Detection rules › Kusto

Suspicious access of BEC related documents

Severity
medium
Time window
14d
Group by
User
Source
github.com/Azure/Azure-Sentinel

This query looks for users with suspicious spikes in the number of files accessed that relate to topics commonly accessed as part of Business Email Compromise (BEC) attacks. The query looks for access to files in storage that relate to topics such as invoices or payments, and then looks for users accessing these files in significantly higher numbers than in the previous 14 days. Incidents raised by this analytic should be investigated to see if the user accessing these files should be accessing them, and if the volume they accessed them at was related to a legitimate business need. This query contains thresholds to reduce the chance of false positives, these can be adjusted to suit individual environments. In addition false positives could be generated by legitimate, scheduled actions that occur less often than every 14 days, additional exclusions can be added for these actions on username or IP address entities. This query uses the imFileEvent schema from ASIM, you will first need to ensure you have ASIM deployed in your environment. Ref https://learn.microsoft.com/azure/sentinel/normalization-about-parsers

MITRE ATT&CK coverage

TacticTechniques
CollectionT1530 Data from Cloud Storage

Event coverage

Rule body kusto

id: cd8d946d-10a4-40a9-bac1-6d0a6c847d65
name: Suspicious access of BEC related documents
description: |
  'This query looks for users with suspicious spikes in the number of files accessed that relate to topics commonly accessed as part of Business Email Compromise (BEC) attacks.
  The query looks for access to files in storage that relate to topics such as invoices or payments, and then looks for users accessing these files in significantly higher numbers than in the previous 14 days. Incidents raised by this analytic should be investigated to see if the user accessing these files should be accessing them, and if the volume they accessed them at was related to a legitimate business need. 
  This query contains thresholds to reduce the chance of false positives, these can be adjusted to suit individual environments. In addition false positives could be generated by legitimate, scheduled actions that occur less often than every 14 days, additional exclusions can be added for these actions on username or IP address entities. This query uses the imFileEvent schema from ASIM, you will first need to ensure you have ASIM deployed in your environment. Ref https://learn.microsoft.com/azure/sentinel/normalization-about-parsers'
severity: Medium
requiredDataConnectors: []
queryFrequency: 1d
queryPeriod: 14d
triggerOperator: gt
triggerThreshold: 0
tactics:
  - Collection
relevantTechniques:
  - T1530
eventGroupingSettings:
  aggregationKind: SingleAlert
query: |
  let BEC_Keywords = dynamic([ 'invoice','payment','paycheck','transfer','bank statement','bank details','closing','funds','bank account','account details','remittance','purchase','deposit',"PO#","Zahlung","Rechnung","Paiement", "virement bancaire","Bankuberweisung",'hacked','phishing']);
  // Adjust this threshold based on your environment
  let sensitivity = 2.5;
  let Events = materialize(imFileEvent
  | where TimeGenerated between(startofday(ago(14d))..endofday(ago(0d)))
  | where User !~ "app@sharepoint"
  | where EventType =~ "FileAccessed"
  | extend OriginalEvent = column_ifexists("EventOriginalType","Unknown")
  | where OriginalEvent !~ "FileSyncDownloadedFull"
  | where EventProduct in ("SharePoint 365", "Azure File Storage", "OneDrive" , "SharePoint")
  | where FilePath has_any(BEC_Keywords)
  | extend _AuthDetails = column_ifexists("AuthorizationDetails", "None")
  | extend SPuser = case(gettype(_AuthDetails) == "array", tostring(todynamic(_AuthDetails)[0].principals[0].id), "Unknown")
  | extend User = case(isnotempty(User), User, SPuser)
  | where isnotempty(User));
  Events
  | summarize dcount(FileName) by User, bin(startofday(TimeGenerated), 1d)
  | summarize CountOfDocs = make_list(dcount_FileName, 10000), TimeStamp = make_list(TimeGenerated, 10000) by User
