Acquire and analyze mobile device data using Cellebrite UFED and open-source tools to extract communications, location data, and application artifacts.
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Acquire and analyze mobile device data using Cellebrite UFED and open-source tools to extract communications, location data, and application artifacts.
Performing Mobile Device Forensics with Cellebrite
When to Use
When extracting evidence from smartphones or tablets during an investigation
For recovering deleted messages, call logs, and location data from mobile devices
During investigations involving communications via messaging apps
When analyzing mobile application data for evidence of criminal activity
For corporate investigations involving employee mobile device misuse
Detection Gaps & Validation
The extraction type and device lock state silently bound what you can recover. Don't mistake a shallow acquisition for a complete one:
Logical extraction misses the deleted data. UFED/iLEAPP logical pulls return only live app data; deleted messages, freelist/WAL rows, and unallocated content require File System or Physical (checkm8/GrayKey on supported iOS, rooted/EDL on Android). Report the extraction level explicitly — "no deleted messages found" on a logical pull is a coverage gap, not a finding.
Encryption and locked bootloaders defeat acquisition. Modern iOS (Secure Enclave) and FBE Android encrypt at rest; without the passcode/AFU state you may get only metadata. A locked bootloader, BFU (Before First Unlock) state, or unsupported chipset can block physical extraction entirely. Document lock type, BFU/AFU, and that gaps stem from encryption, not absence of evidence.
App-layer deletion and Secure Enclave. Signal/Telegram secret chats, disappearing messages, and Keychain/Keystore-protected blobs may be unrecoverable even from a physical image. Recover deleted SQLite rows from -wal/-shm and freelists before concluding a conversation never existed.
Validate timestamps and provenance. Reconcile the three time bases: Unix ms (Android/WhatsApp), Mac Absolute Time +978307200 (iOS sms.db), and Chrome/WebKit microseconds. Confirm device clock wasn't manually changed (check timezone/NITZ artifacts), and cross-corroborate a message against call logs, notifications (KnowledgeC/biome), and cloud backups before relying on it.
Interpretation false positives. A geotagged photo proves the camera/file was at a location, not the suspect; cached/forwarded media and synced multi-device content can appear "local." Hash all media against known sets, and verify hash-database hits before escalating. Always isolate in a Faraday bag first — a remote wipe or sync after seizure destroys or alters the very evidence you're collecting.
Prerequisites
Cellebrite UFED Touch/4PC or UFED Physical Analyzer (licensed)
Alternative open-source tools: ALEAPP, iLEAPP, MEAT, libimobiledevice
Appropriate cables and adapters for target device
Faraday bag to isolate the device from network signals
Legal authorization (warrant, consent, or corporate policy)
Knowledge of iOS and Android file system structures
Workflow
Step 1: Prepare the Device and Isolation
# CRITICAL: Immediately place device in airplane mode or Faraday bag# This prevents remote wipe commands and additional data changes# Document device state before acquisition# Record: make, model, IMEI, serial number, OS version, screen lock status# Photograph the device from all angles# For Android - Enable USB debugging if accessible# Settings > Developer Options > USB Debugging > Enable# For iOS - Trust the forensic workstation# When prompted on device, tap "Trust This Computer"# If device is locked, document lock type (PIN, pattern, biometric)# Cellebrite UFED can bypass certain lock types depending on device model# Install open-source tools as alternatives
pip install aleapp # Android Logs Events And Protobuf Parser
pip install ileapp # iOS Logs Events And Properties Parsersudo apt-get install libimobiledevice-utils # iOS acquisition on Linux
