Rare client observed with high reverse DNS lookup count - Anomaly based ASIM DNS Solution

let threshold = 2.5;
let SearchDomain = dynamic(["in-addr.arpa"]);
let min_t = ago(14d);
let max_t = now();
let timeframe = 1d;
let DNSEvents=(stime: datetime, etime: datetime) {
  _Im_Dns(starttime=stime, endtime=etime, domain_has_any=SearchDomain)
};
DNSEvents(stime=min_t, etime=max_t)
| make-series QueryCount=dcount(DnsQuery) on TimeGenerated from min_t to max_t step timeframe by SrcIpAddr
| extend (anomalies, score, baseline) = series_decompose_anomalies(QueryCount, threshold, -1, 'linefit')
| mv-expand anomalies, score, baseline, TimeGenerated, QueryCount
| extend
  anomalies = toint(anomalies),
  score = toint(score),
  baseline = toint(baseline),
  EventTime = todatetime(TimeGenerated),
  Total = tolong(QueryCount)
| where EventTime >= ago(timeframe)
| where score >= threshold * 2
| join kind = inner (DNSEvents(stime=ago(timeframe), etime=max_t)
  | summarize DNSQueries=make_set(DnsQuery, 1000) by SrcIpAddr)
  on SrcIpAddr
| project-away SrcIpAddr1

requiredDataConnectors: []
name: Rare client observed with high reverse DNS lookup count - Anomaly based (ASIM DNS Solution)
queryFrequency: 1d
tactics:
- Reconnaissance
severity: Medium
relevantTechniques:
- T1590
status: Available
eventGroupingSettings:
  aggregationKind: AlertPerResult
alertDetailsOverride:
  alertDescriptionFormat: |-
    Client has been identified as making high reverse DNS counts which could be carrying out reconnaissance or discovery activity.

    Reverse DNS lookup count baseline for this client: '{{baseline}}'

    Current reverse DNS lookup count by this client showing as: '{{Total}}'

    DNS queries requested by this client inlcude: '{{DNSQueries}}'    
  alertDisplayNameFormat: "[Anomaly] Rare client has been observed as making high reverse DNS lookup count  - client IP: '{{SrcIpAddr}}'"
id: 0fe6bde4-b215-480c-99b4-84a96edcdbd7
queryPeriod: 14d
entityMappings:
- fieldMappings:
  - identifier: Address
    columnName: SrcIpAddr
  entityType: IP
kind: Scheduled
OriginalUri: https://github.com/Azure/Azure-Sentinel/blob/master/Solutions/DNS Essentials/Analytic Rules/RareClientObservedWithHighReverseDNSLookupCountAnomalyBased.yaml
tags:
- SchemaVersion: 0.1.6
  Schema: ASimDns
query: |
  let threshold = 2.5;
  let SearchDomain = dynamic(["in-addr.arpa"]);
  let min_t = ago(14d);
  let max_t = now();
  let timeframe = 1d;
  let DNSEvents=(stime: datetime, etime: datetime) {
    _Im_Dns(starttime=stime, endtime=etime, domain_has_any=SearchDomain)
  };
  DNSEvents(stime=min_t, etime=max_t)
  | make-series QueryCount=dcount(DnsQuery) on TimeGenerated from min_t to max_t step timeframe by SrcIpAddr
  | extend (anomalies, score, baseline) = series_decompose_anomalies(QueryCount, threshold, -1, 'linefit')
  | mv-expand anomalies, score, baseline, TimeGenerated, QueryCount
  | extend
    anomalies = toint(anomalies),
    score = toint(score),
    baseline = toint(baseline),
    EventTime = todatetime(TimeGenerated),
    Total = tolong(QueryCount)
  | where EventTime >= ago(timeframe)
  | where score >= threshold * 2
  | join kind = inner (DNSEvents(stime=ago(timeframe), etime=max_t)
    | summarize DNSQueries=make_set(DnsQuery, 1000) by SrcIpAddr)
    on SrcIpAddr
  | project-away SrcIpAddr1  
customDetails:
  AnomalyScore: score
  DNSQueries: DNSQueries
  baseline: baseline
  Total: Total
triggerOperator: gt
description: |
    'This rule makes use of the series decompose anomaly method to identify clients with high reverse DNS counts. This helps in detecting the possible initial phases of an attack, like discovery and reconnaissance. It utilizes [ASIM](https://aka.ms/AboutASIM) normalization and is applied to any source that supports the ASIM DNS schema.'
triggerThreshold: 0
version: 1.0.2

