Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.
日本語の概要は準備中です。原文の説明を表示しています。
Detecting data exfiltration attempts from AWS S3 buckets by analyzing CloudTrail S3 data events, VPC Flow Logs, GuardDuty findings, Amazon Macie alerts, and S3 access patterns to identify unauthorized bulk downloads and cross-account data transfers.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Do not use for preventing data exfiltration (use S3 bucket policies, VPC endpoints, and SCPs), for data classification (use Amazon Macie discovery jobs), or for network-level exfiltration detection (use VPC Flow Logs with network analysis tools).
GetObject, PutObject, CopyObject)Configure CloudTrail to capture all S3 object-level operations for forensic analysis.
# Enable S3 data events on an existing trail
aws cloudtrail put-event-selectors \
--trail-name management-trail \
--event-selectors '[{
"ReadWriteType": "All",
"IncludeManagementEvents": true,
"DataResources": [{
"Type": "AWS::S3::Object",
"Values": ["arn:aws:s3:::sensitive-data-bucket/", "arn:aws:s3:::customer-records/"]
}]
}]'
# Verify data event configuration
aws cloudtrail get-event-selectors --trail-name management-trail \
--query 'EventSelectors[*].DataResources' --output json
# Enable GuardDuty S3 Protection
aws guardduty update-detector \
--detector-id $(aws guardduty list-detectors --query 'DetectorIds[0]' --output text) \
--data-sources '{"S3Logs":{"Enable":true}}'
Analyze CloudTrail logs for bulk download activity, unusual access times, and unfamiliar source IPs.
# Athena query: Top S3 downloaders by volume in last 24 hours
cat << 'EOF'
SELECT
useridentity.arn as principal,
sourceipaddress,
COUNT(*) as request_count,
SUM(CAST(json_extract_scalar(requestparameters, '$.bytesTransferredOut') AS bigint)) as bytes_downloaded
FROM cloudtrail_logs
WHERE eventname = 'GetObject'
AND eventsource = 's3.amazonaws.com'
AND eventtime > date_add('hour', -24, now())
GROUP BY useridentity.arn, sourceipaddress
ORDER BY request_count DESC
LIMIT 50
EOF
# CloudWatch Logs Insights: S3 GetObject requests from unusual IPs
aws logs start-query \
--log-group-name cloudtrail-logs \
--start-time $(date -d "24 hours ago" +%s) \
--end-time $(date +%s) \
--query-string '
fields @timestamp, userIdentity.arn, sourceIPAddress, requestParameters.bucketName, requestParameters.key
| filter eventName = "GetObject"
| stats count() as requestCount by sourceIPAddress, userIdentity.arn
| sort requestCount desc
| limit 25
'
# Detect cross-account copies (potential exfiltration)
aws logs start-query \
--log-group-name cloudtrail-logs \
--start-time $(date -d "7 days ago" +%s) \
--end-time $(date +%s) \
--query-string '
fields @timestamp, userIdentity.arn, sourceIPAddress, requestParameters.bucketName
| filter eventName in ["CopyObject", "ReplicateObject", "UploadPart"]
| filter userIdentity.accountId != "OUR_ACCOUNT_ID"
| sort @timestamp desc
| limit 100
'
Check for GuardDuty S3-specific finding types that indicate exfiltration activity.
# List active S3 exfiltration-related findings
aws guardduty list-findings \
--detector-id $(aws guardduty list-detectors --query 'DetectorIds[0]' --output text) \
--finding-criteria '{
"Criterion": {
"type": {
"Eq": [
"Exfiltration:S3/MaliciousIPCaller",
"Exfiltration:S3/ObjectRead.Unusual",
"Discovery:S3/MaliciousIPCaller.Custom",
"Discovery:S3/BucketEnumeration.Unusual",
"UnauthorizedAccess:S3/MaliciousIPCaller.Custom",
"UnauthorizedAccess:S3/TorIPCaller",
"Impact:S3/AnomalousBehavior.Delete"
]
}
}
}' --output json
# Get detailed finding information
aws guardduty get-findings \
--detector-id $(aws guardduty list-detectors --query 'DetectorIds[0]' --output text) \
--finding-ids FINDING_IDS \
--query 'Findings[*].{Type:Type,Severity:Severity,Resource:Resource.S3BucketDetails[0].Name,Action:Service.Action}' \
--output table
Review Macie findings to correlate data sensitivity with access anomalies.
