performing-linux-log-forensics-investigation▌
mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026
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Perform forensic investigation of Linux system logs including syslog, auth.log, systemd journal, kern.log, and application logs to reconstruct user activity, detect unauthorized access, and establish event timelines on compromised Linux systems.
| name | performing-linux-log-forensics-investigation |
| description | Perform forensic investigation of Linux system logs including syslog, auth.log, systemd journal, kern.log, and application logs to reconstruct user activity, detect unauthorized access, and establish event timelines on compromised Linux systems. |
| domain | cybersecurity |
| subdomain | digital-forensics |
| tags | - linux-forensics - syslog - auth-log - systemd-journal - journalctl - linux-logs - ssh-forensics - cron - audit-log - log-analysis |
| version | '1.0' |
| author | mahipal |
| license | Apache-2.0 |
| nist_csf | - RS.AN-01 - RS.AN-03 - DE.AE-02 - RS.MA-01 |
Performing Linux Log Forensics Investigation
Overview
Linux systems maintain extensive logs that serve as primary evidence sources in forensic investigations. Unlike Windows Event Logs, Linux logs are typically plain-text files stored in /var/log/ and binary journal files managed by systemd-journald. Key forensic logs include auth.log (authentication events, sudo usage, SSH sessions), syslog (system-wide messages), kern.log (kernel events), and application-specific logs. The Linux Audit framework (auditd) provides detailed security event logging comparable to Windows Security Event Logs. Forensic analysis of these logs enables investigators to reconstruct user sessions, identify unauthorized access, detect privilege escalation, trace lateral movement, and establish comprehensive event timelines.
When to Use
- When conducting security assessments that involve performing linux log forensics investigation
- When following incident response procedures for related security events
- When performing scheduled security testing or auditing activities
- When validating security controls through hands-on testing
Prerequisites
- Familiarity with digital forensics concepts and tools
- Access to a test or lab environment for safe execution
- Python 3.8+ with required dependencies installed
- Appropriate authorization for any testing activities
Key Log Files and Locations
| Log File | Path | Contents |
|---|---|---|
| auth.log / secure | /var/log/auth.log (Debian) or /var/log/secure (RHEL) | Authentication, sudo, SSH, PAM |
| syslog / messages | /var/log/syslog (Debian) or /var/log/messages (RHEL) | General system messages |
| kern.log | /var/log/kern.log | Kernel messages, USB events, driver loads |
| lastlog | /var/log/lastlog | Last login per user (binary) |
| wtmp | /var/log/wtmp | Login/logout records (binary, read with last) |
| btmp | /var/log/btmp | Failed login attempts (binary, read with lastb) |
| faillog | /var/log/faillog | Failed login counter (binary) |
| cron.log | /var/log/cron or /var/log/syslog | Scheduled task execution |
| audit.log | /var/log/audit/audit.log | Linux Audit Framework events |
| journal | /var/log/journal/ or /run/log/journal/ | systemd binary journal |
| dpkg.log | /var/log/dpkg.log | Package installation/removal (Debian) |
| yum.log | /var/log/yum.log | Package installation/removal (RHEL) |
Analysis Techniques
Authentication Log Analysis
# Find all successful SSH logins
grep "Accepted" /var/log/auth.log
# Find failed SSH login attempts
grep "Failed password" /var/log/auth.log
# Extract unique source IPs from failed logins
grep "Failed password" /var/log/auth.log | grep -oP '\d+\.\d+\.\d+\.\d+' | sort -u
