Suspicious access of BEC related documents in AWS S3 buckets

id: f3e2d35f-1202-4215-995c-4654ef07d1d8
name: Suspicious access of BEC related documents in AWS S3 buckets
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 AWS S3 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.'
severity: Medium
requiredDataConnectors:
  - connectorId: AWS
    dataTypes:
      - AWSCloudTrail
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(AWSCloudTrail
  | where TimeGenerated between (ago(14d)..ago(0d))
  | where UserIdentityAccountId != "anonymous"
  | where EventSource startswith "s3."
  | where EventName =~ "GetObject"
  | extend FilePath = tostring(parse_json(RequestParameters).key)
  | where FilePath has_any(BEC_Keywords)
  );
  Events
  | summarize dcount(FilePath) by UserIdentityPrincipalid, bin(startofday(TimeGenerated), 1d)
  | summarize CountOfDocs = make_list(dcount_FilePath, 10000), TimeStamp = make_list(TimeGenerated, 10000) by UserIdentityPrincipalid
  | 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, UserIdentityPrincipalid
  | join kind=inner(Events | extend TimeStamp = startofday(TimeGenerated)) on TimeStamp, UserIdentityPrincipalid
  | extend Name = iif(UserIdentityUserName contains "@", split(UserIdentityUserName, "@")[0], UserIdentityUserName)
  | extend UPNSuffix = iif(UserIdentityUserName contains "@", split(UserIdentityUserName, "@")[1], "")
  | project-reorder TimeGenerated, UserIdentityType, UserIdentityPrincipalid, UserIdentityUserName, FilePath, EventName, UserAgent, SourceIpAddress, CountOfDocs, Baseline, Score
entityMappings:
  - entityType: Account
    fieldMappings:
      - identifier: FullName
        columnName: UserIdentityUserName
      - identifier: Name
        columnName: Name
      - identifier: UPNSuffix
        columnName: UPNSuffix
  - entityType: IP
    fieldMappings:
      - identifier: Address
        columnName: SourceIpAddress
  - entityType: File
    fieldMappings:
      - identifier: Name
        columnName: FilePath
customDetails:
  UserType: UserIdentityType
  Event: EventName
  UserAgent: UserAgent
alertDetailsOverride:
  alertDisplayNameFormat: Suspicious access of {{CountOfDocs}} BEC related documents in AWS S3 buckets by {{UserIdentityUserName}}
  alertDescriptionFormat: |
    This query looks for users (in this case {{UserIdentityUserName}}) with suspicious spikes in the number of files accessed (in this case {{CountOfDocs}})that relate to topics commonly accessed as part of Business Email Compromise (BEC) attacks. The query looks for access to files in AWS S3 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.
version: 1.0.4
kind: Scheduled