, DestinationIP, DestinationPort, ReceivedBytes, SentBytes, DeviceAction
// where destination IPs are public
| where ipv4_is_private(DestinationIP) == false
// sort into source->destination 'sessions'
| sort by SourceIP asc, DestinationIP asc, DestinationPort asc, TimeGenerated asc
| serialize
// time diff the contact times between source and destination to get a list of deltas
| extend nextTimeGenerated = next(TimeGenerated, 1), nextSourceIP = next(SourceIP, 1), nextDestIP = next(DestinationIP, 1), nextDestPort = next(DestinationPort, 1)
| extend TimeDeltainSeconds = datetime_diff("second",nextTimeGenerated,TimeGenerated)
| where SourceIP == nextSourceIP and DestinationIP == nextDestIP and DestinationPort == nextDestPort
// remove small time deltas below the set threshold
| where TimeDeltainSeconds > TimeDeltaThresholdInSeconds
// summarize the deltas by source->destination
| summarize count(), StartTime=min(TimeGenerated), EndTime=max(TimeGenerated), sum(ReceivedBytes), sum(SentBytes), makelist(TimeDeltainSeconds), makeset(DeviceAction) by SourceIP, DestinationIP, DestinationPort
// get some statistical properties of the delta distribution and smooth any outliers (e.g. laptop shut overnight, working hours)
| extend series_stats(list_TimeDeltainSeconds), outliers=series_outliers(list_TimeDeltainSeconds)
// expand the deltas and the outliers
| mvexpand list_TimeDeltainSeconds to typeof(double), outliers to typeof(double)
// replace outliers with the average of the distribution
| extend list_TimeDeltainSeconds_normalized=iff(outliers > 1.5 or outliers < -1.5, series_stats_list_TimeDeltainSeconds_avg , list_TimeDeltainSeconds)
// summarize with the smoothed distribution
| summarize BeaconCount=count(), makelist(list_TimeDeltainSeconds), list_TimeDeltainSeconds_normalized=makelist(list_TimeDeltainSeconds_normalized), makeset(set_DeviceAction) by StartTime, EndTime, SourceIP, DestinationIP, DestinationPort, sum_ReceivedBytes, sum_SentBytes
// get stats on the smoothed distribution
| extend series_stats(list_TimeDeltainSeconds_normalized)
// match jitter tolerance on smoothed distrib
| extend MaxJitter = (series_stats_list_TimeDeltainSeconds_normalized_avg*JitterTolerance)
| where series_stats_list_TimeDeltainSeconds_normalized_stdev < MaxJitter
// where the minimum beacon threshold is satisfied and there was some data transfer
| where BeaconCount > TotalBeaconsThreshold and (sum_SentBytes > 0 or sum_ReceivedBytes > 0)
// final projection
| project StartTime, EndTime, SourceIP, DestinationIP, DestinationPort, BeaconCount, TimeDeltasInSeconds=list_list_TimeDeltainSeconds, Periodicity=series_stats_list_TimeDeltainSeconds_normalized_avg, ReceivedBytes=sum_ReceivedBytes, SentBytes=sum_SentBytes, Actions=set_set_DeviceAction
// where periodicity is order of magnitude larger than time delta threshold (eliminates FPs whose periodicity is close to the values we ignored)
| where Periodicity >= (10*TimeDeltaThresholdInSeconds)
| extend timestamp = StartTime, IPCustomEntity = DestinationIP
```

```
OriginalUri: https://github.com/Azure/Azure-Sentinel/blob/master/Detections/CommonSecurityLog/Fortinet-NetworkBeaconPattern.yaml
entityMappings:
- fieldMappings:
- identifier: Address
columnName: IPCustomEntity
entityType: IP
kind: Scheduled
query: |
let starttime = 1d;
let TimeDeltaThresholdInSeconds = 60; // we ignore beacons diffs that fall below this threshold
let TotalBeaconsThreshold = 4; // minimum number of beacons required in a session to surface a row
let JitterTolerance = 0.2; // tolerance to jitter, e.g. - 0.2 = 20% jitter is tolerated either side of the periodicity
CommonSecurityLog
| where DeviceVendor == "Fortinet"
| where TimeGenerated > ago(starttime)
// eliminate bad data
| where isnotempty(SourceIP) and isnotempty(DestinationIP) and SourceIP != "0.0.0.0"