Step 2: Perform Device Acquisition
# === Cellebrite UFED Acquisition ===# 1. Launch UFED 4PC or connect UFED Touch# 2. Select Device > Identify device model automatically# 3. Choose extraction type:# - Logical: App data, contacts, messages, call logs (fastest, least data)# - File System: Full file system access including databases# - Physical: Bit-for-bit image including deleted data (most complete)# - Advanced (Checkm8/GrayKey): For locked iOS devices (specific models)# 4. Select output format and destination# 5. Begin extraction# === Open-source iOS acquisition with libimobiledevice ===# List connected iOS devices
idevice_id -l
# Get device information
ideviceinfo -u <UDID>
# Create iOS backup (logical acquisition)
idevicebackup2 backup --full /cases/case-2024-001/mobile/ios_backup/
# For encrypted backups (contains more data including passwords)
idevicebackup2 backup --full --password /cases/case-2024-001/mobile/ios_backup/
# === Android acquisition with ADB ===# List connected devices
adb devices
# Full backup (requires screen unlock)
adb backup -apk -shared -all -f /cases/case-2024-001/mobile/android_backup.ab
# Extract specific app data
adb shell pm list packages | grep -i "whatsapp\|telegram\|signal"
adb pull /data/data/com.whatsapp/ /cases/case-2024-001/mobile/whatsapp/
# For rooted Android devices - full filesystem
adb shell "su -c 'dd if=/dev/block/mmcblk0 bs=4096'" | \
dd of=/cases/case-2024-001/mobile/android_physical.dd
# Hash the acquisitionsha256sum /cases/case-2024-001/mobile/*.dd > /cases/case-2024-001/mobile/acquisition_hashes.txt
Step 3: Analyze with ALEAPP (Android) or iLEAPP (iOS)
# === Android analysis with ALEAPP ===# ALEAPP processes Android file system extractions
python3 -m aleapp \
-t fs \
-i /cases/case-2024-001/mobile/android_extraction/ \
-o /cases/case-2024-001/analysis/aleapp_report/
# ALEAPP extracts and reports on:# - Call logs, SMS/MMS messages# - Chrome browser history and searches# - WiFi connection history# - Installed applications# - Google account activity# - Location data (Google Maps, Photos)# - WhatsApp, Telegram, Signal messages# - App usage statistics# - Device settings and accounts# === iOS analysis with iLEAPP ===
python3 -m ileapp \
-t tar \
-i /cases/case-2024-001/mobile/ios_backup.tar \
-o /cases/case-2024-001/analysis/ileapp_report/
# iLEAPP extracts and reports on:# - iMessage and SMS messages# - Safari browsing history# - WiFi and Bluetooth connections# - Health data and location history# - App usage (KnowledgeC)# - Photos with EXIF/GPS data# - Notes, Calendar, Reminders# - Keychain data (if decryptable)# - Screen time data
Step 4: Extract Communications and Messaging Data
# Extract WhatsApp messages from Android
python3 << 'PYEOF'
import sqlite3
import os
# WhatsApp database location
db_path = "/cases/case-2024-001/mobile/android_extraction/data/data/com.whatsapp/databases/msgstore.db"if os.path.exists(db_path):
conn = sqlite3.connect(db_path)
cursor = conn.cursor()
# Extract messages
cursor.execute("""
SELECT
key_remote_jid AS contact,
CASE WHEN key_from_me = 1 THEN 'SENT' ELSE 'RECEIVED' END AS direction,
data AS message_text,
datetime(timestamp/1000, 'unixepoch') AS msg_time,
media_mime_type,
media_size
FROM messages
WHERE data IS NOT NULL
ORDER BY timestamp DESC
LIMIT 1000
""")
with open('/cases/case-2024-001/analysis/whatsapp_messages.csv', 'w') as f:
f.write("contact,direction,message,timestamp,media_type,media_size\n")
for row in cursor.fetchall():
f.write(','.join(str(x) for x in row) + '\n')
conn.close()
print("WhatsApp messages extracted successfully")
PYEOF
# Extract iOS iMessage/SMS from sms.db
python3 << 'PYEOF'
import sqlite3
db_path = "/cases/case-2024-001/mobile/ios_extraction/HomeDomain/Library/SMS/sms.db"
conn = sqlite3.connect(db_path)
cursor = conn.cursor()
cursor.execute("""
SELECT
h.id AS phone_number,
CASE WHEN m.is_from_me = 1 THEN 'SENT' ELSE 'RECEIVED' END AS direction,
m.text,
datetime(m.date/1000000000 + 978307200, 'unixepoch') AS msg_time,
m.service
FROM message m
