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# === AWS CloudTrail Logs ===# Download CloudTrail events for the investigation period
aws cloudtrail lookup-events \
--start-time "2024-01-15T00:00:00Z" \
--end-time "2024-01-20T23:59:59Z" \
--max-results 1000 \
> /cases/case-2024-001/cloud/cloudtrail_events.json
# Filter for specific user activity
aws cloudtrail lookup-events \
--lookup-attributes AttributeKey=Username,AttributeValue=compromised-user \
--start-time "2024-01-15T00:00:00Z" \
> /cases/case-2024-001/cloud/user_activity.json
# Download S3 access logs
aws s3 sync s3://my-cloudtrail-bucket/AWSLogs/ /cases/case-2024-001/cloud/cloudtrail_s3/
# Query CloudTrail with Athena for large-scale analysis
aws athena start-query-execution \
--query-string "SELECT eventTime, eventName, userIdentity.arn, sourceIPAddress, errorCode
FROM cloudtrail_logs
WHERE eventTime BETWEEN '2024-01-15' AND '2024-01-20'
AND sourceIPAddress NOT IN ('10.0.0.0/8')
ORDER BY eventTime" \
--result-configuration OutputLocation=s3://forensics-bucket/athena-results/
# === AWS VPC Flow Logs ===
aws logs filter-log-events \
--log-group-name "vpc-flow-logs" \
--start-time $(date -d "2024-01-15" +%s000) \
--end-time $(date -d "2024-01-20" +%s000) \
--filter-pattern "ACCEPT" \
> /cases/case-2024-001/cloud/vpc_flow_logs.json
# === Azure Activity Log ===
az monitor activity-log list \
--start-time "2024-01-15T00:00:00Z" \
--end-time "2024-01-20T23:59:59Z" \
--output json > /cases/case-2024-001/cloud/azure_activity.json
# === GCP Audit Logs ===
gcloud logging read'logName="projects/PROJECT_ID/logs/cloudaudit.googleapis.com%2Factivity"
AND timestamp>="2024-01-15T00:00:00Z"
AND timestamp<="2024-01-20T23:59:59Z"' \
--format=json > /cases/case-2024-001/cloud/gcp_audit.json
Step 3: Analyze IAM and Access Patterns
# Analyze compromised credentials usage
python3 << 'PYEOF'
import json
from collections import defaultdict
with open('/cases/case-2024-001/cloud/cloudtrail_events.json') as f:
data = json.load(f)
# Analyze by source IP
ip_events = defaultdict(list)
error_events = []
critical_actions = []
for event in data.get('Events', []):
ct = json.loads(event.get('CloudTrailEvent', '{}'))
source_ip = ct.get('sourceIPAddress', 'Unknown')
event_name = ct.get('eventName', 'Unknown')
user_arn = ct.get('userIdentity', {}).get('arn', 'Unknown')
error = ct.get('errorCode')
timestamp = ct.get('eventTime', '')
ip_events[source_ip].append(event_name)
if error:
error_events.append({'time': timestamp, 'action': event_name, 'error': error, 'ip': source_ip})
# Flag critical actions
critical = ['CreateUser', 'CreateAccessKey', 'AttachUserPolicy', 'CreateRole',
'PutBucketPolicy', 'StopLogging', 'DeleteTrail', 'CreateKeyPair',
'RunInstances', 'AuthorizeSecurityGroupIngress']
if event_name in critical:
critical_actions.append({'time': timestamp, 'action': event_name, 'user': user_arn, 'ip': source_ip})
print("=== SOURCE IP ANALYSIS ===")
for ip, events in sorted(ip_events.items(), key=lambda x: len(x[1]), reverse=True):
print(f" {ip}: {len(events)} events ({len(set(events))} unique actions)")
print(f"\n=== ACCESS ERRORS ({len(error_events)} total) ===")
for e in error_events[:10]:
print(f" [{e['time']}] {e['action']} -> {e['error']} from {e['ip']}")
print(f"\n=== CRITICAL ACTIONS ({len(critical_actions)} total) ===")
for a in critical_actions:
print(f" [{a['time']}] {a['action']} by {a['user']} from {a['ip']}")
PYEOF
Step 4: Acquire and Analyze VM Disk Image
# Create a forensic analysis instance from the snapshot
SNAPSHOT_ID="snap-0abc123def456789"# Create volume from snapshot in isolated forensic VPC
FORENSIC_VOL=$(aws ec2 create-volume --snapshot-id $SNAPSHOT_ID \
--availability-zone us-east-1a \
--tag-specifications "ResourceType=volume,Tags=[{Key=Case,Value=2024-001}]" \
--query 'VolumeId' --output text)
# Attach to forensic analysis instance (read-only mount)
aws ec2 attach-volume --volume-id $FORENSIC_VOL \
--instance-id i-forensic-workstation \
--device /dev/xvdf
# On the forensic instance, mount read-onlysudo mount -o ro /dev/xvdf1 /mnt/evidence
# Perform standard disk forensics on the mounted volume# Extract logs, analyze file system, check for persistencels /mnt/evidence/var/log/
cp -r /mnt/evidence/var/log/ /cases/case-2024-001/cloud/vm_logs/
cp -r /mnt/evidence/etc/crontab /cases/case-2024-001/cloud/persistence/
cp -r /mnt/evidence/home/*/.ssh/ /cases/case-2024-001/cloud/ssh_keys/
cp -r /mnt/evidence/home/*/.bash_history /cases/case-2024-001/cloud/bash_history/
Step 5: Generate Cloud Forensics Report
# Compile findings into structured report
python3 << 'PYEOF'
report = """
CLOUD FORENSICS INVESTIGATION REPORT
======================================
Case: 2024-001
Cloud Provider: AWS (Account: 123456789012)
Region: us-east-1
Investigation Period: 2024-01-15 to 2024-01-20
