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# Install python-evtx for EVTX parsing
pip install python-evtx
# Convert EVTX to XML/JSON for analysis
python3 -c "
import Evtx.Evtx as evtx
import json, xml.etree.ElementTree as ET
with evtx.Evtx('/cases/case-2024-001/logs/windows/Security.evtx') as log:
for record in log.records():
print(record.xml())
" > /cases/case-2024-001/logs/windows/Security_parsed.xml
# Using evtxexport (libevtx-utils)sudo apt-get install libevtx-utils
evtxexport /cases/case-2024-001/logs/windows/Security.evtx \
> /cases/case-2024-001/logs/windows/Security_exported.txt
# Key Security Event IDs to investigate# 4624 - Successful logon# 4625 - Failed logon# 4648 - Logon using explicit credentials (runas, lateral movement)# 4672 - Special privileges assigned (admin logon)# 4688 - Process creation (with command line if auditing enabled)# 4697 - Service installed# 4698/4702 - Scheduled task created/updated# 4720 - User account created# 4732 - Member added to security-enabled local group# 1102 - Audit log cleared# Extract specific events with python-evtx
python3 << 'PYEOF'
import Evtx.Evtx as evtx
import xml.etree.ElementTree as ET
target_events = ['4624', '4625', '4648', '4672', '4688', '4697', '1102']
with evtx.Evtx('/cases/case-2024-001/logs/windows/Security.evtx') as log:
for record in log.records():
root = ET.fromstring(record.xml())
ns = {'ns': 'http://schemas.microsoft.com/win/2004/08/events/event'}
event_id = root.find('.//ns:EventID', ns).text
if event_id in target_events:
time = root.find('.//ns:TimeCreated', ns).get('SystemTime')
print(f"[{time}] EventID: {event_id}")
for data in root.findall('.//ns:Data', ns):
print(f" {data.get('Name')}: {data.text}")
print()
PYEOF
# Normalize timestamps and merge log sources
python3 << 'PYEOF'
import csv
import datetime
from collections import defaultdict
events = []
# Parse Windows Security events (pre-exported to CSV)
with open('/cases/case-2024-001/analysis/windows_events.csv') as f:
reader = csv.DictReader(f)
for row in reader:
events.append({
'timestamp': row['TimeCreated'],
'source': 'Windows-Security',
'event_id': row['EventID'],
'description': row['Description'],
'details': row.get('Details', '')
})
# Parse Linux auth events
with open('/cases/case-2024-001/analysis/auth_events.txt') as f:
for line in f:
parts = line.strip().split()
if len(parts) >= 6:
events.append({
'timestamp': ' '.join(parts[:3]),
'source': 'Linux-Auth',
'event_id': parts[4].rstrip(':'),
'description': ' '.join(parts[5:]),
'details': ''
})
# Sort by timestamp
events.sort(key=lambda x: x['timestamp'])
# Write correlated timeline
with open('/cases/case-2024-001/analysis/correlated_timeline.csv', 'w', newline='') as f:
writer = csv.DictWriter(f, fieldnames=['timestamp', 'source', 'event_id', 'description', 'details'])
writer.writeheader()
writer.writerows(events)
print(f"Total correlated events: {len(events)}")
PYEOF
# Quick correlation: find events within time windows# Look for lateral movement patterns
grep "4648\|4624.*Type.*3\|4624.*Type.*10" /cases/case-2024-001/analysis/windows_events.csv | \
sort > /cases/case-2024-001/analysis/lateral_movement.txt
Step 5: Generate Forensic Timeline Report
# Create structured investigation reportcat << 'REPORT' > /cases/case-2024-001/analysis/log_analysis_report.txt
LOG ANALYSIS FORENSIC REPORT
=============================
Case: 2024-001
Analyst: [Examiner Name]
Date: $(date -u)
LOG SOURCES ANALYZED:
- Windows Security Event Log (Security.evtx) - 245,678 events
- Windows System Event Log (System.evtx) - 45,234 events
- Windows PowerShell Operational - 12,456 events
- Linux auth.log - 34,567 entries
- Apache access.log - 567,890 entries
- Linux audit.log - 89,012 entries
KEY FINDINGS:
1. Initial Access: [timestamp] - Successful RDP login from external IP
