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

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Id	0fe6bde4-b215-480c-99b4-84a96edcdbd7
Rulename	Rare client observed with high reverse DNS lookup count - Anomaly based (ASIM DNS Solution)
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 normalization and is applied to any source that supports the ASIM DNS schema.
Severity	Medium
Tactics	Reconnaissance
Techniques	T1590
Kind	Scheduled
Query frequency	1d
Query period	14d
Trigger threshold	0
Trigger operator	gt
Source Uri	https://github.com/Azure/Azure-Sentinel/blob/master/Solutions/DNS Essentials/Analytic Rules/RareClientObservedWithHighReverseDNSLookupCountAnomalyBased.yaml
Version	1.0.2
Arm template	0fe6bde4-b215-480c-99b4-84a96edcdbd7.json

KQL

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

YAML

kind: Scheduled
tactics:
- Reconnaissance
triggerThreshold: 0
triggerOperator: gt
version: 1.0.2
tags:
- SchemaVersion: 0.1.6
  Schema: ASimDns
alertDetailsOverride:
  alertDisplayNameFormat: "[Anomaly] Rare client has been observed as making high reverse DNS lookup count  - client IP: '{{SrcIpAddr}}'"
  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}}'    
status: Available
queryFrequency: 1d
id: 0fe6bde4-b215-480c-99b4-84a96edcdbd7
requiredDataConnectors: []
eventGroupingSettings:
  aggregationKind: AlertPerResult
name: Rare client observed with high reverse DNS lookup count - Anomaly based (ASIM DNS Solution)
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.'
customDetails:
  DNSQueries: DNSQueries
  Total: Total
  AnomalyScore: score
  baseline: baseline
relevantTechniques:
- T1590
entityMappings:
- entityType: IP
  fieldMappings:
  - columnName: SrcIpAddr
    identifier: Address
OriginalUri: https://github.com/Azure/Azure-Sentinel/blob/master/Solutions/DNS Essentials/Analytic Rules/RareClientObservedWithHighReverseDNSLookupCountAnomalyBased.yaml
queryPeriod: 14d
severity: Medium
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

ARM