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

Status: available
Severity: medium
Time window: 14d
Group by: SrcIpAddr
Source: github.com/Azure/Azure-Sentinel

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.

MITRE ATT&CK coverage

Tactic	Techniques
Reconnaissance	`T1590` Gather Victim Network Information

Event coverage

Provider	Event	Title
Sysmon	Event ID 22	DNSEvent (DNS query)

Rule body kusto

id: 0fe6bde4-b215-480c-99b4-84a96edcdbd7
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.'
severity: Medium
status: Available 
tags:
  - Schema: ASimDns
    SchemaVersion: 0.1.6
requiredDataConnectors: []
queryFrequency: 1d
queryPeriod: 14d
triggerOperator: gt
triggerThreshold: 0
tactics:
  - Reconnaissance
relevantTechniques:
  - T1590
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
entityMappings:
  - entityType: IP
    fieldMappings:
      - identifier: Address
        columnName: SrcIpAddr
eventGroupingSettings:
  aggregationKind: AlertPerResult
customDetails:
  DNSQueries: DNSQueries
  AnomalyScore: score
  baseline: baseline
  Total: Total
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.\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}}'"
version: 1.0.2
kind: Scheduled

Stages and Predicates

Parameters

let threshold = 2.5;
let SearchDomain = dynamic(["in-addr.arpa"]);
let min_t = ago(14d);
let max_t = now();
let timeframe = 1d;

Let binding: `DNSEvents`

let DNSEvents = (stime: datetime, etime: datetime) {
  _Im_Dns(starttime=stime, endtime=etime, domain_has_any=SearchDomain)
};

Derived from SearchDomain.

Stage 1: `source`

DNSEvents(stime=min_t, etime=max_t)

The stages below score time-series anomalies (make-series, series_decompose_anomalies).

Stage 2: `summarize`

| make-series QueryCount=dcount(DnsQuery) on TimeGenerated from min_t to max_t step timeframe by SrcIpAddr

Stage 3: `extend`

| extend (anomalies, score, baseline) = series_decompose_anomalies(QueryCount, threshold, -1, 'linefit')

Stage 4: `mv-expand`

| mv-expand anomalies, score, baseline, TimeGenerated, QueryCount

Stage 5: `extend`

| extend
  anomalies = toint(anomalies),
  score = toint(score),
  baseline = toint(baseline),
  EventTime = todatetime(TimeGenerated),
  Total = tolong(QueryCount)

Stage 6: `where`

| where EventTime >= ago(timeframe)

Stage 7: `where`

| where score >= threshold * 2

Stage 8: `join`

| join kind = inner (DNSEvents(stime=ago(timeframe), etime=max_t)
  | summarize DNSQueries=make_set(DnsQuery, 1000) by SrcIpAddr)
  on SrcIpAddr

Stage 9: `project-away`

| project-away SrcIpAddr1

Indicators

Each row is a field, operator, and value that the rule matches. The corpus column counts how many other rules in the catalog look for the same combination: high numbers point to widely-used, community-vetted indicators. Blank or 1 shows that the indicator is specific to this rule.

Field	Kind	Values
`score`	ge	`5` transforms: `cased` corpus 6 (kusto 6)

Output fields

Fields the rule emits when it matches. Chronicle authors list these in the outcome block; they appear on the detection and $risk_score drives alerting. Sentinel / Defender XDR rules build them up through project / summarize / extend stages. Sentinel maps these into alert fields via entityMappings and customDetails; Defender XDR custom detections surface them as alert fields directly.

Field	Source
`QueryCount`	`summarize`
`SrcIpAddr`	`summarize`
`anomalies`	`extend`
`baseline`	`extend`
`score`	`extend`
`EventTime`	`extend`
`Total`	`extend`

`j` / `k`	Scroll down / up
`d` / `u`	Half-page down / up
`gg` / `G`	Top / bottom
`h` / `l`	History back / forward
`f`	Follow link (`Shift` = new tab)
`/`	Focus search
`?`	Toggle this help
`↑` / `↓`	Navigate search results
`Enter`	Open highlighted result
`Esc`	Close results / dialog

