Detect excessive NXDOMAIN DNS queries - 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 generate an alert when client requests excessive amount of DNS queries to non-existent domains. This helps in identifying possible C2 communications. It utilizes ASIM normalization and is applied to any source that supports the ASIM DNS schema.'

MITRE ATT&CK coverage

Tactic	Techniques
Command & Control	`T1008` Fallback Channels, `T1568` Dynamic Resolution

Rule body kusto

id: 02f23312-1a33-4390-8b80-f7cd4df4dea0
name: Detect excessive NXDOMAIN DNS queries - Anomaly based (ASIM DNS Solution)
description: |
  'This rule makes use of the series decompose anomaly method to generate an alert when client requests excessive amount of DNS queries to non-existent domains. This helps in identifying possible C2 communications. 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:
  - CommandAndControl
relevantTechniques:
  - T1568
  - T1008
query: |
  let threshold = 2.5;
  let min_t = ago(14d);
  let max_t = now();
  let dt = 1d;
  let summarizationexist = (
    union isfuzzy=true 
        (
        DNS_Summarized_Logs_ip_CL
        | where EventTime_t > ago(1d) 
        | project v = int(2)
        ),
        (
        print int(1) 
        | project v = print_0
        )
    | summarize maxv = max(v)
    | extend sumexist = (maxv > 1)
    );
  let allData = union isfuzzy=true
        (
        (datatable(exists: int, sumexist: bool)[1, false]
        | join (summarizationexist) on sumexist)
        | join (
            _Im_Dns(responsecodename='NXDOMAIN', starttime=todatetime(min_t), endtime=max_t)
            | summarize Count=count() by SrcIpAddr, bin(TimeGenerated, 1h)
            | extend EventTime = TimeGenerated, Count = toint(Count), exists=int(1)
            )
            on exists
        | project-away exists, maxv, sum*
        ),
        (
        DNS_Summarized_Logs_ip_CL
        | where EventTime_t > min_t and EventResultDetails_s == 'NXDOMAIN'
        | summarize Count=toint(sum(count__d)) by SrcIpAddr=SrcIpAddr_s, bin(EventTime=EventTime_t, 1h)
        );
  allData
  | make-series EventCount=sum(Count) on EventTime from min_t to max_t step dt by SrcIpAddr
  | extend (anomalies, score, baseline) = series_decompose_anomalies(EventCount, threshold, -1, 'linefit')
  | mv-expand anomalies, score, baseline, EventTime, EventCount
  | extend
    anomalies = toint(anomalies),
    score = toint(score),
    baseline = toint(baseline),
    EventTime = todatetime(EventTime),
    Total = tolong(EventCount)
  | where EventTime >= ago(dt)
  | where score >= threshold * 2
  | join kind=inner(_Im_Dns(responsecodename='NXDOMAIN', starttime=ago(dt), endtime=max_t)
    | summarize DNSQueries = make_set(DnsQuery) 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] Excessive NXDOMAIN DNS Queries has been detected from client IP: '{{SrcIpAddr}}'"
  alertDescriptionFormat: "This client is generating excessive amount of DNS queries for non-existent domains. This can be an indication of possible C2 communications.\n\nBaseline for 'NXDOMAIN' error count for this client: '{{baseline}}'\n\nCurrent 'NXDOMAIN' error count for this client: '{{Total}}'\n\nDNS queries requested by the client include:\n\n'{{DNSQueries}}'"
version: 1.0.2
kind: Scheduled

Stages and Predicates

Parameters

let threshold = 2.5;
let min_t = ago(14d);
let max_t = now();
let dt = 1d;

Let binding: `summarizationexist`

let summarizationexist = (
  union isfuzzy=true 
      (
      DNS_Summarized_Logs_ip_CL
      | where EventTime_t > ago(1d) 
      | project v = int(2)
      ),
      (
      print int(1) 
      | project v = print_0
      )
  | summarize maxv = max(v)
  | extend sumexist = (maxv > 1)
  );

union `isfuzzy=true` (2 sources)

Each leg below queries one source; the rule matches if any leg does. Sources: datatable(exists:, DNS_Summarized_Logs_ip_CL

Leg 1: `datatable(exists:`

(datatable(exists: int, sumexist: bool)[1, false]
      | join (summarizationexist) on sumexist)
      | join (
          _Im_Dns(responsecodename='NXDOMAIN', starttime=todatetime(min_t), endtime=max_t)
          | summarize Count=count() by SrcIpAddr, bin(TimeGenerated, 1h)
          | extend EventTime = TimeGenerated, Count = toint(Count), exists=int(1)
          )
          on exists
      | project-away exists, maxv, sum*

Leg 2: `DNS_Summarized_Logs_ip_CL`

DNS_Summarized_Logs_ip_CL
      | where EventTime_t > min_t and EventResultDetails_s == 'NXDOMAIN'
      | summarize Count=toint(sum(count__d)) by SrcIpAddr=SrcIpAddr_s, bin(EventTime=EventTime_t, 1h)

Applied to the combined result

| make-series EventCount=sum(Count) on EventTime from min_t to max_t step dt by SrcIpAddr | extend (anomalies, score, baseline) = series_decompose_anomalies(EventCount, threshold, -1, 'linefit') | mv-expand anomalies, score, baseline, EventTime, EventCount | extend
  anomalies = toint(anomalies),
  score = toint(score),
  baseline = toint(baseline),
  EventTime = todatetime(EventTime),
  Total = tolong(EventCount) | where EventTime >= ago(dt) | where score >= threshold * 2 | join kind=inner(_Im_Dns(responsecodename='NXDOMAIN', starttime=ago(dt), endtime=max_t)
  | summarize DNSQueries = make_set(DnsQuery) by SrcIpAddr)
  on SrcIpAddr | 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
`EventResultDetails_s`	eq	`NXDOMAIN` transforms: `cased`
`EventTime_t`	gt	`min_t` transforms: `cased`
`score`	ge	`5` transforms: `cased`

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
`EventCount`	`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)

Detect excessive NXDOMAIN DNS queries - Anomaly based (ASIM DNS Solution)

MITRE ATT&CK coverage

Rule body kusto

Stages and Predicates

Parameters

Let binding: `summarizationexist`

union `isfuzzy=true` (2 sources)

Leg 1: `datatable(exists:`

Leg 2: `DNS_Summarized_Logs_ip_CL`

Applied to the combined result

Indicators

Output fields

Keyboard shortcuts

Search operators

Detect excessive NXDOMAIN DNS queries - Anomaly based (ASIM DNS Solution)

MITRE ATT&CK coverage

Rule body kusto

Stages and Predicates

Parameters

Let binding: summarizationexist

union isfuzzy=true (2 sources)

Leg 1: datatable(exists:

Leg 2: DNS_Summarized_Logs_ip_CL

Applied to the combined result

Indicators

Output fields

Let binding: `summarizationexist`

union `isfuzzy=true` (2 sources)

Leg 1: `datatable(exists:`

Leg 2: `DNS_Summarized_Logs_ip_CL`