  | extend (Anomalies, Score, Baseline) = series_decompose_anomalies(CountOfDocs, sensitivity, -1, 'linefit')
  | mv-expand CountOfDocs to typeof(double), TimeStamp to typeof(datetime), Anomalies to typeof(double), Score to typeof(double), Baseline to typeof(long)
  | where Anomalies > 0
  | project TimeStamp, CountOfDocs, Baseline, Score, Anomalies, User
  | join kind=inner(Events | extend TimeStamp = startofday(TimeGenerated)) on TimeStamp, User
  | extend IpAddr = column_ifexists("IpAddr", SrcIpAddr)
  | extend Name = iif(User contains "@", split(User, "@")[0], split(User, "\\")[1])
  | extend UPNSuffix = iif(User contains "@", split(User, "@")[1], "")
  | extend NTDomain = iif(User contains "@", split(User, "\\")[0], "")
  | project-reorder TimeGenerated, User, EventType, EventResult, EventProduct, FilePath, HttpUserAgent, IpAddr, CountOfDocs, Baseline, Score
entityMappings:
  - entityType: Account
    fieldMappings:
      - identifier: FullName
        columnName: User
      - identifier: Name
        columnName: Name
      - identifier: UPNSuffix
        columnName: UPNSuffix
  - entityType: Account
    fieldMappings:
      - identifier: FullName
        columnName: User
      - identifier: Name
        columnName: Name
      - identifier: NTDomain
        columnName: NTDomain   
  - entityType: Account
    fieldMappings:        
      - identifier: AadUserId
        columnName: User
  - entityType: IP
    fieldMappings:
      - identifier: Address
        columnName: IpAddr
  - entityType: File
    fieldMappings:
      - identifier: Name
        columnName: FilePath
customDetails:
  Type: EventType
  Result: EventResult
  Product: EventProduct
  UserAgent: HttpUserAgent
alertDetailsOverride:
  alertDisplayNameFormat: Suspicious access of {{CountOfDocs}} BEC related documents by {{User}}
  alertDescriptionFormat: |
    This query looks for users (in this case {{User}}) with suspicious spikes in the number of files accessed (in this case {{CountOfDocs}} events) that relate to topics commonly accessed as part of Business Email Compromise (BEC) attacks. The query looks for access to files in storage that relate to topics such as invoices or payments, and then looks for users accessing these files in significantly higher numbers than in the previous 14 days. Incidents raised by this analytic should be investigated to see if the user accessing these files should be accessing them, and if the volume they accessed them at was related to a legitimate business need. 
    This query contains thresholds to reduce the chance of false positives, these can be adjusted to suit individual environments. In addition false positives could be generated by legitimate, scheduled actions that occur less often than every 14 days, additional exclusions can be added for these actions on username or IP address entities. This query uses the imFileEvent schema from ASIM, you will first need to ensure you have ASIM deployed in your environment. Ref https://learn.microsoft.com/azure/sentinel/normalization-about-parsers
version: 1.0.5
kind: Scheduled

Stages and Predicates

Parameters

let sensitivity = 2.5;

Let binding: BEC_Keywords

let BEC_Keywords = dynamic([ 'invoice','payment','paycheck','transfer','bank statement','bank details','closing','funds','bank account','account details','remittance','purchase','deposit',"PO#","Zahlung","Rechnung","Paiement", "virement bancaire","Bankuberweisung",'hacked','phishing']);

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

Stage 1: source

imFileEvent

Stage 2: where

| where TimeGenerated between(startofday(ago(14d))..endofday(ago(0d)))

Stage 3: where

| where User !~ "app@sharepoint"

Stage 4: where

| where EventType =~ "FileAccessed"

Stage 5: extend

| extend OriginalEvent = column_ifexists("EventOriginalType","Unknown")

Stage 6: where

| where OriginalEvent !~ "FileSyncDownloadedFull"

Stage 7: where

| where EventProduct in ("SharePoint 365", "Azure File Storage", "OneDrive" , "SharePoint")

Stage 8: where

| where FilePath has_any(BEC_Keywords)

References BEC_Keywords (defined above).

