| name | acquiring-disk-image-with-dd-and-dcfldd |
| description | Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification. Use when createing forensically sound bit-for-bit disk images using dd and dcfldd. |
| domain | cybersecurity |
| tags | ["forensics","disk-imaging","evidence-acquisition","dd","dcfldd","hash-verification"] |
| subdomain | digital-forensics |
| version | 1.0 |
| author | oyi77 |
| license | Apache-2.0 |
| nist_csf | ["RS.AN-01","RS.AN-03","DE.AE-02","RS.MA-01"] |
Acquiring Disk Image With Dd And Dcfldd
Overview
Cybersecurity skill for acquiring disk image with dd and dcfldd. Follows industry best practices and security standards.
When to Use
Trigger phrases:
-
"acquiring disk image with dd and dcfldd"
-
"Create forensically sound bit-for-bit disk images using dd and dcfldd while pres"
-
When you need to create a forensic copy of a suspect drive for investigation
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During incident response when preserving volatile disk evidence before analysis
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When law enforcement or legal proceedings require a verified bit-for-bit copy
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Before performing any destructive analysis on a storage device
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When acquiring images from physical drives, USB devices, or memory cards
When NOT to Use
- When you lack proper authorization for testing
- For production systems without change management
- When the task requires legal or compliance expertise beyond technical scope
Prerequisites
- Linux-based forensic workstation (SIFT, Kali, or any Linux distro)
dd (pre-installed on all Linux systems) or dcfldd (enhanced forensic version)
- Write-blocker hardware or software write-blocking configured
- Destination drive with sufficient storage (larger than source)
- Root/sudo privileges on the forensic workstation
- SHA-256 or MD5 hashing utilities (
sha256sum, md5sum)
Workflow
import re
IOC_PATTERNS = {
"ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",
"domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",
"hash_md5": r"\b[a-f0-9]{32}\b",
"hash_sha256": r"\b[a-f0-9]{64}\b",
}
def extract_iocs(text: str) -> dict:
return {k: re.findall(v, text) k, v IOC_PATTERNS.items()}