2. Privilege Escalation: [timestamp] - New admin account created
3. Lateral Movement: [timestamp] - Pass-the-hash detected across 3 systems
4. Data Exfiltration: [timestamp] - Large data transfer to external IP
5. Log Tampering: [timestamp] - Security event log cleared (Event 1102)
TIMELINE OF EVENTS:
[See correlated_timeline.csv for complete chronology]
REPORT
# Package analysis artifacts
tar -czf /cases/case-2024-001/log_analysis_package.tar.gz \
/cases/case-2024-001/analysis/
Key Concepts
Concept
Description
Event correlation
Linking related events across multiple log sources by time, IP, user, or session
Log normalization
Converting diverse log formats into a common schema for unified analysis
Timeline analysis
Chronological ordering of events to reconstruct incident sequence
Log integrity
Verifying logs have not been tampered with using hashes and chain of custody
Logon types
Windows categorization of authentication methods (2=interactive, 3=network, 10=RDP)
Audit policy
System configuration determining which events are recorded in logs
Log rotation
Automatic archiving of log files that affects evidence availability
Anti-forensics
Attacker techniques for clearing or modifying logs to cover tracks
Tools & Systems
Tool
Purpose
python-evtx
Python library for parsing Windows EVTX event log files
evtxexport
Command-line EVTX export utility from libevtx
LogParser
Microsoft SQL-like query engine for Windows logs
ausearch
Linux audit log search utility
jq
JSON query tool for parsing structured log formats
ELK Stack
Elasticsearch, Logstash, Kibana for log aggregation and visualization
Chainsaw
Sigma-based Windows Event Log analysis tool
Hayabusa
Fast Windows Event Log forensic timeline generator
Common Scenarios
Scenario 1: Brute Force Attack Detection
Filter Security.evtx for Event ID 4625 (failed logons), group by source IP and target account, identify patterns of rapid successive failures, find the successful logon (4624) that followed, trace subsequent activity from the compromised account.
Scenario 2: Insider Threat Investigation
Collect all log sources from the suspect's workstation and accessed servers, correlate file access events with authentication events, build timeline of data access during non-business hours, identify data transfers to external media or cloud storage.
Scenario 3: Web Application Compromise
Parse web server access logs for SQLi, XSS, and path traversal patterns, identify the attack IP and timeline, correlate with application logs for successful exploitation, trace post-exploitation activity through system and auth logs.
Scenario 4: Ransomware Incident Timeline
Identify the initial execution through process creation events (4688), trace privilege escalation through service installation (4697), map lateral movement via network logons (4624 Type 3), identify encryption start from file system activity, find the earliest IoC for remediation scoping.
Output Format
Log Analysis Summary:
Investigation Period: 2024-01-15 00:00 to 2024-01-20 23:59 UTC
Total Events Analyzed: 894,567
Log Sources: 6 (3 Windows, 3 Linux)
Critical Events:
Failed Logons: 1,234 (from 5 unique IPs)
Successful Logons: 456 (3 anomalous)
Account Changes: 12 (1 unauthorized admin creation)
Process Creations: 8,234 (15 suspicious)
Log Clearings: 2 (Security log cleared at 2024-01-18 03:00 UTC)
Service Installs: 3 (1 unknown service)
Attack Timeline:
2024-01-15 14:32 - Initial access via RDP brute force
2024-01-15 14:45 - Admin account "svcbackup" created
2024-01-16 02:15 - Lateral movement to 3 servers
2024-01-17 03:00 - Data staging in C:\ProgramData\temp\
2024-01-18 01:30 - 4.2 GB exfiltrated to 185.x.x.x
2024-01-18 03:00 - Security logs cleared
Report: /cases/case-2024-001/analysis/log_analysis_report.txt