// filter out deny, close, rst and SNMP to reduce data volume
| where DeviceAction !in ("close", "client-rst", "server-rst", "deny") and DestinationPort != 161
// map input fields
| project TimeGenerated , SourceIP, DestinationIP, DestinationPort, ReceivedBytes, SentBytes, DeviceAction
// where destination IPs are public
| where ipv4_is_private(DestinationIP) == false
// sort into source->destination 'sessions'
| sort by SourceIP asc, DestinationIP asc, DestinationPort asc, TimeGenerated asc
| serialize
// time diff the contact times between source and destination to get a list of deltas
| extend nextTimeGenerated = next(TimeGenerated, 1), nextSourceIP = next(SourceIP, 1), nextDestIP = next(DestinationIP, 1), nextDestPort = next(DestinationPort, 1)
| extend TimeDeltainSeconds = datetime_diff("second",nextTimeGenerated,TimeGenerated)
| where SourceIP == nextSourceIP and DestinationIP == nextDestIP and DestinationPort == nextDestPort
// remove small time deltas below the set threshold
| where TimeDeltainSeconds > TimeDeltaThresholdInSeconds
// summarize the deltas by source->destination
| summarize count(), StartTime=min(TimeGenerated), EndTime=max(TimeGenerated), sum(ReceivedBytes), sum(SentBytes), makelist(TimeDeltainSeconds), makeset(DeviceAction) by SourceIP, DestinationIP, DestinationPort
// get some statistical properties of the delta distribution and smooth any outliers (e.g. laptop shut overnight, working hours)
| extend series_stats(list_TimeDeltainSeconds), outliers=series_outliers(list_TimeDeltainSeconds)
// expand the deltas and the outliers
| mvexpand list_TimeDeltainSeconds to typeof(double), outliers to typeof(double)
// replace outliers with the average of the distribution
| extend list_TimeDeltainSeconds_normalized=iff(outliers > 1.5 or outliers < -1.5, series_stats_list_TimeDeltainSeconds_avg , list_TimeDeltainSeconds)
// summarize with the smoothed distribution
| summarize BeaconCount=count(), makelist(list_TimeDeltainSeconds), list_TimeDeltainSeconds_normalized=makelist(list_TimeDeltainSeconds_normalized), makeset(set_DeviceAction) by StartTime, EndTime, SourceIP, DestinationIP, DestinationPort, sum_ReceivedBytes, sum_SentBytes
// get stats on the smoothed distribution
| extend series_stats(list_TimeDeltainSeconds_normalized)
// match jitter tolerance on smoothed distrib
| extend MaxJitter = (series_stats_list_TimeDeltainSeconds_normalized_avg*JitterTolerance)
| where series_stats_list_TimeDeltainSeconds_normalized_stdev < MaxJitter
// where the minimum beacon threshold is satisfied and there was some data transfer
| where BeaconCount > TotalBeaconsThreshold and (sum_SentBytes > 0 or sum_ReceivedBytes > 0)
// final projection
| project StartTime, EndTime, SourceIP, DestinationIP, DestinationPort, BeaconCount, TimeDeltasInSeconds=list_list_TimeDeltainSeconds, Periodicity=series_stats_list_TimeDeltainSeconds_normalized_avg, ReceivedBytes=sum_ReceivedBytes, SentBytes=sum_SentBytes, Actions=set_set_DeviceAction
// where periodicity is order of magnitude larger than time delta threshold (eliminates FPs whose periodicity is close to the values we ignored)
| where Periodicity >= (10*TimeDeltaThresholdInSeconds)
| extend timestamp = StartTime, IPCustomEntity = DestinationIP
triggerOperator: gt
version: 1.0.3
queryPeriod: 1d
name: Fortinet - Beacon pattern detected
metadata:
categories:
domains:
- Security - Network
support:
tier: Community
source:
kind: Community
author:
name: robMSFT
severity: Low
relevantTechniques:
- T1071
- T1571
requiredDataConnectors:
- dataTypes:
- CommonSecurityLog
connectorId: Fortinet
triggerThreshold: 0
tactics:
- CommandAndControl
queryFrequency: 1d
id: 3255ec41-6bd6-4f35-84b1-c032b18bbfcb
description: |
'Identifies patterns in the time deltas of contacts between internal and external IPs in Fortinet network data that are consistent with beaconing.
