Detect DNS queries reporting multiple errors from different clients - Anomaly Based (ASIM DNS Solution)

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

'This rule makes use of the series decompose anomaly method to generate an alert when multiple clients report errors for the same DNS query. This rule monitors DNS traffic over a period of 14 days to detect possible similar C2 communication originating from different clients. 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, `T1573` Encrypted Channel

Rule body kusto

id: cf687598-5a2c-46f8-81c8-06b15ed489b1
name: Detect DNS queries reporting multiple errors from different clients - Anomaly Based (ASIM DNS Solution)
description: |
  'This rule makes use of the series decompose anomaly method to generate an alert when multiple clients report errors for the same DNS query. This rule monitors DNS traffic over a period of 14 days to detect possible similar C2 communication originating from different clients. 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
  - T1573
  - T1008
query: |
  let threshold = 2.5;
  let min_t = ago(14d);
  let max_t = now();
  let dt = 1d;
  let Errors = dynamic(['NXDOMAIN', 'SERVFAIL', 'REFUSED']);
  // calculate avg. eps(events per second)
  let eps = materialize (_Im_Dns | project TimeGenerated | where TimeGenerated > ago(5m) | count | extend Count = Count/300);
  let maxSummarizedTime = toscalar (
    union isfuzzy=true 
        (
            DNS_Summarized_Logs_ip_CL 
                | where EventTime_t >= min_t
                | summarize max_TimeGenerated=max(EventTime_t)
                | extend max_TimeGenerated = datetime_add('hour',1,max_TimeGenerated)
        ),
        (
            print(min_t)
            | project max_TimeGenerated = print_0
        )
      | summarize maxTimeGenerated = max(max_TimeGenerated) 
    );
    let summarizationexist = materialize(
              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] | where toscalar(eps) > 1000 | join (summarizationexist) on sumexist) | join (
    _Im_Dns(starttime=todatetime(ago(2d)), endtime=now())
    | where TimeGenerated > maxSummarizedTime and EventResultDetails in (Errors)
    | summarize Count=count() by SrcIpAddr, DnsQuery, bin(TimeGenerated,1h)
    | extend EventTime = TimeGenerated, Count = toint(Count), exists=int(1)
    ) on exists
    | project-away exists, maxv, sum*
  ),
  (
    (datatable(exists:int, sumexist:bool)[1,false] | where toscalar(eps) between (501 .. 1000) | join (summarizationexist) on sumexist) | join (
    _Im_Dns(starttime=todatetime(ago(3d)), endtime=now())
    | where TimeGenerated > maxSummarizedTime and EventResultDetails in (Errors)
    | summarize Count=count() by SrcIpAddr, DnsQuery, bin(TimeGenerated,1h)
    | extend EventTime = TimeGenerated, Count = toint(Count), exists=int(1)
    ) on exists
    | project-away exists, maxv, sum*
  ),
  (
    (datatable(exists:int, sumexist:bool)[1,false] | where toscalar(eps) <= 500 | join (summarizationexist) on sumexist) | join (
    _Im_Dns(starttime=todatetime(ago(4d)), endtime=now())
    | where TimeGenerated > maxSummarizedTime and EventResultDetails in (Errors)
    | summarize Count=count() by SrcIpAddr, DnsQuery, 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 in (Errors)
        | project-rename SrcIpAddr=SrcIpAddr_s, DnsQuery=DnsQuery_s, Count=count__d, EventTime=EventTime_t
        | extend Count = toint(Count) 
  );
  allData
  | make-series TotalIPCount=dcount(SrcIpAddr) on EventTime from min_t to max_t step dt by DnsQuery
  | extend (anomalies, score, baseline) = series_decompose_anomalies(TotalIPCount, threshold, -1, 'linefit')
  | mv-expand anomalies, score, baseline, EventTime, TotalIPCount
  | extend
    anomalies = toint(anomalies),
    score = toint(score),
    baseline = toint(baseline),
    EventTime = todatetime(EventTime),
    TotalIPs = tolong(TotalIPCount)
  | where EventTime >= ago(dt)
  | where score >= threshold * 2
  | join kind=inner(allData | where TimeGenerated>ago(dt) | summarize SrcIps = make_set(SrcIpAddr,1000) by DnsQuery) on DnsQuery
  | project-away DnsQuery1
entityMappings:
  - entityType: DNS
    fieldMappings:
      - identifier: DomainName
        columnName: DnsQuery
eventGroupingSettings:
  aggregationKind: AlertPerResult
customDetails:
  SrcIps: SrcIps
  AnomalyScore: score
  baseline: baseline
  TotalIPs: TotalIPs
alertDetailsOverride:
  alertDisplayNameFormat: "[Anomaly] Multiple errors for the same DNS query has been detected - '{{DnsQuery}}'"
  alertDescriptionFormat: "Multiple errors were detected on different clients for the same DNS query. These unsuccessful responses can be an indication of C2 communication. \n\nBaseline for total clients reporting errors for this DNS query: '{{baseline}}'\n\nCurrent count of clients reporting errors for this DNS query: '{{TotalIPs}}'\n\nClients requesting this DNS query include:\n'{{SrcIps}}'"
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 Errors = dynamic(['NXDOMAIN', 'SERVFAIL', 'REFUSED']);

Let binding: `eps`

let eps = materialize (_Im_Dns | project TimeGenerated | where TimeGenerated > ago(5m) | count | extend Count = Count/300);

Let binding: `maxSummarizedTime`

let maxSummarizedTime = toscalar (
  union isfuzzy=true 
      (
          DNS_Summarized_Logs_ip_CL 
              | where EventTime_t >= min_t
              | summarize max_TimeGenerated=max(EventTime_t)
              | extend max_TimeGenerated = datetime_add('hour',1,max_TimeGenerated)
      ),
      (
          print(min_t)
          | project max_TimeGenerated = print_0
      )
    | summarize maxTimeGenerated = max(max_TimeGenerated) 
  );

Derived from min_t.

