Potential DGA(Domain Generation Algorithm) detected via Repetitive Failures - 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 detect clients with a high NXDomain response count, which could be indicative of a DGA (cycling through possible C2 domains where most C2s are not live). An alert is generated when new IP address DNS activity is identified as an outlier when compared to the baseline, indicating a recurring pattern. 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: 01191239-274e-43c9-b154-3a042692af06
name: Potential DGA(Domain Generation Algorithm) detected via Repetitive Failures - Anomaly based (ASIM DNS Solution)
description: |
  'This rule makes use of the series decompose anomaly method to detect clients with a high NXDomain response count, which could be indicative of a DGA (cycling through possible C2 domains where most C2s are not live). An alert is generated when new IP address DNS activity is identified as an outlier when compared to the baseline, indicating a recurring pattern. 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 timeframe = 1d;
  // 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(responsecodename='NXDOMAIN', starttime=todatetime(ago(2d)), endtime=now())
            | where TimeGenerated > maxSummarizedTime
            | 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(responsecodename='NXDOMAIN', starttime=todatetime(ago(3d)), endtime=now())
            | where TimeGenerated > maxSummarizedTime
            | 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(responsecodename='NXDOMAIN', starttime=todatetime(ago(4d)), endtime=now())
            | where TimeGenerated > maxSummarizedTime
            | 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 == 'NXDOMAIN'
        | project-rename
            SrcIpAddr=SrcIpAddr_s,
            DnsQuery=DnsQuery_s,
            Count=count__d,
            EventTime=EventTime_t
        | extend Count = toint(Count)
        );
  allData
  | make-series QueryCount=dcount(DnsQuery) on EventTime from min_t to max_t step timeframe by SrcIpAddr
  // include calculated Anomalies, Score and Baseline
  | extend (anomalies, score, baseline) = series_decompose_anomalies(QueryCount, threshold, -1, 'linefit')
  | mv-expand anomalies, score, baseline, EventTime, QueryCount
  | extend
    anomalies = toint(anomalies),
    score = toint(score),
    baseline = toint(baseline),
    EventTime = todatetime(EventTime),
    Total = tolong(QueryCount)
  | where EventTime >= ago(timeframe)
  | where score >= threshold * 2
  // Join allData to include DnsQuery details
  | join kind=inner(allData
    | where TimeGenerated >= ago(timeframe)
    | 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] Potential DGA (Domain Generation Algorithm) originating from client IP: '{{SrcIpAddr}}' has been detected."
  alertDescriptionFormat: "Client has been identified with high NXDomain count which could be indicative of a DGA (cycling through possible C2 domains where most C2s are not live). This client is found to be communicating with multiple Domains which do not exist.\n\nBaseline Domain or DNS query count from this client: '{{baseline}}'\n\nCurrent Domain or DNS query count from this client: '{{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 min_t = ago(14d);
let max_t = now();
let timeframe = 1d;

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:, datatable(exists:, datatable(exists:, DNS_Summarized_Logs_ip_CL

Leg 1: `datatable(exists:`

(datatable(exists: int, sumexist: bool)[1, false]
      | where toscalar(eps) > 1000
      | join (summarizationexist) on sumexist)
      | join (
          _Im_Dns(responsecodename='NXDOMAIN', starttime=todatetime(ago(2d)), endtime=now())
          | where TimeGenerated > maxSummarizedTime
          | 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:`

(datatable(exists: int, sumexist: bool)[1, false]
      | where toscalar(eps) between (501 .. 1000)
      | join (summarizationexist) on sumexist)
      | join (
          _Im_Dns(responsecodename='NXDOMAIN', starttime=todatetime(ago(3d)), endtime=now())
          | where TimeGenerated > maxSummarizedTime
          | 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:`

(datatable(exists: int, sumexist: bool)[1, false]
      | where toscalar(eps) <= 500
      | join (summarizationexist) on sumexist)
      | join (
          _Im_Dns(responsecodename='NXDOMAIN', starttime=todatetime(ago(4d)), endtime=now())
          | where TimeGenerated > maxSummarizedTime
          | 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 == 'NXDOMAIN'
      | 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 QueryCount=dcount(DnsQuery) on EventTime 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, EventTime, QueryCount | extend
  anomalies = toint(anomalies),
  score = toint(score),
  baseline = toint(baseline),
  EventTime = todatetime(EventTime),
  Total = tolong(QueryCount) | where EventTime >= ago(timeframe) | where score >= threshold * 2 | join kind=inner(allData
  | where TimeGenerated >= ago(timeframe)
  | summarize DNSQueries = make_set(DnsQuery, 1000) 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`
`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
`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)

Potential DGA(Domain Generation Algorithm) detected via Repetitive Failures - 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:`

Leg 2: `datatable(exists:`

Leg 3: `datatable(exists:`

Leg 4: `DNS_Summarized_Logs_ip_CL`

Applied to the combined result

Indicators

Output fields

Keyboard shortcuts

Search operators

Potential DGA(Domain Generation Algorithm) detected via Repetitive Failures - 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:

Leg 2: datatable(exists:

Leg 3: datatable(exists:

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:`

Leg 2: `datatable(exists:`

Leg 3: `datatable(exists:`

Leg 4: `DNS_Summarized_Logs_ip_CL`