Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.
日本語の概要は準備中です。原文の説明を表示しています。
Deploys canary files (honeytokens) across file systems to detect ransomware encryption activity in real time. Uses strategically placed decoy documents monitored via file integrity monitoring or OS-level watchdogs to trigger alerts when ransomware modifies or encrypts them. Activates for requests involving ransomware canary deployment, honeyfile setup, deception-based ransomware detection, or file integrity monitoring for encryption.
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Do not use decoy files as the sole ransomware defense. They are a detection mechanism, not a prevention mechanism, and should complement backups, EDR, and access controls.
watchdog library for cross-platform file system monitoringPlan file placement for maximum detection coverage:
Canary File Placement Strategy:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Naming Convention:
- Use names that sort FIRST and LAST alphabetically in each directory
- Ransomware typically enumerates directories A-Z or Z-A
- Examples: _AAAA_budget_2024.docx, ~zzzz_report_final.xlsx
Placement Locations:
- Root of every file share (\\server\share\_AAAA_canary.docx)
- Desktop, Documents, Downloads on each endpoint
- Department-specific shares (Finance, HR, Legal)
- Backup staging directories
- Home directories of high-privilege accounts
File Types:
- .docx, .xlsx, .pdf (most targeted by ransomware)
- .sql, .bak (database files, high value)
- Mix of file types to detect ransomware that targets specific extensions
Create decoy files with realistic content and metadata:
import os
import time
def create_canary_docx(filepath, content="Q4 Financial Summary - Confidential"):
"""Create a realistic .docx canary file using python-docx."""
from docx import Document
doc = Document()
doc.add_heading("Financial Report - CONFIDENTIAL", level=1)
doc.add_paragraph(content)
doc.add_paragraph(f"Generated: {time.strftime('%Y-%m-%d')}")
doc.save(filepath)
def create_canary_txt(filepath):
"""Create a simple text canary with known content for hash verification."""
content = "CANARY_TOKEN_DO_NOT_MODIFY\n"
content += f"Created: {time.strftime('%Y-%m-%dT%H:%M:%S')}\n"
content += "This file is monitored for unauthorized changes.\n"
with open(filepath, "w") as f:
f.write(content)
Monitor canary files for any modification, rename, or deletion:
from watchdog.observers import Observer
from watchdog.events import FileSystemEventHandler
class CanaryHandler(FileSystemEventHandler):
def __init__(self, canary_paths, alert_callback):
self.canary_paths = set(canary_paths)
self.alert_callback = alert_callback
def on_modified(self, event):
if event.src_path in self.canary_paths:
self.alert_callback("MODIFIED", event.src_path)
def on_deleted(self, event):
if event.src_path in self.canary_paths:
self.alert_callback("DELETED", event.src_path)
def on_moved(self, event):
if event.src_path in self.canary_paths:
self.alert_callback("RENAMED", event.src_path)
Define automated responses when canary files are triggered:
Alert Response Matrix:
━━━━━━━━━━━━━━━━━━━━━
Event: Canary MODIFIED
→ Severity: CRITICAL
→ Action: Alert SOC, identify modifying process (PID), isolate endpoint
Event: Canary DELETED
→ Severity: HIGH
→ Action: Alert SOC, check for ransomware note in same directory
Event: Canary RENAMED (new extension added)
→ Severity: CRITICAL
→ Action: Alert SOC, check extension against known ransomware extensions
→ Automated: Kill modifying process, disable network interface
Event: Multiple canaries triggered within 60 seconds
→ Severity: EMERGENCY
→ Action: Network-wide isolation, activate incident response plan
Test that canary files detect actual ransomware behavior:
# Simulate ransomware encryption (safe test - modifies canary content)
echo "ENCRYPTED_BY_TEST" > /path/to/canary/_AAAA_budget.docx
# Simulate ransomware rename (adds extension)
mv /path/to/canary/report.xlsx /path/to/canary/report.xlsx.locked
# Verify alerts were generated in SIEM/alerting system
| Term | Definition |
|---|---|
| Canary File | A decoy file placed in a directory that is monitored for any access or modification, serving as a tripwire for unauthorized activity |
| Honeytoken | A broader category of deception artifacts (files, credentials, database records) designed to alert when accessed |
| File Integrity Monitoring | Continuous monitoring of file attributes (hash, size, permissions, timestamps) to detect unauthorized changes |
| ReadDirectoryChangesW | Windows API for monitoring file system changes in a directory; used by the watchdog library on Windows |
| inotify | Linux kernel subsystem for monitoring file system events; provides near-instant notification of file changes |
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概要と使いどころ
Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.
日本語の概要は準備中です。原文の説明を表示しています。
Detect dangerous ACL misconfigurations in Active Directory using ldap3 to identify GenericAll, WriteDACL, and WriteOwner abuse paths
日本語の概要は準備中です。原文の説明を表示しています。
Perform static analysis of Android APK malware samples using apktool for decompilation, jadx for Java source recovery, and androguard for permission analysis, manifest inspection, and suspicious API call detection.
日本語の概要は準備中です。原文の説明を表示しています。
Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect BOLA/IDOR attacks, rate limit bypass, credential scanning, and injection attempts. Uses pandas for statistical analysis of request patterns and anomaly detection. Use when investigating API abuse or building API-specific threat detection rules.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze advanced persistent threat (APT) group techniques using MITRE ATT&CK Navigator to create layered heatmaps of adversary TTPs for detection gap analysis and threat-informed defense.
日本語の概要は準備中です。原文の説明を表示しています。
Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query to detect suspicious administrative operations, impossible travel, privilege escalation, and resource modifications. Builds KQL queries for threat hunting in Azure environments. Use when investigating suspicious Azure tenant activity or building cloud SIEM detections.
日本語の概要は準備中です。原文の説明を表示しています。