`type:`	`events` / `rules` / `providers`
`vendor:`	`sigma` / `elastic` / `splunk` / `kusto` / `chronicle` (vendor name alone also works: `sigma:`, `kql:`, `secops:`…)
`tactic:`	TA-id, slug, or name: `credential_access`, `TA0006`
`technique:`	technique or sub-technique ID: `T1003`, `T1003.001` (alias `tech:`)
`severity:`	`critical` / `high` / `medium` / `low` / `informational` (alias `sev:`)
`risk_score`	Numeric comparison on the Elastic risk score (0 to 100): `risk_score>50`, `risk_score<=20`, `risk_score=99` (alias `risk`; Elastic rules only)
`stages:`	Rules with exactly N pipeline stages
`correlation:`	`single_event` / `sequence` / `alternatives` / `alternatives_cross_log` / `all_required` / `correlated`
`with:`	Co-occurrence event-id; stacks (`with:4624 with:4769`) to require all, while a comma list in one occurrence (`with:4624,4769`) is an either-or group. Implies multi-event
`like:`	Structural neighbors of a rule slug (equivalents + subsumption stricter / broader): `like:comsvcs_lsass_memory_dump-splunk-sysmon`
`groupby:`	Entity-grouping substring match against `group_by_keys`: `groupby:user`, `groupby:host`
`uses:`	Rules whose predicate tree touches the field (any kind, any value): `uses:CommandLine`
`excludes:`	Rules with top-level `not()` clauses on the field (FP whitelists): `excludes:ParentImage`
`field:` / `value:`	Predicate search; narrows rule cards to those with a matching leaf and drives the indicator tier. Unquoted = substring, wildcards allowed (`value:mimikatz`)
`indicator:`	Shorthand for `field:F value:V`: `indicator:Image=*\powershell.exe`
`kind:`	Filter by predicate kind. Narrows rule cards to those carrying a matching predicate leaf (`vendor:elastic kind:cidr_match`) and drives the indicator tier: `contains` / `starts_with` / `ends_with` / `regex` / `cidr` / `eq` / `in` … (operator aliases `op:`/`match:`)
`has:` / `no:`	`sample`, `field`, `notes`, `refs`, `trace`, `thirdparty`, `rule`, `pattern`, `timewindow`, `threshold`, `newterms`, `sigma`/`elastic`/`splunk`/`kusto`/`chronicle`
`-op:val`	Exclude matches; works on most operators but not `type:`/`like:`/`has:`/`no:` (use `no:<flag>` to exclude a rule flag): `tactic:execution -vendor:splunk`. Standalone `-kind:`/`-field:`/`-value:` drop every rule carrying a matching predicate leaf (`type:rules -kind:is_null`)
`field:"…"` / `value:"…"`	Quoted value = anchored exact match (also allows spaces): `value:"net user"`
`a,b`	Comma = OR inside one operator (`vendor:sigma,elastic`, `severity:high,critical`); repeating a facet merges the same way. `field:`/`value:` never split (literal commas)
`vendors:` / `stage:`	Singular and plural spellings fold to the canonical operator and value: `tactics:` = `tactic:`, `type:event` = `type:events`, `correlation:sequences` = `correlation:sequence`, `has:thresholds` = `has:threshold`
`"quoted phrase"`	Exact-match a multi-word phrase (free text)

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

MITRE ATT&CK coverage

Event coverage

Rule body kusto

Stages and Predicates

Parameters

Let binding: `DNSEvents`

Stage 1: `source`

Stage 2: `summarize`

Stage 3: `extend`

Stage 4: `mv-expand`

Stage 5: `extend`

Stage 6: `where`

Stage 7: `where`

Stage 8: `join`

Stage 9: `project-away`

Indicators

Output fields

Keyboard shortcuts

Search operators

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

MITRE ATT&CK coverage

Event coverage

Rule body kusto

Stages and Predicates

Parameters

Let binding: DNSEvents

Stage 1: source

Stage 2: summarize

Stage 3: extend

Stage 4: mv-expand

Stage 5: extend

Stage 6: where

Stage 7: where

Stage 8: join

Stage 9: project-away

Indicators

Output fields

Let binding: `DNSEvents`

Stage 1: `source`

Stage 2: `summarize`

Stage 3: `extend`

Stage 4: `mv-expand`

Stage 5: `extend`

Stage 6: `where`

Stage 7: `where`

Stage 8: `join`

Stage 9: `project-away`