Log Signal Extractor

Ever wonder what's actually happening in your SSH logs? This tool digs through auth.log files and finds the real problems—brute force attacks, password spraying, and when someone finally cracks an account.

⚡ Quickstart

# Clone and run
git clone https://github.com/znrac137/log-signal-extractor.git
cd log-signal-extractor
python main.py logs/sample_auth.log --verbose

# Save alerts to JSON
python main.py logs/sample_auth.log --output alerts.json

That's it. No dependencies to install.

Why I Built This

It parses your auth.log and hunts for suspicious patterns. When it finds something sketchy, it spits out detailed JSON alerts so you know exactly what happened and when.

What It Catches

• Brute Force: Someone's trying password after password on the same account. Classic attacker move.
• Password Spray: They found a common password and are throwing it at every username they can find.
• Account Takeover: After a bunch of failures, they got in. That's... not good.

Getting Started

Requirements

• Python 3.8 or later
• Nothing else. Seriously.

Installation

git clone https://github.com/znrac137/log-signal-extractor.git
cd log-signal-extractor

Using It

Want to just run it? Easy:

python main.py logs/sample_auth.log

Want to save the alerts to a file?

python main.py logs/sample_auth.log --output alerts.json

Want to see what's happening behind the scenes?

python main.py logs/sample_auth.log --verbose

Here's what all the options do:

usage: main.py [-h] [-o OUTPUT] [-v] logfile

Extract security signals from SSH authentication logs

positional arguments:
  logfile              Path to auth.log file to analyze

optional arguments:
  -h, --help           show this help message and exit
  -o, --output OUTPUT  Output file for JSON alerts (default: stdout)
  -v, --verbose        Enable verbose output

Sample Terminal Output

[*] Parsing log file: logs/sample_auth.log
[+] Extracted 30 authentication events
[*] Running security detection algorithms...

============================================================
ALERT: BRUTE_FORCE_ATTACK (HIGH)
  Source IP: 192.168.1.100
  Failed attempts: 8
  Usernames targeted: admin, root, postgres, test
  Time window: 10 minutes
============================================================

ALERT: PASSWORD_SPRAY_ATTACK (HIGH)
  Source IP: 192.168.1.101
  Unique usernames: 6
  Common password detected
============================================================

ALERT: SUCCESS_AFTER_FAILURES (CRITICAL)
  Source IP: 192.168.1.100
  Username: root
  Failed attempts before success: 7
  ⚠️ Potential account compromise
============================================================

Analysis Complete
  Events analyzed: 30
  Alerts detected: 3
    CRITICAL: 1
    HIGH: 2

What You Get Back

When it runs, you'll get JSON output with all the alerts. Each one includes the timestamp, usernames involved, the IP address causing trouble, and the actual log lines that triggered the alert:

{
  "metadata": {
    "scan_timestamp": "2026-02-10T12:00:00.000000",
    "log_file": "logs/sample_auth.log",
    "events_analyzed": 30,
    "alerts_detected": 3
  },
  "alerts": [
    {
      "alert_type": "BRUTE_FORCE_ATTACK",
      "severity": "HIGH",
      "source_ip": "192.168.1.100",
      "affected_usernames": ["admin", "root", "postgres", "test"],
      "event_count": 8,
      "timestamp": "2026-02-10T12:00:00.000000",
      "description": "Detected 8 failed login attempts from 192.168.1.100 in 10min window. Possible brute force attack.",
      "events": [
        {
          "timestamp": "2026-01-01T10:15:22",
          "username": "admin",
          "ip_address": "192.168.1.100",
          "event_type": "Invalid user",
          "raw_line": "Jan  1 10:15:22 server sshd[1234]: Invalid user admin from 192.168.1.100 port 54321"
        },
        ...
      ]
    }
  ]
}

Try It Out

The project includes a realistic sample auth.log file with actual attack patterns already in it:

python main.py logs/sample_auth.log --verbose

Sample Output:

[*] Parsing log file: logs/sample_auth.log
[+] Extracted 30 authentication events
[*] Running security detection algorithms...

============================================================
Analysis Complete
============================================================
Events analyzed: 30
Alerts detected: 3
  CRITICAL: 1
  HIGH: 2

Project Structure

log-signal-extractor/
├── logs/
│   └── sample_auth.log              # Sample auth.log with attack patterns
├── extractor/
│   ├── __init__.py                  # Package initialization
│   ├── models.py                    # Data models (LogEvent, Alert)
│   ├── parser.py                    # Auth.log parser
│   ├── detectors.py                 # Detection algorithms
│   └── utils.py                     # Utility functions
├── main.py                          # CLI entry point
├── requirements.txt                 # Python dependencies (none required)
└── README.md                        # This file

Architecture

The Pieces

Here's how it all fits together:

1. models.py - The data structures:

   • LogEvent: A single authentication log entry
   • Alert: A detected security alert
   • EventType: What types of events we track
   • AlertSeverity: How bad is this alert?

