Large number of AD objects accessed by user

Group by: Account
Author: FalconForce
Source: github.com/FalconForceTeam/FalconFriday

This query detects a user accessing a large number of Group and User objects from Active Directory which is outside the baseline of normal behavior for that particular user.

MITRE ATT&CK coverage

Tactic	Techniques
Discovery	`T1087.002` Account Discovery: Domain Account
Collection	`T1119` Automated Collection

References

Event coverage

Provider	Event	Title
Security-Auditing	Event ID 4662	An operation was performed on an object.

Rule body kusto

let timeframe = 2*1d;
let RuleId = "0108";
let DedupFields = dynamic(["TimeGenerated"]);
let lookback_days=14d; // Look back this many days to calculate baseline for maximum number of objects accessed per day.
let min_suspicious_count = 20000; // Only consider users looking up at least this many objects during the timeframe.
let suspicious_factor = 50; // Consider as suspicious if requesting more than suspicious_factor*daily maximum in lookback period.
let ADObjectAccess=(
    SecurityEvent
    | where EventID == 4662
    | where not(Account endswith "$")
    | where ObjectType in~ (
        "%{bf967aba-0de6-11d0-a285-00aa003049e2}", // User.
        "%{bf967a9c-0de6-11d0-a285-00aa003049e2}" //  Group.
    )
);
let AccessBaseline=(
    ADObjectAccess
    | where TimeGenerated <= ago(timeframe)
    | where TimeGenerated >= ago(timeframe + lookback_days)
    | summarize BaselineObjectCount=count() by Account, bin(TimeGenerated,1d)
    | summarize PreviousMaxObjectPerDay=max(BaselineObjectCount) by Account
);
ADObjectAccess
| where ingestion_time() >= ago(timeframe)
| summarize ObjectCount=count(),TimeGenerated=min(TimeGenerated) by Account
| where ObjectCount > min_suspicious_count
| join kind=leftouter AccessBaseline on Account
| extend PreviousMaxObjectPerDay=coalesce(PreviousMaxObjectPerDay,0)
// Calculate what is considered a suspicious number for the given user.
| extend SuspiciousThreshold=max_of(PreviousMaxObjectPerDay*suspicious_factor,min_suspicious_count)
| where ObjectCount > SuspiciousThreshold
| project TimeGenerated, Account, ObjectCount, PreviousMaxObjectPerDay, SuspiciousThreshold
| extend AccountName=iif(Account contains @"\",tostring(split(Account,@"\")[1]),Account),AccountDomain=iif(Account contains @"\",tostring(split(Account,@"\")[0]),"")
// Begin environment-specific filter.
// End environment-specific filter.
// Begin de-duplication logic.
| extend DedupFieldValues=pack_all()
| mv-apply e=DedupFields to typeof(string) on (
    extend DedupValue=DedupFieldValues[tostring(e)]
    | order by e // Sorting is required to ensure make_list is deterministic.
    | summarize DedupValues=make_list(DedupValue)
)
| extend DedupEntity=strcat_array(DedupValues, "|")
| project-away DedupFieldValues, DedupValues
| join kind=leftanti (
    SecurityAlert
    | where AlertName has RuleId and ProviderName has "ASI"
    | where TimeGenerated >= ago(timeframe)
    | extend DedupEntity = tostring(parse_json(tostring(parse_json(ExtendedProperties)["Custom Details"])).DedupEntity[0])
    | project DedupEntity
) on DedupEntity
// End de-duplication logic.

Stages and Predicates

Parameters

let timeframe = 2*1d;
let RuleId = "0108";
let DedupFields = dynamic(["TimeGenerated"]);
let lookback_days = 14d;
let min_suspicious_count = 20000;
let suspicious_factor = 50;

Let binding: `AccessBaseline`

let AccessBaseline = (
    ADObjectAccess
    | where TimeGenerated <= ago(timeframe)
    | where TimeGenerated >= ago(timeframe + lookback_days)
    | summarize BaselineObjectCount=count() by Account, bin(TimeGenerated,1d)
    | summarize PreviousMaxObjectPerDay=max(BaselineObjectCount) by Account
);

Derived from timeframe, lookback_days, ADObjectAccess.

The stages below define let ADObjectAccess (the rule's main pipeline source).

Stage 1: `source`

let ADObjectAccess

Stage 2: `source`

let AccessBaseline

Stage 3: `source`

SecurityEvent

Stage 4: `where`

| where EventID == 4662

Stage 5: `where`

| where not(Account endswith "$")

Stage 6: `where`

| where ObjectType in~ (
        "%{bf967aba-0de6-11d0-a285-00aa003049e2}",
        "%{bf967a9c-0de6-11d0-a285-00aa003049e2}"
    )

The stages below run on ADObjectAccess (the outer pipeline).