EVIDENCE PRESERVED:
- EC2 Instance Snapshot: snap-0abc123def456789 (i-0abc123def456789)
- CloudTrail Logs: 2024-01-15 to 2024-01-20
- VPC Flow Logs: 2024-01-15 to 2024-01-20
- Instance Metadata: captured and hashed
- Security Group Configuration: captured at time of isolation
FINDINGS:
1. Initial Access:
- Compromised IAM access key AKIA... used from IP 203.0.113.45
- First unauthorized API call: 2024-01-15 14:32:00 UTC
- IP geolocation: Foreign jurisdiction (not company IP range)
2. Persistence:
- New IAM user 'backup-admin' created with AdministratorAccess
- New access key pair generated for backup-admin
- SSH key added to EC2 instance authorized_keys
3. Lateral Movement:
- S3 bucket policies modified to allow public access
- Security group rules modified to allow SSH from 0.0.0.0/0
- 3 additional EC2 instances launched for crypto-mining
4. Data Exfiltration:
- S3 bucket 'company-confidential' accessed 234 times
- 12 GB of data downloaded via GetObject API calls
- Data transferred to external IP 185.x.x.x
5. Anti-Forensics:
- CloudTrail logging disabled at 2024-01-18 03:00 UTC
- CloudWatch log groups deleted
RECOMMENDATIONS:
- Rotate all IAM credentials immediately
- Enable MFA on all accounts
- Restore CloudTrail logging
- Review and restrict S3 bucket policies
- Implement GuardDuty for continuous monitoring
"""
with open('/cases/case-2024-001/cloud/cloud_forensics_report.txt', 'w') as f:
f.write(report)
print(report)
PYEOF
Key Concepts
Concept
Description
Cloud API logging
Service logs recording all API calls (CloudTrail, Activity Log, Audit Log)
Volume snapshots
Point-in-time copies of cloud disk volumes for forensic preservation
VPC Flow Logs
Network traffic metadata logs showing source, destination, and action
IAM credential compromise
Unauthorized use of access keys, tokens, or assumed roles
Instance metadata
EC2/VM configuration data including network, storage, and security settings
Shared responsibility
Cloud provider secures infrastructure; customer secures data and access
Evidence volatility
Cloud resources can be terminated; evidence must be preserved quickly
Multi-region artifacts
Attacks may span regions requiring cross-region log collection
Tools & Systems
Tool
Purpose
AWS CLI
Command-line interface for AWS service interaction and log collection
CloudTrail
AWS API call logging service for investigation and auditing
Azure Monitor
Azure logging and diagnostics platform
GCP Cloud Logging
Google Cloud audit and access logging service
Athena
AWS serverless SQL query service for analyzing CloudTrail logs at scale
Prowler
Open-source AWS security assessment and forensic collection tool
ScoutSuite
Multi-cloud security auditing tool
CADO Response
Cloud-native digital forensics and incident response platform
Common Scenarios
Scenario 1: Compromised IAM Access Keys
Identify the compromised key in CloudTrail, trace all API calls made with the key, determine the source IPs and actions taken, check for persistence mechanisms (new users, roles, keys), revoke the compromised credentials, assess data access scope.
Scenario 2: Cryptojacking on EC2 Instances
Detect unauthorized instance launches in CloudTrail, snapshot the mining instances for analysis, examine security group changes that allowed C2 communication, identify the initial access vector (stolen keys, SSRF), calculate resource costs incurred.
Scenario 3: S3 Data Breach
Analyze S3 access logs and CloudTrail for GetObject/PutBucketPolicy events, identify who modified bucket policies to allow public access, determine the scope of data exposure, check for data downloads from unauthorized IPs, assess regulatory reporting requirements.
Scenario 4: Container Escape in EKS/AKS/GKE
Collect Kubernetes audit logs and cloud provider logs, analyze pod creation events for privilege escalation attempts, examine node-level logs for container escape evidence, check for unauthorized access to cloud metadata service (169.254.169.254), trace lateral movement to cloud APIs.
Output Format
Cloud Forensics Summary:
Cloud: AWS (us-east-1) Account: 123456789012
Investigation: 2024-01-15 to 2024-01-20
Incident Type: IAM Credential Compromise + Data Exfiltration
Evidence Collected:
EBS Snapshots: 3 volumes preserved
CloudTrail Events: 12,456 (1,234 from attacker IP)
VPC Flow Logs: 45,678 records
S3 Access Logs: 2,345 entries
Attack Timeline:
2024-01-15 14:32 - Compromised access key first used from 203.0.113.45
2024-01-15 14:45 - New IAM user created with admin privileges
2024-01-16 02:00 - S3 bucket policy modified (public access enabled)
2024-01-16 03:00 - 12 GB downloaded from company-confidential bucket
2024-01-18 03:00 - CloudTrail logging disabled
Impact Assessment:
Data Exposed: 12 GB from 3 S3 buckets
Resources Created: 3 EC2 instances (crypto mining)
Estimated Cost: $4,500 in unauthorized compute
