Suspicious access of BEC related documents in AWS S3 buckets
| Id | f3e2d35f-1202-4215-995c-4654ef07d1d8 |
| Rulename | 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 |
| Tactics | Collection |
| Techniques | T1530 |
| Required data connectors | AWS |
| Kind | Scheduled |
| Query frequency | 1d |
| Query period | 14d |
| Trigger threshold | 0 |
| Trigger operator | gt |
| Source Uri | https://github.com/Azure/Azure-Sentinel/blob/master/Solutions/Business Email Compromise - Financial Fraud/Analytic Rules/SuspiciousAccessOfBECRelatedDocumentsInAWSS3Buckets.yaml |
| Version | 1.0.3 |
| Arm template | f3e2d35f-1202-4215-995c-4654ef07d1d8.json |
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
kind: Scheduled
queryPeriod: 14d
customDetails:
UserType: UserIdentityType
Event: EventName
UserAgent: UserAgent
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.'
tactics:
- Collection
id: f3e2d35f-1202-4215-995c-4654ef07d1d8
requiredDataConnectors:
- connectorId: AWS
dataTypes:
- AWSCloudTrail
relevantTechniques:
- T1530
severity: Medium
version: 1.0.3
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
name: Suspicious access of BEC related documents in AWS S3 buckets
triggerOperator: gt
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
queryFrequency: 1d
alertDetailsOverride:
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.
alertDisplayNameFormat: Suspicious access of {{CountOfDocs}} BEC related documents in AWS S3 buckets by {{UserIdentityUserName}}
OriginalUri: https://github.com/Azure/Azure-Sentinel/blob/master/Solutions/Business Email Compromise - Financial Fraud/Analytic Rules/SuspiciousAccessOfBECRelatedDocumentsInAWSS3Buckets.yaml
triggerThreshold: 0
{
"$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentTemplate.json#",
"contentVersion": "1.0.0.0",
"parameters": {
"workspace": {
"type": "String"
}
},
"resources": [
{
"apiVersion": "2023-02-01-preview",
"id": "[concat(resourceId('Microsoft.OperationalInsights/workspaces/providers', parameters('workspace'), 'Microsoft.SecurityInsights'),'/alertRules/f3e2d35f-1202-4215-995c-4654ef07d1d8')]",
"kind": "Scheduled",
"name": "[concat(parameters('workspace'),'/Microsoft.SecurityInsights/f3e2d35f-1202-4215-995c-4654ef07d1d8')]",
"properties": {
"alertDetailsOverride": {
"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. \nThis 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.\n",
"alertDisplayNameFormat": "Suspicious access of {{CountOfDocs}} BEC related documents in AWS S3 buckets by {{UserIdentityUserName}}"
},
"alertRuleTemplateName": "f3e2d35f-1202-4215-995c-4654ef07d1d8",
"customDetails": {
"Event": "EventName",
"UserAgent": "UserAgent",
"UserType": "UserIdentityType"
},
"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. \nThis 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.'\n",
"displayName": "Suspicious access of BEC related documents in AWS S3 buckets",
"enabled": true,
"entityMappings": [
{
"entityType": "Account",
"fieldMappings": [
{
"columnName": "UserIdentityUserName",
"identifier": "FullName"
},
{
"columnName": "Name",
"identifier": "Name"
},
{
"columnName": "UPNSuffix",
"identifier": "UPNSuffix"
}
]
},
{
"entityType": "IP",
"fieldMappings": [
{
"columnName": "SourceIpAddress",
"identifier": "Address"
}
]
},
{
"entityType": "File",
"fieldMappings": [
{
"columnName": "FilePath",
"identifier": "Name"
}
]
}
],
"eventGroupingSettings": {
"aggregationKind": "SingleAlert"
},
"OriginalUri": "https://github.com/Azure/Azure-Sentinel/blob/master/Solutions/Business Email Compromise - Financial Fraud/Analytic Rules/SuspiciousAccessOfBECRelatedDocumentsInAWSS3Buckets.yaml",
"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']);\n// Adjust this threshold based on your environment\nlet sensitivity = 2.5;\nlet Events = materialize(AWSCloudTrail\n| where TimeGenerated between (ago(14d)..ago(0d))\n| where UserIdentityAccountId != \"anonymous\"\n| where EventSource startswith \"s3.\"\n| where EventName =~ \"GetObject\"\n| extend FilePath = tostring(parse_json(RequestParameters).key)\n| where FilePath has_any(BEC_Keywords)\n);\nEvents\n| summarize dcount(FilePath) by UserIdentityPrincipalid, bin(startofday(TimeGenerated), 1d)\n| summarize CountOfDocs = make_list(dcount_FilePath, 10000), TimeStamp = make_list(TimeGenerated, 10000) by UserIdentityPrincipalid\n| extend (Anomalies, Score, Baseline) = series_decompose_anomalies(CountOfDocs, sensitivity, -1, 'linefit')\n| mv-expand CountOfDocs to typeof(double), TimeStamp to typeof(datetime), Anomalies to typeof(double),Score to typeof(double), Baseline to typeof(long)\n| where Anomalies > 0\n| project TimeStamp, CountOfDocs, Baseline, Score, Anomalies, UserIdentityPrincipalid\n| join kind=inner(Events | extend TimeStamp = startofday(TimeGenerated)) on TimeStamp, UserIdentityPrincipalid\n| extend Name = iif(UserIdentityUserName contains \"@\", split(UserIdentityUserName, \"@\")[0], UserIdentityUserName)\n| extend UPNSuffix = iif(UserIdentityUserName contains \"@\", split(UserIdentityUserName, \"@\")[1], \"\")\n| project-reorder TimeGenerated, UserIdentityType, UserIdentityPrincipalid, UserIdentityUserName, FilePath, EventName, UserAgent, SourceIpAddress, CountOfDocs, Baseline, Score\n",
"queryFrequency": "P1D",
"queryPeriod": "P14D",
"severity": "Medium",
"suppressionDuration": "PT1H",
"suppressionEnabled": false,
"tactics": [
"Collection"
],
"techniques": [
"T1530"
],
"templateVersion": "1.0.3",
"triggerOperator": "GreaterThan",
"triggerThreshold": 0
},
"type": "Microsoft.OperationalInsights/workspaces/providers/alertRules"
}
]
}