Let binding: `summarizationexist`

let summarizationexist = materialize(
            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` (4 sources)

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

Leg 1: `datatable(exists:int,`

(datatable(exists:int, sumexist:bool)[1,false] | where toscalar(eps) > 1000 | join (summarizationexist) on sumexist) | join (
  _Im_Dns(starttime=todatetime(ago(2d)), endtime=now())
  | where TimeGenerated > maxSummarizedTime and EventResultDetails in (Errors)
  | summarize Count=count() by SrcIpAddr, DnsQuery, bin(TimeGenerated,1h)
  | extend EventTime = TimeGenerated, Count = toint(Count), exists=int(1)
  ) on exists
  | project-away exists, maxv, sum*

Leg 2: `datatable(exists:int,`

(datatable(exists:int, sumexist:bool)[1,false] | where toscalar(eps) between (501 .. 1000) | join (summarizationexist) on sumexist) | join (
  _Im_Dns(starttime=todatetime(ago(3d)), endtime=now())
  | where TimeGenerated > maxSummarizedTime and EventResultDetails in (Errors)
  | summarize Count=count() by SrcIpAddr, DnsQuery, bin(TimeGenerated,1h)
  | extend EventTime = TimeGenerated, Count = toint(Count), exists=int(1)
  ) on exists
  | project-away exists, maxv, sum*

Leg 3: `datatable(exists:int,`

(datatable(exists:int, sumexist:bool)[1,false] | where toscalar(eps) <= 500 | join (summarizationexist) on sumexist) | join (
  _Im_Dns(starttime=todatetime(ago(4d)), endtime=now())
  | where TimeGenerated > maxSummarizedTime and EventResultDetails in (Errors)
  | summarize Count=count() by SrcIpAddr, DnsQuery, bin(TimeGenerated,1h)
  | extend EventTime = TimeGenerated, Count = toint(Count), exists=int(1)
  ) on exists
  | project-away exists, maxv, sum*

Leg 4: `DNS_Summarized_Logs_ip_CL`

DNS_Summarized_Logs_ip_CL
      | where EventTime_t > min_t and EventResultDetails_s in (Errors)
      | project-rename SrcIpAddr=SrcIpAddr_s, DnsQuery=DnsQuery_s, Count=count__d, EventTime=EventTime_t
      | extend Count = toint(Count)

Applied to the combined result

| make-series TotalIPCount=dcount(SrcIpAddr) on EventTime from min_t to max_t step dt by DnsQuery | extend (anomalies, score, baseline) = series_decompose_anomalies(TotalIPCount, threshold, -1, 'linefit') | mv-expand anomalies, score, baseline, EventTime, TotalIPCount | extend
  anomalies = toint(anomalies),
  score = toint(score),
  baseline = toint(baseline),
  EventTime = todatetime(EventTime),
  TotalIPs = tolong(TotalIPCount) | where EventTime >= ago(dt) | where score >= threshold * 2 | join kind=inner(allData | where TimeGenerated>ago(dt) | summarize SrcIps = make_set(SrcIpAddr,1000) by DnsQuery) on DnsQuery | project-away DnsQuery1

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`	in	`NXDOMAIN` transforms: `cased` `REFUSED` transforms: `cased` `SERVFAIL` transforms: `cased`
`EventResultDetails_s`	in	`NXDOMAIN` transforms: `cased` `REFUSED` transforms: `cased` `SERVFAIL` transforms: `cased`
`EventTime_t`	gt	`min_t` transforms: `cased`
`TimeGenerated`	gt	`maxSummarizedTime` 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
`DnsQuery`	`summarize`
`TotalIPCount`	`summarize`
`anomalies`	`extend`
`baseline`	`extend`
`score`	`extend`
`EventTime`	`extend`
`TotalIPs`	`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 DNS queries reporting multiple errors from different clients - Anomaly Based (ASIM DNS Solution)

MITRE ATT&CK coverage

Rule body kusto

Stages and Predicates

Parameters

Let binding: `eps`

Let binding: `maxSummarizedTime`

Let binding: `summarizationexist`

union `isfuzzy=true` (4 sources)

Leg 1: `datatable(exists:int,`

Leg 2: `datatable(exists:int,`

Leg 3: `datatable(exists:int,`

Leg 4: `DNS_Summarized_Logs_ip_CL`

Applied to the combined result

Indicators

Output fields

Keyboard shortcuts

Search operators

Detect DNS queries reporting multiple errors from different clients - Anomaly Based (ASIM DNS Solution)

MITRE ATT&CK coverage

Rule body kusto

Stages and Predicates

Parameters

Let binding: eps

Let binding: maxSummarizedTime

Let binding: summarizationexist

union isfuzzy=true (4 sources)

Leg 1: datatable(exists:int,

Leg 2: datatable(exists:int,

Leg 3: datatable(exists:int,

Leg 4: DNS_Summarized_Logs_ip_CL

Applied to the combined result

Indicators

Output fields

Let binding: `eps`

Let binding: `maxSummarizedTime`

Let binding: `summarizationexist`

union `isfuzzy=true` (4 sources)

Leg 1: `datatable(exists:int,`

Leg 2: `datatable(exists:int,`

Leg 3: `datatable(exists:int,`

Leg 4: `DNS_Summarized_Logs_ip_CL`