# Find sudo command execution
grep "sudo:" /var/log/auth.log | grep "COMMAND"
# Detect brute force patterns (>10 failures from same IP)
grep "Failed password" /var/log/auth.log | awk '{print $(NF-3)}' | sort | uniq -c | sort -rn | head -20
# Find account creation events
grep "useradd\|adduser" /var/log/auth.log
# Detect SSH key authentication
grep "Accepted publickey" /var/log/auth.log
Systemd Journal Analysis
# Export journal in JSON format for forensic processing
journalctl --output=json --no-pager > journal_export.json
# Filter by time range
journalctl --since "2025-02-01" --until "2025-02-15" --output=json > timerange.json
# Filter by unit/service
journalctl -u sshd --output=json > sshd_journal.json
# Show kernel messages (USB events, module loads)
journalctl -k --output=json > kernel_journal.json
# Filter by priority (0=emerg to 7=debug)
journalctl -p err --output=json > errors.json
# Boot-specific logs
journalctl -b 0 --output=json > current_boot.json
journalctl --list-boots # List all recorded boot sessions
Linux Audit Framework Analysis
# Search audit log for specific event types
ausearch -m USER_AUTH --start today
# Search for file access events
ausearch -f /etc/shadow
# Search for process execution
ausearch -m EXECVE --start "02/01/2025" --end "02/28/2025"
# Generate report of login events
aureport --login --start "02/01/2025"
# Generate summary of failed authentications
aureport --auth --failed
# Search for specific user activity
ausearch -ua 1001 # By UID
ausearch -ua username # By username
Cron Job Investigation
# Check system-wide crontab
cat /etc/crontab
# Check user crontabs
ls -la /var/spool/cron/crontabs/
# Review cron execution logs
grep "CRON" /var/log/syslog
# Check for at/batch jobs
ls -la /var/spool/at/
atq
Python Forensic Log Parser
import re
import json
import sys
import os
from datetime import datetime
from collections import defaultdict
class LinuxLogForensicAnalyzer:
"""Analyze Linux system logs for forensic investigation."""
def __init__(self, log_dir: str, output_dir: str):
self.log_dir = log_dir
self.output_dir = output_dir
os.makedirs(output_dir, exist_ok=True)
def parse_auth_log(self, auth_log_path: str) -> dict:
"""Parse auth.log for authentication events."""
events = {
"successful_logins": [],
"failed_logins": [],
"sudo_commands": [],
"account_changes": [],
"ssh_sessions": []
}
ssh_accepted = re.compile(
r'(\w+\s+\d+\s+[\d:]+)\s+(\S+)\s+sshd\[\d+\]:\s+Accepted\s+(\S+)\s+for\s+(\S+)\s+from\s+([\d.]+)'
)
ssh_failed = re.compile(
r'(\w+\s+\d+\s+[\d:]+)\s+(\S+)\s+sshd\[\d+\]:\s+Failed\s+password\s+for\s+(\S*)\s+from\s+([\d.]+)'
)
sudo_cmd = re.compile(
r'(\w+\s+\d+\s+[\d:]+)\s+(\S+)\s+sudo:\s+(\S+)\s+:.*COMMAND=(.*)'
)
useradd = re.compile(
r'(\w+\s+\d+\s+[\d:]+)\s+(\S+)\s+useradd\[\d+\]:\s+new user: name=(\S+)'
)
with open(auth_log_path, "r", errors="replace") as f:
for line in f:
m = ssh_accepted.search(line)
if m:
events["successful_logins"].append({
"timestamp": m.group(1), "host": m.group(2),
"method": m.group(3), "user": m.group(4), "source_ip": m.group(5)
})
continue
m = ssh_failed.search(line)
if m:
events["failed_logins"].append({
"timestamp": m.group(1), "host": m.group(2),
"user": m.group(3), "source_ip": m.group(4)
})
continue
m = sudo_cmd.search(line)
if m:
events["sudo_commands"].append({
"timestamp": m.group(1), "host": m.group(2),
"user": m.group(3), "command": m.group(4).strip()
})
continue
m = useradd.search(line)
if m:
events["account_changes"].append({
"timestamp": m.group(1), "host": m.group(2),
"new_user": m.group(3)
})
return events
def detect_brute_force(self, auth_events: dict, threshold: int = 10) -> list:
"""Detect brute force attempts from auth log data."""