# List Macie findings for sensitive data exposure
aws macie2 list-findings \
--finding-criteria '{
"criterion": {
"category": {"eq": ["CLASSIFICATION"]},
"severity.description": {"eq": ["High", "Critical"]}
}
}' \
--sort-criteria '{"attributeName": "updatedAt", "orderBy": "DESC"}' \
--max-results 25
# Get detailed finding with data classification
aws macie2 get-findings \
--finding-ids FINDING_IDS \
--query 'findings[*].{Type:type,Severity:severity.description,Bucket:resourcesAffected.s3Bucket.name,SensitiveDataTypes:classificationDetails.result.sensitiveData[*].category}' \
--output table
# Run a sensitive data discovery job on target bucket
aws macie2 create-classification-job \
--job-type ONE_TIME \
--name "exfiltration-investigation" \
--s3-job-definition '{
"bucketDefinitions": [{
"accountId": "ACCOUNT_ID",
"buckets": ["sensitive-data-bucket"]
}]
}'
Create CloudWatch alarms and EventBridge rules for real-time exfiltration detection.
# CloudWatch metric filter for high-volume S3 downloads
aws logs put-metric-filter \
--log-group-name cloudtrail-logs \
--filter-name s3-bulk-download \
--filter-pattern '{$.eventName = "GetObject" && $.eventSource = "s3.amazonaws.com"}' \
--metric-transformations '[{
"metricName": "S3GetObjectCount",
"metricNamespace": "SecurityMetrics",
"metricValue": "1",
"defaultValue": 0
}]'
# Alarm for anomalous download volume (>1000 objects/hour)
aws cloudwatch put-metric-alarm \
--alarm-name s3-exfiltration-alert \
--metric-name S3GetObjectCount \
--namespace SecurityMetrics \
--statistic Sum \
--period 3600 \
--threshold 1000 \
--comparison-operator GreaterThanThreshold \
--evaluation-periods 1 \
--alarm-actions arn:aws:sns:us-east-1:ACCOUNT:security-alerts
# EventBridge rule for GuardDuty S3 findings
aws events put-rule \
--name guardduty-s3-exfiltration \
--event-pattern '{
"source": ["aws.guardduty"],
"detail-type": ["GuardDuty Finding"],
"detail": {
"type": [{"prefix": "Exfiltration:S3/"}]
}
}'
Deploy bucket policies and VPC endpoint policies to restrict data movement paths.