Stage 9: extend (3 consecutive steps)

| extend _AuthDetails = column_ifexists("AuthorizationDetails", "None")
| extend SPuser = case(gettype(_AuthDetails) == "array", tostring(todynamic(_AuthDetails)[0].principals[0].id), "Unknown")
| extend User = case(isnotempty(User), User, SPuser)

Stage 10: where

| where isnotempty(User)

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

Stage 11: summarize

Events
| summarize dcount(FileName) by User, bin(startofday(TimeGenerated), 1d)

Stage 12: summarize

| summarize CountOfDocs = make_list(dcount_FileName, 10000), TimeStamp = make_list(TimeGenerated, 10000) by User

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

Stage 13: extend

| extend (Anomalies, Score, Baseline) = series_decompose_anomalies(CountOfDocs, sensitivity, -1, 'linefit')

Stage 14: mv-expand

| mv-expand CountOfDocs to typeof(double), TimeStamp to typeof(datetime), Anomalies to typeof(double), Score to typeof(double), Baseline to typeof(long)

Stage 15: where

| where Anomalies > 0

Stage 16: project

| project TimeStamp, CountOfDocs, Baseline, Score, Anomalies, User

Stage 17: join

| join kind=inner(Events | extend TimeStamp = startofday(TimeGenerated)) on TimeStamp, User

Stage 18: extend (4 consecutive steps)

| extend IpAddr = column_ifexists("IpAddr", SrcIpAddr)
| extend Name = iif(User contains "@", split(User, "@")[0], split(User, "\\")[1])
| extend UPNSuffix = iif(User contains "@", split(User, "@")[1], "")
| extend NTDomain = iif(User contains "@", split(User, "\\")[0], "")

Stage 19: project-reorder

| project-reorder TimeGenerated, User, EventType, EventResult, EventProduct, FilePath, HttpUserAgent, IpAddr, CountOfDocs, Baseline, Score

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.

FieldKindValues
Anomaliesgt
  • 0 transforms: cased corpus 6 (kusto 6)
EventProductin
  • Azure File Storage transforms: cased
  • OneDrive transforms: cased
  • SharePoint transforms: cased
  • SharePoint 365 transforms: cased
EventTypeeq
  • FileAccessed
FilePathmatch
  • Bankuberweisung corpus 2 (kusto 2)
  • PO# corpus 2 (kusto 2)
  • Paiement corpus 2 (kusto 2)
  • Rechnung corpus 2 (kusto 2)
  • Zahlung corpus 2 (kusto 2)
  • account details corpus 2 (kusto 2)
  • bank account corpus 2 (kusto 2)
  • bank details corpus 2 (kusto 2)
  • bank statement corpus 2 (kusto 2)
  • closing corpus 2 (kusto 2)
  • deposit corpus 2 (kusto 2)
  • funds corpus 2 (kusto 2)
  • hacked corpus 2 (kusto 2)
  • invoice corpus 2 (kusto 2)
  • paycheck corpus 2 (kusto 2)
  • payment corpus 2 (kusto 2)
  • phishing corpus 2 (kusto 2)
  • purchase corpus 2 (kusto 2)
  • remittance corpus 2 (kusto 2)
  • transfer corpus 2 (kusto 2)
  • virement bancaire corpus 2 (kusto 2)
OriginalEventne
  • FileSyncDownloadedFull
Useris_not_null
  • (no value, null check)
Userne
  • app@sharepoint corpus 2 (splunk 1, kusto 1)

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.

FieldSource
Anomaliesproject
Baselineproject
CountOfDocsproject
Scoreproject
TimeStampproject
Userproject
IpAddrextend
Nameextend
UPNSuffixextend
NTDomainextend