Suspicious Network Beacons - Microsoft Defender for Endpoint Aggregated Reports

Group by: DeviceName, InitiatingProcessFileName, RemoteIP
Author: Cyb3rMonk
Source: github.com/Cyb3r-Monk/Threat-Hunting-and-Detection

Below query detects suspicious beaconing activity by analyzing DeviceNetworkEvents Aggregated Reports telemetry. Use it as a starting point and refine further as it may generate too many results.

MITRE ATT&CK coverage

Tactic	Techniques
Command & Control	No specific technique

Event coverage

Provider	Event/ActionType	Title
Sysmon	Event ID 3	Network connection
Security-Auditing	Event ID 5156	The Windows Filtering Platform has permitted a connection.
Defender-DeviceNetworkEvents	any	Network activity (any)

Rule body kusto

// Author: Cyb3rMonk(https://twitter.com/Cyb3rMonk, https://mergene.medium.com)
// Link to original post: https://academy.bluraven.io/blog/beaconing-detection-using-mde-aggregated-report-telemetry
//
// Query parameters:
let lookback = 3d;
let min_uniform_count = 4 * (lookback / 1d); // (4 uniform distribution per lookback)
DeviceNetworkEvents
| where Timestamp > ago(lookback)
| where ActionType endswith "AggregatedReport"
| where ipv4_is_private(RemoteIP) == false
| extend ConnectionCount = toint(parse_json(AdditionalFields).uniqueEventsAggregated)
| project Timestamp = bin(Timestamp, 1h), DeviceName, InitiatingProcessFileName, RemoteIP, ConnectionCount
| sort by Timestamp asc 
| summarize Timestamp = make_list(Timestamp), ConnCounts = make_list(ConnectionCount) by DeviceName, InitiatingProcessFileName, RemoteIP
| extend count_of_hours = array_length(Timestamp)
| extend anomalies_decomposed = series_decompose_anomalies(ConnCounts, 1.5, -1),
        series_stats(ConnCounts)
| mv-apply anomaly = anomalies_decomposed to typeof(int) on (
                summarize inliner_count = countif(anomaly !in (-1, 1)), outlier_count = countif(anomaly in (-1, 1))
    )
| where inliner_count >= min_uniform_count and series_stats_ConnCounts_avg > 45 // avg=45 is roughly 1 min sleep with some jitter
| sort by series_stats_ConnCounts_avg desc 
| extend FirstConnection = Timestamp[0], LastConnection=Timestamp[-1], avg_conn_count=toint(series_stats_ConnCounts_avg)
| project-reorder FirstConnection, LastConnection, DeviceName, InitiatingProcessFileName, RemoteIP, count_of_hours, inliner_count, outlier_count, avg_conn_count

Stages and Predicates

Parameters

let lookback = 3d;
let min_uniform_count = 4 * (lookback / 1d);

Stage 1: `source`

DeviceNetworkEvents

Stage 2: `where`

| where Timestamp > ago(lookback)

Stage 3: `where`

| where ActionType endswith "AggregatedReport"

Stage 4: `where`

| where ipv4_is_private(RemoteIP) == false

Stage 5: `extend`

| extend ConnectionCount = toint(parse_json(AdditionalFields).uniqueEventsAggregated)

Stage 6: `project`

| project Timestamp = bin(Timestamp, 1h), DeviceName, InitiatingProcessFileName, RemoteIP, ConnectionCount

Stage 7: `sort`

| sort by Timestamp asc

Stage 8: `summarize`

| summarize Timestamp = make_list(Timestamp), ConnCounts = make_list(ConnectionCount) by DeviceName, InitiatingProcessFileName, RemoteIP

Stage 9: `extend`

| extend count_of_hours = array_length(Timestamp)

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

Stage 10: `extend`

| extend anomalies_decomposed = series_decompose_anomalies(ConnCounts, 1.5, -1),
        series_stats(ConnCounts)

Stage 11: `kusto:mv-apply`

| mv-apply anomaly = anomalies_decomposed to typeof(int) on (
                summarize inliner_count = countif(anomaly !in (-1, 1)), outlier_count = countif(anomaly in (-1, 1))
    )

Stage 12: `where`

| where inliner_count >= min_uniform_count and series_stats_ConnCounts_avg > 45

Stage 13: `sort`

| sort by series_stats_ConnCounts_avg desc

Stage 14: `extend`

| extend FirstConnection = Timestamp[0], LastConnection=Timestamp[-1], avg_conn_count=toint(series_stats_ConnCounts_avg)

Stage 15: `project-reorder`

| project-reorder FirstConnection, LastConnection, DeviceName, InitiatingProcessFileName, RemoteIP, count_of_hours, inliner_count, outlier_count, avg_conn_count

Exclusions

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

Field	Kind	Excluded values
`RemoteIP`	cidr_match	`10.0.0.0/8`, `172.16.0.0/12`, `192.168.0.0/16`, `169.254.0.0/16`, `127.0.0.0/8`

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
`ActionType`	ends_with	`AggregatedReport`
`inliner_count`	ge	`min_uniform_count` transforms: `cased`
`series_stats_ConnCounts_avg`	gt	`45` 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
`ConnCounts`	`summarize`
`DeviceName`	`summarize`
`InitiatingProcessFileName`	`summarize`
`RemoteIP`	`summarize`
`Timestamp`	`summarize`
`count_of_hours`	`extend`
`anomalies_decomposed`	`extend`
`FirstConnection`	`extend`
`LastConnection`	`extend`
`avg_conn_count`	`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)

Suspicious Network Beacons - Microsoft Defender for Endpoint Aggregated Reports

MITRE ATT&CK coverage

Event coverage

Rule body kusto

Stages and Predicates

Parameters

Stage 1: `source`

Stage 2: `where`

Stage 3: `where`

Stage 4: `where`

Stage 5: `extend`

Stage 6: `project`

Stage 7: `sort`

Stage 8: `summarize`

Stage 9: `extend`

Stage 10: `extend`

Stage 11: `kusto:mv-apply`

Stage 12: `where`

Stage 13: `sort`

Stage 14: `extend`

Stage 15: `project-reorder`

Exclusions

Indicators

Output fields

Keyboard shortcuts

Search operators

Suspicious Network Beacons - Microsoft Defender for Endpoint Aggregated Reports

MITRE ATT&CK coverage

Event coverage

Rule body kusto

Stages and Predicates

Parameters

Stage 1: source

Stage 2: where

Stage 3: where

Stage 4: where

Stage 5: extend

Stage 6: project

Stage 7: sort

Stage 8: summarize

Stage 9: extend

Stage 10: extend

Stage 11: kusto:mv-apply

Stage 12: where

Stage 13: sort

Stage 14: extend

Stage 15: project-reorder

Exclusions

Indicators

Output fields

Stage 1: `source`

Stage 2: `where`

Stage 3: `where`

Stage 4: `where`

Stage 5: `extend`

Stage 6: `project`

Stage 7: `sort`

Stage 8: `summarize`

Stage 9: `extend`

Stage 10: `extend`

Stage 11: `kusto:mv-apply`

Stage 12: `where`

Stage 13: `sort`

Stage 14: `extend`

Stage 15: `project-reorder`