Anomaly in SMB Traffic(ASIM Network Session schema)

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

This detection detects abnormal SMB traffic, a file-sharing protocol. By calculating the average deviation of SMB connections over last 14 days, flagging sources exceeding 50 average deviations.

MITRE ATT&CK coverage

Tactic	Techniques
Lateral Movement	`T1021.002` Remote Services: SMB/Windows Admin Shares

Event coverage

Provider	Event	Title
Sysmon	Event ID 3	Network connection
Security-Auditing	Event ID 5152	The Windows Filtering Platform blocked a packet.
Security-Auditing	Event ID 5154	The Windows Filtering Platform has permitted an application or service to listen on a port for incoming connections.
Security-Auditing	Event ID 5155	The Windows Filtering Platform has blocked an application or service from listening on a port for incoming connections.
Security-Auditing	Event ID 5156	The Windows Filtering Platform has permitted a connection.
Security-Auditing	Event ID 5157	The Windows Filtering Platform has blocked a connection.
Security-Auditing	Event ID 5158	The Windows Filtering Platform has permitted a bind to a local port.
Security-Auditing	Event ID 5159	The Windows Filtering Platform has blocked a bind to a local port.

Rule body kusto

id: 8717e498-7b5d-4e23-9e7c-fa4913dbfd79
name: Anomaly in SMB Traffic(ASIM Network Session schema)
description: |
  'This detection detects abnormal SMB traffic, a file-sharing protocol. By calculating the average deviation of SMB connections over last 14 days, flagging sources exceeding 50 average deviations.'
severity: Medium
status: Available 
tags:
  - Schema: ASimNetworkSessions
    SchemaVersion: 0.2.4
requiredDataConnectors: []
queryFrequency: 1d
queryPeriod: 14d
triggerOperator: gt
triggerThreshold: 0
tactics:
  - LateralMovement
relevantTechniques:
  - T1021
  - T1021.002
query: |
  // Define the threshold for deviation
  let threshold = 50;
  // Define the time range for the baseline data
  let starttime = 14d;
  let endtime = 1d;
  // Define the SMB ports to monitor
  let SMBPorts = dynamic(["139", "445"]);
  // Get the baseline data for user network sessions and Filter for the defined time range
  let userBaseline = _Im_NetworkSession(starttime=ago(starttime), endtime=ago(endtime))
    | where ipv4_is_private(SrcIpAddr) and tostring(DstPortNumber) has_any (SMBPorts) and SrcIpAddr != DstIpAddr // Filter for private IP addresses and SMB ports
    | summarize Count = count() by SrcIpAddr, DstPortNumber // Group by source IP and destination port
    | summarize AvgCount = avg(Count) by SrcIpAddr, DstPortNumber; // Calculate the average count
  // Get the recent user activity data and Filter for recent activity
  let recentUserActivity = _Im_NetworkSession(starttime=ago(endtime))
    | where ipv4_is_private(SrcIpAddr) and tostring(DstPortNumber) has_any (SMBPorts) and SrcIpAddr != DstIpAddr // Filter for private IP addresses and SMB ports
    | summarize StartTimeUtc = min(TimeGenerated), EndTimeUtc = max(TimeGenerated), RecentCount = count() by SrcIpAddr, DstPortNumber; // Group by source IP and destination port
  // Join the baseline and recent activity data
  let UserBehaviorAnalysis = userBaseline
    | join kind=inner (recentUserActivity) on SrcIpAddr, DstPortNumber
    | extend Deviation = abs(RecentCount - AvgCount) / AvgCount; // Calculate the deviation
  // Filter for deviations greater than the threshold
  UserBehaviorAnalysis
    | where Deviation > threshold
    | project SrcIpAddr, DstPortNumber, Deviation, Count = RecentCount; // Project the required columns
entityMappings:
  - entityType: IP
    fieldMappings:
      - identifier: Address
        columnName: SrcIpAddr
eventGroupingSettings:
  aggregationKind: AlertPerResult
version: 1.0.0
kind: Scheduled

Stages and Predicates

Parameters

let threshold = 50;
let starttime = 14d;
let endtime = 1d;
let SMBPorts = dynamic(["139", "445"]);

Let binding: `recentUserActivity`

let recentUserActivity = _Im_NetworkSession(starttime=ago(endtime))
  | where ipv4_is_private(SrcIpAddr) and tostring(DstPortNumber) has_any (SMBPorts) and SrcIpAddr != DstIpAddr
  | summarize StartTimeUtc = min(TimeGenerated), EndTimeUtc = max(TimeGenerated), RecentCount = count() by SrcIpAddr, DstPortNumber;

Derived from starttime, endtime, SMBPorts.

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

Stage 1: `source`

_Im_NetworkSession(starttime=ago(starttime), endtime=ago(endtime))

Stage 2: `where`

| where ipv4_is_private(SrcIpAddr) and tostring(DstPortNumber) has_any (SMBPorts) and SrcIpAddr != DstIpAddr

Stage 3: `summarize`

| summarize Count = count() by SrcIpAddr, DstPortNumber

Stage 4: `summarize`

| summarize AvgCount = avg(Count) by SrcIpAddr, DstPortNumber

Stage 5: `join`

| join kind=inner (recentUserActivity) on SrcIpAddr, DstPortNumber

Stage 6: `extend`

| extend Deviation = abs(RecentCount - AvgCount) / AvgCount

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

Stage 7: `where`

UserBehaviorAnalysis
| where Deviation > threshold

Stage 8: `project`

| project SrcIpAddr, DstPortNumber, Deviation, Count = RecentCount;

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
`Deviation`	gt	`50` transforms: `cased`
`DstPortNumber`	match	`139` transforms: `tostring` `445` transforms: `tostring`
`SrcIpAddr`	cidr_match	`10.0.0.0/8` corpus 9 (kusto 7, elastic 2) `127.0.0.0/8` corpus 9 (kusto 7, elastic 2) `169.254.0.0/16` corpus 8 (kusto 7, elastic 1) `172.16.0.0/12` corpus 9 (kusto 7, elastic 2) `192.168.0.0/16` corpus 9 (kusto 7, elastic 2)
`SrcIpAddr`	ne	`DstIpAddr` transforms: `cased` corpus 2 (kusto 2)

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
`Count`	`project`
`Deviation`	`project`
`DstPortNumber`	`project`
`SrcIpAddr`	`project`

`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)

Anomaly in SMB Traffic(ASIM Network Session schema)

MITRE ATT&CK coverage

Event coverage

Rule body kusto

Stages and Predicates

Parameters

Let binding: `recentUserActivity`

Stage 1: `source`

Stage 2: `where`

Stage 3: `summarize`

Stage 4: `summarize`

Stage 5: `join`

Stage 6: `extend`

Stage 7: `where`

Stage 8: `project`

Indicators

Output fields

Keyboard shortcuts

Search operators

Anomaly in SMB Traffic(ASIM Network Session schema)

MITRE ATT&CK coverage

Event coverage

Rule body kusto

Stages and Predicates

Parameters

Let binding: recentUserActivity

Stage 1: source

Stage 2: where

Stage 3: summarize

Stage 4: summarize

Stage 5: join

Stage 6: extend

Stage 7: where

Stage 8: project

Indicators

Output fields

Let binding: `recentUserActivity`

Stage 1: `source`

Stage 2: `where`

Stage 3: `summarize`

Stage 4: `summarize`

Stage 5: `join`

Stage 6: `extend`

Stage 7: `where`

Stage 8: `project`