Accounts for randomness (jitter) and seasonality such as working hours that may have been introduced into the beacon pattern.
The lookback is set to 1d, the minimum granularity in time deltas is set to 60 seconds and the minimum number of beacons required to emit a
detection is set to 4.
Increase the lookback period to capture beacons with larger periodicities.
The jitter tolerance is set to 0.2 - This means we account for an overall 20% deviation from the infered beacon periodicity. Seasonality is dealt with
automatically using series_outliers.
Note: In large environments it may be necessary to reduce the lookback period to get fast query times.'
```

```
{
"$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/3255ec41-6bd6-4f35-84b1-c032b18bbfcb')]",
"kind": "Scheduled",
"name": "[concat(parameters('workspace'),'/Microsoft.SecurityInsights/3255ec41-6bd6-4f35-84b1-c032b18bbfcb')]",
"properties": {
"alertRuleTemplateName": "3255ec41-6bd6-4f35-84b1-c032b18bbfcb",
"customDetails": null,
"description": "'Identifies patterns in the time deltas of contacts between internal and external IPs in Fortinet network data that are consistent with beaconing.\n Accounts for randomness (jitter) and seasonality such as working hours that may have been introduced into the beacon pattern.\n The lookback is set to 1d, the minimum granularity in time deltas is set to 60 seconds and the minimum number of beacons required to emit a\n detection is set to 4.\n Increase the lookback period to capture beacons with larger periodicities.\n The jitter tolerance is set to 0.2 - This means we account for an overall 20% deviation from the infered beacon periodicity. Seasonality is dealt with\n automatically using series_outliers.\n Note: In large environments it may be necessary to reduce the lookback period to get fast query times.'\n",
"displayName": "Fortinet - Beacon pattern detected",
"enabled": true,
"entityMappings": [
{
"entityType": "IP",
"fieldMappings": [
{
"columnName": "IPCustomEntity",
"identifier": "Address"
}
]
}
],
"OriginalUri": "https://github.com/Azure/Azure-Sentinel/blob/master/Detections/CommonSecurityLog/Fortinet-NetworkBeaconPattern.yaml",
"query": "let starttime = 1d;\nlet TimeDeltaThresholdInSeconds = 60; // we ignore beacons diffs that fall below this threshold\nlet TotalBeaconsThreshold = 4; // minimum number of beacons required in a session to surface a row\nlet JitterTolerance = 0.2; // tolerance to jitter, e.g. - 0.2 = 20% jitter is tolerated either side of the periodicity\nCommonSecurityLog\n| where DeviceVendor == \"Fortinet\"\n| where TimeGenerated > ago(starttime)\n// eliminate bad data\n| where isnotempty(SourceIP) and isnotempty(DestinationIP) and SourceIP != \"0.0.0.0\"\n// filter out deny, close, rst and SNMP to reduce data volume\n| where DeviceAction !in (\"close\", \"client-rst\", \"server-rst\", \"deny\") and DestinationPort != 161\n// map input fields\n| project TimeGenerated , SourceIP, DestinationIP, DestinationPort, ReceivedBytes, SentBytes, DeviceAction\n// where destination IPs are public\n| where ipv4_is_private(DestinationIP) == false\n// sort into source->destination 'sessions'\n| sort by SourceIP asc, DestinationIP