ip_failures = defaultdict(int)
for event in auth_events.get("failed_logins", []):
ip_failures[event["source_ip"]] += 1
brute_force = []
for ip, count in ip_failures.items():
if count >= threshold:
brute_force.append({"source_ip": ip, "failed_attempts": count})
return sorted(brute_force, key=lambda x: x["failed_attempts"], reverse=True)
def generate_report(self, auth_log_path: str) -> str:
"""Generate comprehensive forensic analysis report."""
auth_events = self.parse_auth_log(auth_log_path)
brute_force = self.detect_brute_force(auth_events)
report = {
"analysis_timestamp": datetime.now().isoformat(),
"log_source": auth_log_path,
"summary": {
"successful_logins": len(auth_events["successful_logins"]),
"failed_logins": len(auth_events["failed_logins"]),
"sudo_commands": len(auth_events["sudo_commands"]),
"account_changes": len(auth_events["account_changes"]),
"brute_force_sources": len(brute_force)
},
"brute_force_detected": brute_force,
"auth_events": auth_events
}
report_path = os.path.join(self.output_dir, "linux_log_forensics.json")
with open(report_path, "w") as f:
json.dump(report, f, indent=2)
print(f"[*] Successful logins: {report['summary']['successful_logins']}")
print(f"[*] Failed logins: {report['summary']['failed_logins']}")
print(f"[*] Sudo commands: {report['summary']['sudo_commands']}")
print(f"[*] Brute force sources: {report['summary']['brute_force_sources']}")
return report_path
def main():
if len(sys.argv) < 3:
print("Usage: python process.py <auth_log_path> <output_dir>")
sys.exit(1)
analyzer = LinuxLogForensicAnalyzer(os.path.dirname(sys.argv[1]), sys.argv[2])
analyzer.generate_report(sys.argv[1])
if __name__ == "__main__":
main()
References
- Linux Forensics In Depth: https://amr-git-dot.github.io/forensic%20investigation/Linux_Forensics/
- SANS Practical Linux Forensics: https://nostarch.com/linuxforensics
- HackTricks Linux Forensics: https://book.hacktricks.xyz/generic-methodologies-and-resources/basic-forensic-methodology/linux-forensics
- Log Sources for Digital Forensics: https://letsdefend.io/blog/log-sources-for-digital-forensics-windows-and-linux
How to use performing-linux-log-forensics-investigation on Cursor
AI-first code editor with Composer
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-linux-log-forensics-investigation
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches performing-linux-log-forensics-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-linux-log-forensics-investigation. Access the skill through slash commands (e.g., /performing-linux-log-forensics-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▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.8★★★★★50 reviews- ★★★★★Zara Khanna· Dec 24, 2024
Keeps context tight: performing-linux-log-forensics-investigation is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Ama Mensah· Dec 16, 2024
Solid pick for teams standardizing on skills: performing-linux-log-forensics-investigation is focused, and the summary matches what you get after install.
- ★★★★★Arya Sethi· Dec 12, 2024
We added performing-linux-log-forensics-investigation from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Neel Zhang· Dec 8, 2024
I recommend performing-linux-log-forensics-investigation for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Arya Desai· Dec 4, 2024
performing-linux-log-forensics-investigation has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Arya Shah· Nov 23, 2024
Solid pick for teams standardizing on skills: performing-linux-log-forensics-investigation is focused, and the summary matches what you get after install.
- ★★★★★Emma Ramirez· Nov 11, 2024
I recommend performing-linux-log-forensics-investigation for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Chinedu Sethi· Nov 7, 2024
performing-linux-log-forensics-investigation has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Li Mehta· Nov 3, 2024
Useful defaults in performing-linux-log-forensics-investigation — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★James Menon· Oct 26, 2024
performing-linux-log-forensics-investigation fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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