# VPC endpoint policy restricting S3 access to specific buckets
aws ec2 modify-vpc-endpoint \
--vpc-endpoint-id vpce-ENDPOINT_ID \
--policy-document '{
"Statement": [{
"Sid": "RestrictToOwnBuckets",
"Effect": "Allow",
"Principal": "*",
"Action": ["s3:GetObject", "s3:PutObject"],
"Resource": ["arn:aws:s3:::approved-bucket-1/*", "arn:aws:s3:::approved-bucket-2/*"]
}]
}'
# Bucket policy denying access from outside the VPC
aws s3api put-bucket-policy --bucket sensitive-data-bucket --policy '{
"Version": "2012-10-17",
"Statement": [{
"Sid": "DenyNonVpcAccess",
"Effect": "Deny",
"Principal": "*",
"Action": "s3:GetObject",
"Resource": "arn:aws:s3:::sensitive-data-bucket/*",
"Condition": {
"StringNotEquals": {
"aws:sourceVpce": "vpce-ENDPOINT_ID"
}
}
}]
}'
| Term | Definition |
|---|---|
| S3 Data Events | CloudTrail object-level logging that captures GetObject, PutObject, DeleteObject, and CopyObject API calls with request details |
| GuardDuty S3 Protection | Threat detection feature analyzing CloudTrail S3 data events to identify anomalous access patterns and exfiltration attempts |
| Amazon Macie | Data security service that discovers and classifies sensitive data in S3 and generates findings for data exposure risks |
| VPC Endpoint Policy | Access control policy on an S3 VPC endpoint that restricts which buckets and actions can be accessed through the endpoint |
| Data Exfiltration | Unauthorized transfer of data from an organization's S3 storage to an external location controlled by an attacker |
| Anomalous Behavior Detection | Machine learning-based identification of S3 access patterns that deviate from established baselines for a principal |
Context: GuardDuty reports an Exfiltration:S3/ObjectRead.Unusual finding indicating that a developer's access key is downloading thousands of objects from a sensitive data bucket at 3 AM from an IP address in a foreign country.
Approach:
Pitfalls: CloudTrail S3 data events can generate massive log volume. Use Athena with partitioned tables rather than CloudWatch Logs Insights for queries spanning more than 24 hours. GuardDuty baseline learning requires 7-14 days, so new accounts may generate false positives for normal access patterns.
S3 Data Exfiltration Investigation Report
============================================
Account: 123456789012
Detection Source: GuardDuty Exfiltration:S3/ObjectRead.Unusual
Investigation Date: 2026-02-23
INCIDENT TIMELINE:
2026-02-23 02:47 UTC - First anomalous GetObject from 185.x.x.x
2026-02-23 02:47-04:12 UTC - 12,847 GetObject requests
2026-02-23 04:15 UTC - GuardDuty finding generated
2026-02-23 04:20 UTC - PagerDuty alert received by SOC
2026-02-23 04:25 UTC - Access key deactivated
COMPROMISED PRINCIPAL:
ARN: arn:aws:iam::123456789012:user/developer-jane
Access Key: AKIA...WXYZ
Source IP: 185.x.x.x (Tor exit node)
DATA IMPACT ASSESSMENT:
Buckets accessed: 3
Objects downloaded: 12,847
Total data volume: 4.7 GB
Sensitive data types: PII (SSN, email), Financial (credit card)
Macie severity: CRITICAL
CONTAINMENT ACTIONS:
[x] Access key deactivated
[x] User password reset and MFA re-enrolled
[x] VPC endpoint policy applied to sensitive buckets
[x] Bucket policy restricting to VPC-only access
[x] TruffleHog scan initiated on developer repositories
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.
日本語の概要は準備中です。原文の説明を表示しています。
Detect dangerous ACL misconfigurations in Active Directory using ldap3 to identify GenericAll, WriteDACL, and WriteOwner abuse paths
日本語の概要は準備中です。原文の説明を表示しています。
Perform static analysis of Android APK malware samples using apktool for decompilation, jadx for Java source recovery, and androguard for permission analysis, manifest inspection, and suspicious API call detection.
日本語の概要は準備中です。原文の説明を表示しています。
Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect BOLA/IDOR attacks, rate limit bypass, credential scanning, and injection attempts. Uses pandas for statistical analysis of request patterns and anomaly detection. Use when investigating API abuse or building API-specific threat detection rules.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze advanced persistent threat (APT) group techniques using MITRE ATT&CK Navigator to create layered heatmaps of adversary TTPs for detection gap analysis and threat-informed defense.
日本語の概要は準備中です。原文の説明を表示しています。
Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query to detect suspicious administrative operations, impossible travel, privilege escalation, and resource modifications. Builds KQL queries for threat hunting in Azure environments. Use when investigating suspicious Azure tenant activity or building cloud SIEM detections.
日本語の概要は準備中です。原文の説明を表示しています。