跳到正文
cccskills
免费在 GitHub 公开

deploying-decoy-files-for-ransomware-detection

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.

中文简介正在准备中。当前显示原文说明。

集成工具ExcelWord
查看安装方法

包含的文件(4)

  • SKILL.md7.5 KB
  • LICENSE11.0 KB
  • references/api-reference.md3.1 KB
  • scripts/agent.py9.2 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Deploying Decoy Files for Ransomware Detection

When to Use

  • Setting up early-warning detection for ransomware on file servers or endpoints
  • Supplementing EDR/AV with a deception-based detection layer that catches unknown ransomware variants
  • Creating high-fidelity ransomware alerts that have very low false-positive rates (legitimate users have no reason to touch decoy files)
  • Testing ransomware response procedures by validating that canary file modifications trigger the expected alerting pipeline
  • Protecting high-value file shares (finance, HR, legal) with tripwire files that indicate unauthorized encryption activity

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.

Prerequisites

  • Python 3.8+ with watchdog library for cross-platform file system monitoring
  • Administrative access to target file shares or endpoints for canary placement
  • File integrity monitoring (FIM) tool or SIEM integration for alert routing
  • Understanding of target directory structure to place canaries in high-value locations
  • Windows: NTFS change journal or ReadDirectoryChangesW API access
  • Linux: inotify support in kernel (standard in modern kernels)

Workflow

Step 1: Design Canary File Strategy

Plan 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

Step 2: Generate Realistic Canary Files

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)

Step 3: Deploy File System Watcher

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)

Step 4: Configure Alerting and Response

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

Step 5: Validate Detection Coverage

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

Verification

  • Confirm all canary files are present and unmodified using stored hash baselines
  • Verify that modifying any canary file generates an alert within the expected timeframe (under 30 seconds)
  • Test that alert routing to SOC/SIEM is functional with a controlled modification
  • Validate that automated response actions (process kill, network isolation) execute correctly
  • Check that canary files survive normal backup and restore operations
  • Ensure legitimate users and processes are excluded from false-positive alerts (backup agents, AV scans)

Key Concepts

TermDefinition
Canary FileA decoy file placed in a directory that is monitored for any access or modification, serving as a tripwire for unauthorized activity
HoneytokenA broader category of deception artifacts (files, credentials, database records) designed to alert when accessed
File Integrity MonitoringContinuous monitoring of file attributes (hash, size, permissions, timestamps) to detect unauthorized changes
ReadDirectoryChangesWWindows API for monitoring file system changes in a directory; used by the watchdog library on Windows
inotifyLinux kernel subsystem for monitoring file system events; provides near-instant notification of file changes

Tools & Systems

  • watchdog (Python): Cross-platform file system event monitoring library supporting Windows, Linux, and macOS
  • Canarytokens (Thinkst): Free hosted service for generating various types of canary tokens including files, URLs, and DNS tokens
  • OSSEC/Wazuh: Open-source HIDS with built-in file integrity monitoring and alerting capabilities
  • Elastic Endpoint: Uses canary files internally for ransomware protection and key capture
  • Sysmon: Windows system monitor that logs file creation events (Event ID 11) for canary file monitoring

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.

中文简介正在准备中。当前显示原文说明。

micsapp/micstec-skills42026年3月20日 更新

Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.

中文简介正在准备中。当前显示原文说明。

micsapp/micstec-skills42026年3月20日 更新

Detect dangerous ACL misconfigurations in Active Directory using ldap3 to identify GenericAll, WriteDACL, and WriteOwner abuse paths

中文简介正在准备中。当前显示原文说明。

micsapp/micstec-skills42026年3月20日 更新

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.

中文简介正在准备中。当前显示原文说明。

micsapp/micstec-skills42026年3月20日 更新

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.

中文简介正在准备中。当前显示原文说明。

micsapp/micstec-skills42026年3月20日 更新

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.

中文简介正在准备中。当前显示原文说明。

micsapp/micstec-skills42026年3月20日 更新

查看 micsapp 的全部技能

报告此技能的问题