JOIN handle h ON m.handle_id = h.ROWID
ORDER BY m.date DESC
""")
with open('/cases/case-2024-001/analysis/imessage_sms.csv', 'w') as f:
f.write("phone,direction,text,timestamp,service\n")
for row in cursor.fetchall():
f.write(','.join(str(x) for x in row) + '\n')
conn.close()
PYEOF
Step 5: Extract Location Data and Generate Report
# Extract GPS data from photos
pip install pillow
python3 << 'PYEOF'
from PIL import Image
from PIL.ExifTags import TAGS, GPSTAGS
import os, json
def get_gps(exif_data):
gps_info = {}
for key, val in exif_data.items():
decoded = GPSTAGS.get(key, key)
gps_info[decoded] = val
if'GPSLatitude'in gps_info and 'GPSLongitude'in gps_info:
lat = gps_info['GPSLatitude']
lon = gps_info['GPSLongitude']
lat_val = lat[0] + lat[1]/60 + lat[2]/3600
lon_val = lon[0] + lon[1]/60 + lon[2]/3600
if gps_info.get('GPSLatitudeRef') == 'S': lat_val = -lat_val
if gps_info.get('GPSLongitudeRef') == 'W': lon_val = -lon_val
return lat_val, lon_val
return None
locations = []
photo_dir = "/cases/case-2024-001/mobile/ios_extraction/CameraRollDomain/Media/DCIM/"for root, dirs, files in os.walk(photo_dir):
for fname in files:
if fname.lower().endswith(('.jpg', '.jpeg', '.heic')):
try:
img = Image.open(os.path.join(root, fname))
exif = img._getexif()
if exif and 34853 in exif:
coords = get_gps(exif[34853])
if coords:
locations.append({'file': fname, 'lat': coords[0], 'lon': coords[1]})
except Exception:
pass
with open('/cases/case-2024-001/analysis/photo_locations.json', 'w') as f:
json.dump(locations, f, indent=2)
print(f"Found {len(locations)} geotagged photos")
PYEOF
# Extract location history from Google Location History (Android)# File: /data/data/com.google.android.gms/databases/lbs.db# or exported Google Takeout location data
Key Concepts
Concept
Description
Logical extraction
Extracts accessible user data through device APIs (contacts, messages, photos)
File system extraction
Full access to the device file system including app databases
Physical extraction
Bit-for-bit copy of device storage including deleted and unallocated data
Android Debug Bridge for communicating with Android devices
KnowledgeC
iOS database tracking detailed app and device usage patterns
SQLite databases
Primary storage format for mobile app data (messages, contacts, history)
Checkm8
Hardware-based iOS exploit enabling extraction on A5-A11 devices
Tools & Systems
Tool
Purpose
Cellebrite UFED
Commercial mobile device acquisition and analysis platform
Cellebrite Physical Analyzer
Deep analysis of mobile device extractions
ALEAPP
Open-source Android artifact parser and report generator
iLEAPP
Open-source iOS artifact parser and report generator
libimobiledevice
Open-source iOS communication library
Magnet AXIOM
Commercial mobile and computer forensics platform
MEAT
Mobile Evidence Acquisition Toolkit
ADB
Android Debug Bridge for device interaction and data extraction
Common Scenarios
Scenario 1: Criminal Communications Investigation
Acquire device with UFED physical extraction, decrypt messaging databases, extract WhatsApp/Telegram/Signal conversations, recover deleted messages from WAL files, build communication timeline, export for legal proceedings.
Scenario 2: Employee Data Theft via Personal Phone
Perform logical extraction with employee consent, analyze corporate email and cloud storage app data, check for screenshots of confidential documents, review file transfer app activity, examine browser history for cloud uploads.
Scenario 3: Missing Person Location Tracking
Extract location data from Google Location History, parse GPS data from photos, analyze WiFi connection history for last known locations, check fitness app data for movement patterns, examine messaging apps for last communications.
Scenario 4: Child Exploitation Investigation
Physical extraction preserving all data including deleted content, hash all images against NCMEC/ICSE databases, extract communication records, recover deleted media from unallocated space, document chain of custody meticulously for prosecution.