asc, DestinationPort asc, TimeGenerated asc\n| serialize\n// time diff the contact times between source and destination to get a list of deltas\n| extend nextTimeGenerated = next(TimeGenerated, 1), nextSourceIP = next(SourceIP, 1), nextDestIP = next(DestinationIP, 1), nextDestPort = next(DestinationPort, 1)\n| extend TimeDeltainSeconds = datetime_diff(\"second\",nextTimeGenerated,TimeGenerated)\n| where SourceIP == nextSourceIP and DestinationIP == nextDestIP and DestinationPort == nextDestPort\n// remove small time deltas below the set threshold\n| where TimeDeltainSeconds > TimeDeltaThresholdInSeconds\n// summarize the deltas by source->destination\n| summarize count(), StartTime=min(TimeGenerated), EndTime=max(TimeGenerated), sum(ReceivedBytes), sum(SentBytes), makelist(TimeDeltainSeconds), makeset(DeviceAction) by SourceIP, DestinationIP, DestinationPort\n// get some statistical properties of the delta distribution and smooth any outliers (e.g. laptop shut overnight, working hours)\n| extend series_stats(list_TimeDeltainSeconds), outliers=series_outliers(list_TimeDeltainSeconds)\n// expand the deltas and the outliers\n| mvexpand list_TimeDeltainSeconds to typeof(double), outliers to typeof(double)\n// replace outliers with the average of the distribution\n| extend list_TimeDeltainSeconds_normalized=iff(outliers > 1.5 or outliers < -1.5, series_stats_list_TimeDeltainSeconds_avg , list_TimeDeltainSeconds)\n// summarize with the smoothed distribution\n| summarize BeaconCount=count(), makelist(list_TimeDeltainSeconds), list_TimeDeltainSeconds_normalized=makelist(list_TimeDeltainSeconds_normalized), makeset(set_DeviceAction) by StartTime, EndTime, SourceIP, DestinationIP, DestinationPort, sum_ReceivedBytes, sum_SentBytes\n// get stats on the smoothed distribution\n| extend series_stats(list_TimeDeltainSeconds_normalized)\n// match jitter tolerance on smoothed distrib\n| extend MaxJitter = (series_stats_list_TimeDeltainSeconds_normalized_avg*JitterTolerance)\n| where series_stats_list_TimeDeltainSeconds_normalized_stdev < MaxJitter\n// where the minimum beacon threshold is satisfied and there was some data transfer\n| where BeaconCount > TotalBeaconsThreshold and (sum_SentBytes > 0 or sum_ReceivedBytes > 0)\n// final projection\n| project StartTime, EndTime, SourceIP, DestinationIP, DestinationPort, BeaconCount, TimeDeltasInSeconds=list_list_TimeDeltainSeconds, Periodicity=series_stats_list_TimeDeltainSeconds_normalized_avg, ReceivedBytes=sum_ReceivedBytes, SentBytes=sum_SentBytes, Actions=set_set_DeviceAction\n// where periodicity is order of magnitude larger than time delta threshold (eliminates FPs whose periodicity is close to the values we ignored)\n| where Periodicity >= (10*TimeDeltaThresholdInSeconds)\n| extend timestamp = StartTime, IPCustomEntity = DestinationIP\n",
"queryFrequency": "P1D",
"queryPeriod": "P1D",
"severity": "Low",
"suppressionDuration": "PT1H",
"suppressionEnabled": false,
"tactics": [
"CommandAndControl"
],
"techniques": [
"T1071",
"T1571"
],
"templateVersion": "1.0.3",
"triggerOperator": "GreaterThan",
"triggerThreshold": 0
},
"type": "Microsoft.OperationalInsights/workspaces/providers/alertRules"
}
]
}
```