Stage 7: `where`

ADObjectAccess
| where ingestion_time() >= ago(timeframe)

Stage 8: `summarize`

| summarize ObjectCount=count(),TimeGenerated=min(TimeGenerated) by Account

Threshold: gt 20000

Stage 9: `where`

| where ObjectCount > min_suspicious_count

Stage 10: `join`

| join kind=leftouter AccessBaseline on Account

Stage 11: `extend`

| extend PreviousMaxObjectPerDay=coalesce(PreviousMaxObjectPerDay,0)

Stage 12: `extend`

| extend SuspiciousThreshold=max_of(PreviousMaxObjectPerDay*suspicious_factor,min_suspicious_count)

Stage 13: `where`

| where ObjectCount > SuspiciousThreshold

Stage 14: `project`

| project TimeGenerated, Account, ObjectCount, PreviousMaxObjectPerDay, SuspiciousThreshold

Stage 15: `extend`

| extend AccountName=iif(Account contains @"\",tostring(split(Account,@"\")[1]),Account),AccountDomain=iif(Account contains @"\",tostring(split(Account,@"\")[0]),"")

Stage 16: `extend`

| extend DedupFieldValues=pack_all()

Stage 17: `kusto:mv-apply`

| mv-apply e=DedupFields to typeof(string) on (
    extend DedupValue=DedupFieldValues[tostring(e)]
    | order by e
    | summarize DedupValues=make_list(DedupValue)
)

Stage 18: `extend`

| extend DedupEntity=strcat_array(DedupValues, "|")

Stage 19: `project-away`

| project-away DedupFieldValues, DedupValues

Stage 20: `join` (negated)

| join kind=leftanti (
    SecurityAlert
    | where AlertName has RuleId and ProviderName has "ASI"
    | where TimeGenerated >= ago(timeframe)
    | extend DedupEntity = tostring(parse_json(tostring(parse_json(ExtendedProperties)["Custom Details"])).DedupEntity[0])
    | project DedupEntity
) on DedupEntity

Exclusions

Top-level NOT(...) conjuncts: predicates this rule actively suppresses.

Field	Kind	Excluded values
`Account`	ends_with	`$`
`Account`	ends_with	`$`
`AlertName`	match	`0108`
`ProviderName`	match	`ASI`

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
`EventID`	eq	`4662` transforms: `cased` corpus 13 (splunk 7, kusto 4, elastic 1, chronicle 1)
`ObjectCount`	gt	`20000` transforms: `cased` `SuspiciousThreshold` transforms: `cased`
`ObjectType`	in	`%{bf967a9c-0de6-11d0-a285-00aa003049e2}` `%{bf967aba-0de6-11d0-a285-00aa003049e2}`

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
`Account`	`project`
`ObjectCount`	`project`
`PreviousMaxObjectPerDay`	`project`
`SuspiciousThreshold`	`project`
`TimeGenerated`	`project`
`AccountDomain`	`extend`
`AccountName`	`extend`
`DedupEntity`	`extend`

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`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)

Large number of AD objects accessed by user

MITRE ATT&CK coverage

References

Event coverage

Rule body kusto

Stages and Predicates

Parameters

Let binding: `AccessBaseline`

Stage 1: `source`

Stage 2: `source`

Stage 3: `source`

Stage 4: `where`

Stage 5: `where`

Stage 6: `where`

Stage 7: `where`

Stage 8: `summarize`

Stage 9: `where`

Stage 10: `join`

Stage 11: `extend`

Stage 12: `extend`

Stage 13: `where`

Stage 14: `project`

Stage 15: `extend`

Stage 16: `extend`

Stage 17: `kusto:mv-apply`

Stage 18: `extend`

Stage 19: `project-away`

Stage 20: `join` (negated)

Exclusions

Indicators

Output fields

Keyboard shortcuts

Search operators

Large number of AD objects accessed by user

MITRE ATT&CK coverage

References

Event coverage

Rule body kusto

Stages and Predicates

Parameters

Let binding: AccessBaseline

Stage 1: source

Stage 2: source

Stage 3: source

Stage 4: where

Stage 5: where

Stage 6: where

Stage 7: where

Stage 8: summarize

Stage 9: where

Stage 10: join

Stage 11: extend

Stage 12: extend

Stage 13: where

Stage 14: project

Stage 15: extend

Stage 16: extend

Stage 17: kusto:mv-apply

Stage 18: extend

Stage 19: project-away

Stage 20: join (negated)

Exclusions

Indicators

Output fields

Let binding: `AccessBaseline`

Stage 1: `source`

Stage 2: `source`

Stage 3: `source`

Stage 4: `where`

Stage 5: `where`

Stage 6: `where`

Stage 7: `where`

Stage 8: `summarize`

Stage 9: `where`

Stage 10: `join`

Stage 11: `extend`

Stage 12: `extend`

Stage 13: `where`

Stage 14: `project`

Stage 15: `extend`

Stage 16: `extend`

Stage 17: `kusto:mv-apply`

Stage 18: `extend`

Stage 19: `project-away`

Stage 20: `join` (negated)