2. parser.py - Reads auth.log files:

   • AuthLogParser.parse_file(): Process the whole file
   • AuthLogParser.parse_line(): Handle one line at a time
   • Pulls out: timestamp, username, IP, event type

3. detectors.py - The detection algorithms:

   • detect_bruteforce(): Spots repeated failed attempts
   • detect_password_spray(): Finds multi-user targeting
   • detect_success_after_fail(): Catches account compromise
   • You can tweak thresholds to make it more or less sensitive

4. utils.py - Helper stuff:

   • Parse timestamps
   • Extract and validate IPs
   • Pull out usernames

How It Detects Things

Brute Force Detection

• What it is: Multiple failed login attempts from the same IP
• When it triggers: 5 or more failed attempts in a 10-minute window
• How serious: HIGH
• What it means: Someone's systematically guessing passwords

Password Spray Detection

• What it is: Someone trying to log in as multiple different users from one IP
• When it triggers: 3 or more different usernames attempted in a 10-minute window
• How serious: HIGH
• What it means: They've probably got a common password and are throwing it everywhere

Success After Failures

• What it is: A successful login that comes after a bunch of failures
• When it triggers: Login succeeds after 5+ failed attempts from the same IP and username combo
• How serious: CRITICAL
• What it means: They guessed it. Account's probably compromised.

Tuning the Sensitivity

Want to make it stricter or looser? Modify these thresholds in main.py:

detector = SuspiciousActivityDetector(config={
    "failed_login_threshold": 5,        # How many failed attempts before alert
    "password_spray_threshold": 3,      # How many different users before alert
    "time_window_minutes": 10,          # Window to group attempts together
})

Example: Stricter Detection (catch more)

detector = SuspiciousActivityDetector(config={
    "failed_login_threshold": 3,        # Alert after just 3 failures
    "password_spray_threshold": 2,      # Alert on 2+ users targeted
    "time_window_minutes": 5,           # Tighter 5-minute window
})

Example: Looser Detection (fewer false positives)

detector = SuspiciousActivityDetector(config={
    "failed_login_threshold": 10,       # Only alert after 10 failures
    "password_spray_threshold": 5,      # Need 5+ users targeted
    "time_window_minutes": 15,          # Wider 15-minute window
})

Then just run it normally:

python main.py logs/sample_auth.log --output alerts.json

Things to Keep in Mind

• False Positives: Sometimes legitimate users trigger alerts if their VPN drops or they forget their password
• Tuning: Adjust the thresholds based on what's normal in your environment
• Log Rotation: Make sure your auth.log is being kept around long enough to analyze
• Scale It Up: If you're running this in production, consider using a log aggregation tool like ELK or Splunk

Limitations

• Timestamp Format: The parser expects standard syslog format. Some systems may have different formats
• Year Information: Standard auth.log doesn't include year in timestamps, so alerts use the current year (can cause issues with logs spanning calendar years)
• Log Rotation: If logs are rotated/compressed, you'll need to decompress and pass individual files
• Real-time Analysis: Current version processes static files. For real-time streaming, needs integration with log ingestion tools
• Geographic Anomalies: Doesn't detect impossible travel or unusual access patterns (yet)
• Account Context: Doesn't know which accounts are service accounts vs. human users

Want to Contribute?

Adding New Detections

To add a new detection algorithm:

1. Create a method in SuspiciousActivityDetector:

def detect_custom_pattern(self, events: List[LogEvent]) -> List[Alert]:
    """Your detection logic here"""
    alerts = []
    # ... implementation ...
    return alerts

2. Call it from detect_all():

all_alerts.extend(self.detect_custom_pattern(events))

Testing

Just run it on the sample logs:

python main.py logs/sample_auth.log --output test_alerts.json --verbose

Performance

• Parses typical auth.log file (~1000s of events) in <100ms
• Memory efficient: processes logs line-by-line
• Suitable for real-time log processing pipelines

Future Enhancements

• Real-time log streaming support
• Additional detection patterns (geographic anomalies, timing patterns)
• Integration with threat intelligence feeds
• Elasticsearch/Splunk exporters
• Web dashboard for alert visualization
• Machine learning models for behavioral analysis

License

MIT License - See LICENSE file for details

Author

znrac137

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Version: 1.0.0
Last Updated: February 2026