{
  "$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/0fe6bde4-b215-480c-99b4-84a96edcdbd7')]",
      "kind": "Scheduled",
      "name": "[concat(parameters('workspace'),'/Microsoft.SecurityInsights/0fe6bde4-b215-480c-99b4-84a96edcdbd7')]",
      "properties": {
        "alertDetailsOverride": {
          "alertDescriptionFormat": "Client has been identified as making high reverse DNS counts which could be carrying out reconnaissance or discovery activity.\n\nReverse DNS lookup count baseline for this client: '{{baseline}}'\n\nCurrent reverse DNS lookup count by this client showing as: '{{Total}}'\n\nDNS queries requested by this client inlcude: '{{DNSQueries}}'",
          "alertDisplayNameFormat": "[Anomaly] Rare client has been observed as making high reverse DNS lookup count  - client IP: '{{SrcIpAddr}}'"
        },
        "alertRuleTemplateName": "0fe6bde4-b215-480c-99b4-84a96edcdbd7",
        "customDetails": {
          "AnomalyScore": "score",
          "baseline": "baseline",
          "DNSQueries": "DNSQueries",
          "Total": "Total"
        },
        "description": "'This rule makes use of the series decompose anomaly method to identify clients with high reverse DNS counts. This helps in detecting the possible initial phases of an attack, like discovery and reconnaissance. It utilizes [ASIM](https://aka.ms/AboutASIM) normalization and is applied to any source that supports the ASIM DNS schema.'\n",
        "displayName": "Rare client observed with high reverse DNS lookup count - Anomaly based (ASIM DNS Solution)",
        "enabled": true,
        "entityMappings": [
          {
            "entityType": "IP",
            "fieldMappings": [
              {
                "columnName": "SrcIpAddr",
                "identifier": "Address"
              }
            ]
          }
        ],
        "eventGroupingSettings": {
          "aggregationKind": "AlertPerResult"
        },
        "OriginalUri": "https://github.com/Azure/Azure-Sentinel/blob/master/Solutions/DNS Essentials/Analytic Rules/RareClientObservedWithHighReverseDNSLookupCountAnomalyBased.yaml",
        "query": "let threshold = 2.5;\nlet SearchDomain = dynamic([\"in-addr.arpa\"]);\nlet min_t = ago(14d);\nlet max_t = now();\nlet timeframe = 1d;\nlet DNSEvents=(stime: datetime, etime: datetime) {\n  _Im_Dns(starttime=stime, endtime=etime, domain_has_any=SearchDomain)\n};\nDNSEvents(stime=min_t, etime=max_t)\n| make-series QueryCount=dcount(DnsQuery) on TimeGenerated from min_t to max_t step timeframe by SrcIpAddr\n| extend (anomalies, score, baseline) = series_decompose_anomalies(QueryCount, threshold, -1, 'linefit')\n| mv-expand anomalies, score, baseline, TimeGenerated, QueryCount\n| extend\n  anomalies = toint(anomalies),\n  score = toint(score),\n  baseline = toint(baseline),\n  EventTime = todatetime(TimeGenerated),\n  Total = tolong(QueryCount)\n| where EventTime >= ago(timeframe)\n| where score >= threshold * 2\n| join kind = inner (DNSEvents(stime=ago(timeframe), etime=max_t)\n  | summarize DNSQueries=make_set(DnsQuery, 1000) by SrcIpAddr)\n  on SrcIpAddr\n| project-away SrcIpAddr1\n",
        "queryFrequency": "P1D",
        "queryPeriod": "P14D",
        "severity": "Medium",
        "status": "Available",
        "subTechniques": [],
        "suppressionDuration": "PT1H",
        "suppressionEnabled": false,
        "tactics": [
          "Reconnaissance"
        ],
        "tags": [
          {
            "Schema": "ASimDns",
            "SchemaVersion": "0.1.6"
          }
        ],
        "techniques": [
          "T1590"
        ],
        "templateVersion": "1.0.2",
        "triggerOperator": "GreaterThan",
        "triggerThreshold": 0
      },
      "type": "Microsoft.OperationalInsights/workspaces/providers/alertRules"
    }
  ]
}