performing-log-analysis-for-forensic-investigation▌
mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026
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Collect, parse, and correlate system, application, and security logs to reconstruct events and establish timelines during forensic investigations.
| name | performing-log-analysis-for-forensic-investigation |
| description | Collect, parse, and correlate system, application, and security logs to reconstruct events and establish timelines during forensic investigations. |
| domain | cybersecurity |
| subdomain | digital-forensics |
| tags | - forensics - log-analysis - siem - event-correlation - timeline-analysis - evidence-collection |
| version | '1.0' |
| author | mahipal |
| license | Apache-2.0 |
| nist_csf | - RS.AN-01 - RS.AN-03 - DE.AE-02 - RS.MA-01 |
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
How to use performing-log-analysis-for-forensic-investigation on Cursor
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Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add performing-log-analysis-for-forensic-investigation
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches performing-log-analysis-for-forensic-investigation from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate performing-log-analysis-for-forensic-investigation. Access the skill through slash commands (e.g., /performing-log-analysis-for-forensic-investigation) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
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Use Cases▌
Exploratory Data Analysis
Quickly understand datasets, identify patterns, and generate insights
Example
Analyze CSV with 100K rows, identify outliers, visualize correlations, suggest hypotheses
Reduce EDA time from hours to minutes, uncover insights faster
Data Cleaning & Transformation
Write scripts to clean messy data, handle missing values, normalize formats
Example
Generate Python/SQL to fix date formats, impute missing values, remove duplicates
Automate 80% of data preprocessing work
Statistical Analysis
Perform hypothesis testing, regression, and statistical modeling
Example
Run A/B test analysis, calculate confidence intervals, interpret p-values
Get statistically sound analysis without PhD in statistics
Data Visualization
Create charts, dashboards, and visual reports
Example
Generate matplotlib/seaborn code for time series plots, distribution charts, heatmaps
Build presentation-ready visualizations 3x faster
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Python environment (pandas, numpy, matplotlib) or SQL database access
- ›Basic understanding of data analysis concepts
- ›Sample datasets for testing skill capabilities
Time Estimate
20-40 minutes to set up and run first analysis
Installation Steps
- 1.Install data analysis skill using provided command
- 2.Prepare a sample dataset (CSV, JSON, or database connection)
- 3.Start with descriptive statistics: 'Summarize this dataset'
- 4.Progress to visualization: 'Create a scatter plot of X vs Y'
- 5.Advanced analysis: 'Run linear regression and interpret results'
- 6.Validate outputs: check calculations, verify visualizations make sense
- 7.Document analysis workflow for reproducibility
Common Pitfalls
- ⚠Not validating statistical assumptions before applying tests
- ⚠Accepting visualizations without checking data accuracy
- ⚠Overlooking data quality issues (missing values, outliers)
- ⚠Misinterpreting correlation as causation
- ⚠Using wrong statistical test for data distribution
- ⚠Not considering sample size and statistical power
Best Practices▌
✓ Do
- +Always validate data quality before analysis
- +Check statistical assumptions (normality, independence, etc.)
- +Visualize data before running statistical tests
- +Document analysis steps for reproducibility
- +Cross-validate findings with domain experts
- +Use skill for initial exploration, then dive deeper manually
- +Save generated code for reuse on similar datasets
✗ Don't
- −Don't trust analysis without verifying data quality
- −Don't apply statistical tests without checking assumptions
- −Don't make business decisions solely on AI-generated analysis
- −Don't ignore outliers without investigating cause
- −Don't skip data validation and sanity checks
- −Don't use for mission-critical financial or medical analysis without expert review
💡 Pro Tips
- ★Describe data context: 'This is user behavior data from e-commerce site'
- ★Ask for interpretation: 'What does this correlation mean for business?'
- ★Request multiple approaches: 'Show 3 ways to handle missing data'
- ★Combine AI analysis with domain expertise for best insights
- ★Use for rapid prototyping, then refine analysis manually
When to Use This▌
✓ Use When
Use for exploratory data analysis, data cleaning, statistical testing, visualization prototyping, and learning new analysis techniques. Best for initial exploration and rapid insights.
✗ Avoid When
Avoid for mission-critical financial analysis, medical research requiring regulatory compliance, production ML models, or when deep statistical expertise is required for nuanced interpretation.
Learning Path▌
- 1Basic: descriptive statistics, data cleaning, simple visualizations
- 2Intermediate: hypothesis testing, regression, correlation analysis
- 3Advanced: time series analysis, clustering, predictive modeling
- 4Expert: causal inference, experimental design, advanced statistical methods
Discussion
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Ratings
4.7★★★★★44 reviews- ★★★★★Ira Mehta· Dec 28, 2024
performing-log-analysis-for-forensic-investigation is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Carlos Thomas· Dec 12, 2024
performing-log-analysis-for-forensic-investigation fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Amelia Abbas· Dec 12, 2024
performing-log-analysis-for-forensic-investigation reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Pratham Ware· Dec 4, 2024
I recommend performing-log-analysis-for-forensic-investigation for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Sakshi Patil· Nov 23, 2024
Useful defaults in performing-log-analysis-for-forensic-investigation — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Zaid Dixit· Nov 19, 2024
Solid pick for teams standardizing on skills: performing-log-analysis-for-forensic-investigation is focused, and the summary matches what you get after install.
- ★★★★★Hassan Bansal· Nov 15, 2024
Keeps context tight: performing-log-analysis-for-forensic-investigation is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Carlos Rao· Nov 3, 2024
We added performing-log-analysis-for-forensic-investigation from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Carlos Reddy· Nov 3, 2024
performing-log-analysis-for-forensic-investigation has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Naina Jackson· Oct 22, 2024
Solid pick for teams standardizing on skills: performing-log-analysis-for-forensic-investigation is focused, and the summary matches what you get after install.
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