Performing Log Analysis for Forensic Investigation

When to Use

• When reconstructing the timeline of a security incident from available log sources
• During post-breach investigation to identify initial access, lateral movement, and exfiltration
• When correlating events across multiple systems and log sources
• For establishing evidence of unauthorized access or policy violations
• When preparing forensic reports requiring detailed event chronology

Prerequisites

• Access to collected log files (Windows Event Logs, syslog, application logs)
• Log parsing tools (LogParser, jq, awk, or ELK stack)
• Understanding of log formats (EVTX, syslog, JSON, CSV)
• NTP-synchronized timestamps across all log sources for correlation
• Sufficient storage for log aggregation and indexing
• Timeline analysis tools (log2timeline, Plaso)

Workflow

Step 1: Collect and Preserve Log Sources

# Create case log directory structure
mkdir -p /cases/case-2024-001/logs/{windows,linux,network,application,web}

# Extract Windows Event Logs from forensic image
cp /mnt/evidence/Windows/System32/winevt/Logs/*.evtx /cases/case-2024-001/logs/windows/

# Key Windows Event Logs to collect
# Security.evtx - Authentication, access control, policy changes
# System.evtx - Service starts/stops, driver loads, system errors
# Application.evtx - Application errors and events
# Microsoft-Windows-PowerShell%4Operational.evtx - PowerShell execution
# Microsoft-Windows-Sysmon%4Operational.evtx - Sysmon detailed events
# Microsoft-Windows-TaskScheduler%4Operational.evtx - Scheduled tasks
# Microsoft-Windows-TerminalServices-LocalSessionManager%4Operational.evtx - RDP

# Collect Linux logs
cp /mnt/evidence/var/log/auth.log* /cases/case-2024-001/logs/linux/
cp /mnt/evidence/var/log/syslog* /cases/case-2024-001/logs/linux/
cp /mnt/evidence/var/log/kern.log* /cases/case-2024-001/logs/linux/
cp /mnt/evidence/var/log/secure* /cases/case-2024-001/logs/linux/
cp /mnt/evidence/var/log/audit/audit.log* /cases/case-2024-001/logs/linux/

# Collect web server logs
cp /mnt/evidence/var/log/apache2/access.log* /cases/case-2024-001/logs/web/
cp /mnt/evidence/var/log/nginx/access.log* /cases/case-2024-001/logs/web/

# Hash all collected logs for integrity
find /cases/case-2024-001/logs/ -type f -exec sha256sum {} \; > /cases/case-2024-001/logs/log_hashes.txt

Step 2: Parse Windows Event Logs

# 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

Step 3: Parse and Analyze Linux/Syslog Entries

# Parse auth.log for SSH and sudo events
grep -E '(sshd|sudo|su\[|passwd|useradd|usermod)' \
   /cases/case-2024-001/logs/linux/auth.log* | \
   sort > /cases/case-2024-001/analysis/auth_events.txt

# Extract failed SSH login attempts
grep 'Failed password' /cases/case-2024-001/logs/linux/auth.log* | \
   awk '{print $1,$2,$3,$9,$11}' | sort | uniq -c | sort -rn \
   > /cases/case-2024-001/analysis/failed_ssh.txt

# Extract successful SSH logins
grep 'Accepted' /cases/case-2024-001/logs/linux/auth.log* | \
   awk '{print $1,$2,$3,$9,$11}' > /cases/case-2024-001/analysis/successful_ssh.txt

# Parse audit logs for file access and command execution
ausearch -if /cases/case-2024-001/logs/linux/audit.log \
   --start 2024-01-15 --end 2024-01-20 \
   -m EXECVE > /cases/case-2024-001/analysis/audit_commands.txt

ausearch -if /cases/case-2024-001/logs/linux/audit.log \
   -m USER_AUTH,USER_LOGIN,USER_CMD \
   > /cases/case-2024-001/analysis/audit_auth.txt

# Parse web access logs for suspicious requests
cat /cases/case-2024-001/logs/web/access.log* | \
   grep -iE '(union.*select|<script|\.\.\/|cmd\.exe|/etc/passwd)' \
   > /cases/case-2024-001/analysis/web_attacks.txt

# Extract unique IP addresses from web logs
awk '{print $1}' /cases/case-2024-001/logs/web/access.log* | \
   sort | uniq -c | sort -rn > /cases/case-2024-001/analysis/web_ips.txt

Step 4: Correlate Events Across Sources

# 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 report
cat << '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

Verification Criteria

Confirm successful execution by validating:

• [ ] All prerequisite tools and access requirements are satisfied
• [ ] Each workflow step completed without errors
• [ ] Output matches expected format and contains expected data
• [ ] No security warnings or misconfigurations detected
• [ ] Results are documented and evidence is preserved for audit

Compliance Framework Mapping

This skill supports compliance evidence collection across multiple frameworks:

• SOC 2: CC7.3 (Incident Identification), CC7.4 (Incident Response)
• ISO 27001: A.16.1 (Security Incident Management), A.12.4 (Logging)
• NIST 800-53: AU-6 (Audit Review), IR-4 (Incident Handling), AU-9 (Audit Protection)
• NIST CSF: RS.AN (Analysis), RS.RP (Response Planning)

Claw GRC Tip: When this skill is executed by a registered agent, compliance evidence is automatically captured and mapped to the relevant controls in your active frameworks.

Deploying This Skill with Claw GRC

Agent Execution

Register this skill with your Claw GRC agent for automated execution:

# Install via CLI
npx claw-grc skills add performing-log-analysis-for-forensic-investigation

# Or load dynamically via MCP
grc.load_skill("performing-log-analysis-for-forensic-investigation")

Audit Trail Integration

When executed through Claw GRC, every step of this skill generates tamper-evident audit records:

• SHA-256 chain hashing ensures no step can be modified after execution
• Evidence artifacts (configs, scan results, logs) are automatically attached to relevant controls
• Trust score impact — successful execution increases your agent's trust score

Continuous Compliance

Schedule this skill for recurring execution to maintain continuous compliance posture. Claw GRC monitors for drift and alerts when re-execution is needed.