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Imaging Named

Disk imaging names bit-for-bit copies of storage — understand E01/aff/dd concepts on YOUR $DFIR_LAB; never image unauthorized devices.

15 min+40 XP3 quiz
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Visual · t26_imaging_named

Imaging literacy. $DFIR_LAB only. Original Cyberlium.

Opening

An image is a container for sectors — literacy means naming formats and scope, not cloning stranger laptops.

Forensic imaging creates a verifiable copy of storage media: raw (dd), EWF/E01 (EnCase-style with metadata and optional compression), AFF, and VM disk files (VMDK/VHDX) when scope allows. Images are analyzed on write-protected mounts — originals stay sealed per policy. Cyberlium names imaging concepts on YOUR $DFIR_LAB practice targets — course E01 files, self-created VM disks — not hardware you lack authority to touch. Next: Hashing Integrity.

1. Image formats (named)

Raw/dd: simple sector stream, widely supported. E01: segment files, case metadata, compression — common in training datasets. VM disks: snapshot/copy of virtual disk when VM is yours or course-provided.

On $DFIR_LAB, note which format your practice image uses and one tool that opens it (Autopsy, FTK Imager literacy).

Command guide

Try these commands — Image formats (named)

═══ TOOLS & WEBSITES ═══ Browse / read these (authorized learning only — stay in YOUR lab / program scope)

NIST hashing — https://www.nist.gov/itl/ssd/software-quality-group/computer-forensics-reference-data-sets-cfreds (integrity literacy) RFC 3227 — https://www.rfc-editor.org/rfc/rfc3227 (document hashes at acquisition) SANS DFIR — https://www.sans.org/cyber-security-skills/digital-forensics/

═══ INSTALL ═══

Linux (Debian/Ubuntu):

Command — copy this

sudo apt install python3

macOS:

Command — copy this

brew install python3

Windows: Download https://python.org/downloads/

═══ LINUX / macOS ═══

Command — copy this

export LAB_DFIR=${LAB_DFIR:-$HOME/cyberlium-lab/t26-dfir}
python3 - <<'PY'
data = b'CYBERLIUM-LAB-DFIR-SAMPLE\x00\xde\xad\xbe\xef'
path = __import__('os').path.join(__import__('os').environ.get('LAB_DFIR', __import__('os').path.expanduser('~/cyberlium-lab/t26-dfir')), 'evidence', 'sample.bin')
open(path, 'wb').write(data)
print(f'Created {path} — YOUR lab binary sample only')
PY

Command — copy this

file "$LAB_DFIR/evidence/sample.bin"

Primary tools to practice this lesson: sha256sum, python3. Reference sites: NIST hashing (https://www.nist.gov/itl/ssd/software-quality-group/computer-forensics-reference-data-sets-cfreds); RFC 3227 (https://www.rfc-editor.org/rfc/rfc3227); SANS DFIR (https://www.sans.org/cyber-security-skills/digital-forensics/). Run every command in the box — install first, then the usage lines — only on YOUR lab / program scope.

2. Physical vs logical (named)

Physical imaging: full device or partition sectors — includes unallocated and slack. Logical: selected files/folders — faster but misses deleted and unallocated artifacts.

Defenders choose based on scope and time; students document the choice on lab worksheets.

3. Imaging ethics boundary

Forbidden: imaging stranger USB, phones, or PCs without legal authority. Allowed: imaging checklist naming format, target, and authority source for $DFIR_LAB.

Ship: imaging card — format, physical/logical, target description, authority cite. Next: Hashing Integrity.

4. What you ship: imaging card for $DFIR_LAB

Format, physical/logical, target, authority. $DFIR_LAB named. NO stranger hardware. chmod 600.

5. What you record before the next lesson

Date. Imaging card. $DFIR_LAB named. File t26-m03-l01-imaging-named.txt chmod 600.

6. Wrong vs right: tampering evidence vs chain of custody

Worked failure — same MSF word, opposite target. Right never needs a café Wi-Fi or classmate laptop.

  • Wrong

    Image a found USB drive. Skip format notes because 'Autopsy opens anything.'

  • Right

    Write imaging card for YOUR $DFIR_LAB practice target. Next: Hashing Integrity.

Mission: name YOUR practice image format

1) State image format (E01/raw/VM). 2) Physical vs logical one-liner. 3) Cite authority source. 4) chmod 600.

Stuck? Ask Cyberlium AI Mentor

E01 metadata can carry case fields — fill them in real casework.

Knowledge Check

1

APPLY: Imaging literacy on Cyberlium means:

Multiple choice

Knowledge Check

2

APPLY: True or False: Physical imaging includes unallocated space.

True or False

Knowledge Check

3

APPLY: Before imaging you must have:

Multiple choice

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Answer all 3 knowledge checks to continue. (0/3 answered)