digital forensics

Performing Mobile Device Forensics with Cellebrite

Acquire and analyze mobile device data using Cellebrite UFED and open-source tools to extract communications, location data, and application artifacts.

android-forensicscellebriteforensicsios-forensicsmobile-forensicssmartphone-analysis
Install this skill
npx skills add mukul975/Anthropic-Cybersecurity-Skills
Framework mappings

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

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 Parser
sudo 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 acquisition
sha256sum /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
UFED Universal Forensic Extraction Device - Cellebrite's flagship acquisition platform
ADB 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.

Output Format

Mobile Forensics Summary:
  Device: Samsung Galaxy S23 Ultra (SM-S918B)
  OS: Android 14, One UI 6.0
  IMEI: 353456789012345
  Extraction: Physical (via Cellebrite UFED)
  Duration: 45 minutes
 
  Extracted Data:
    Contacts:       1,234
    Call Logs:       5,678
    SMS/MMS:         3,456
    WhatsApp Msgs:   12,345 (234 deleted, recovered)
    Telegram Msgs:   2,345
    Photos/Videos:   4,567 (345 geotagged)
    Browser History: 2,345 URLs
    WiFi Networks:   67 saved connections
    Installed Apps:  145
 
  Key Findings:
    - Deleted WhatsApp conversation with suspect recovered
    - 23 geotagged photos at crime scene location
    - Browser searches related to investigation subject
    - Signal app used during incident timeframe (encrypted, partial recovery)
 
  Reports:
    ALEAPP Report:   /analysis/aleapp_report/index.html
    Messages Export: /analysis/whatsapp_messages.csv
    Locations:       /analysis/photo_locations.json
Source materials

References and resources

Everything below is rendered for inspection. Script files are read-only and never run.

References 1

api-reference.md1.9 KB

API Reference: Mobile Device Forensics with Cellebrite

Key SQLite Databases

Android

Database Path Content
mmssms.db data/data/com.android.providers.telephony/databases/ SMS/MMS messages
calllog.db data/data/com.android.providers.contacts/databases/ Call logs
contacts2.db data/data/com.android.providers.contacts/databases/ Contacts
msgstore.db data/data/com.whatsapp/databases/ WhatsApp messages
History data/data/com.android.chrome/app_chrome/Default/ Chrome history

iOS

Database Path Content
sms.db HomeDomain/Library/SMS/ iMessage and SMS
AddressBook.sqlitedb HomeDomain/Library/AddressBook/ Contacts
Safari/History.db HomeDomain/Library/Safari/ Safari browsing history
knowledgeC.db RootDomain/private/var/db/CoreDuet/Knowledge/ App usage patterns

ADB Commands (Android)

Command Description
adb devices List connected devices
adb backup -apk -shared -all -f backup.ab Full device backup
adb pull /data/data/<pkg>/ Extract app data (root required)
adb shell pm list packages List installed packages

libimobiledevice (iOS)

Command Description
idevice_id -l List connected iOS devices
ideviceinfo -u <UDID> Get device information
idevicebackup2 backup --full <dir> Create full iOS backup

Python Libraries

Library Version Purpose
sqlite3 stdlib Parse mobile app SQLite databases
csv stdlib Export extracted data
pathlib stdlib Navigate extraction directory structure

References

Scripts 1

agent.py8.8 KB
Display-only source. This catalog never executes bundled scripts.
#!/usr/bin/env python3
"""Agent for performing mobile device forensics.

Analyzes mobile device extractions by parsing SQLite databases
for messages, call logs, contacts, and location data from
iOS and Android file system extractions.
"""

import sqlite3
import json
import sys
import re
from pathlib import Path
from datetime import datetime

_SAFE_TABLE_RE = re.compile(r'^[a-zA-Z_][a-zA-Z0-9_]*$')


class MobileForensicsAgent:
    """Parses mobile device extraction data for forensic analysis."""

    def __init__(self, extraction_dir, output_dir, platform="android"):
        self.extraction_dir = Path(extraction_dir)
        self.output_dir = Path(output_dir)
        self.output_dir.mkdir(parents=True, exist_ok=True)
        self.platform = platform

    def _query_db(self, db_path, query, params=None):
        """Execute a query against a SQLite database."""
        if not Path(db_path).exists():
            return []
        try:
            conn = sqlite3.connect(db_path)
            conn.row_factory = sqlite3.Row
            cursor = conn.cursor()
            cursor.execute(query, params or [])
            results = [dict(row) for row in cursor.fetchall()]
            conn.close()
            return results
        except sqlite3.Error as e:
            return [{"error": str(e), "db": str(db_path)}]

    def extract_sms_android(self):
        """Extract SMS/MMS messages from Android mmssms.db."""
        db_path = self.extraction_dir / "data/data/com.android.providers.telephony/databases/mmssms.db"
        return self._query_db(str(db_path), """
            SELECT address, body, type,
                   datetime(date/1000, 'unixepoch') AS msg_time,
                   read, seen
            FROM sms ORDER BY date DESC LIMIT 5000
        """)

    def extract_sms_ios(self):
        """Extract iMessage/SMS from iOS sms.db."""
        db_path = self.extraction_dir / "HomeDomain/Library/SMS/sms.db"
        return self._query_db(str(db_path), """
            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 LIMIT 5000
        """)

    def extract_call_log_android(self):
        """Extract call logs from Android contacts2.db."""
        db_path = self.extraction_dir / "data/data/com.android.providers.contacts/databases/calllog.db"
        return self._query_db(str(db_path), """
            SELECT number, name,
                   CASE type WHEN 1 THEN 'INCOMING' WHEN 2 THEN 'OUTGOING'
                        WHEN 3 THEN 'MISSED' ELSE 'UNKNOWN' END AS call_type,
                   duration,
                   datetime(date/1000, 'unixepoch') AS call_time
            FROM calls ORDER BY date DESC LIMIT 2000
        """)

    def extract_contacts_android(self):
        """Extract contacts from Android contacts database."""
        db_path = self.extraction_dir / "data/data/com.android.providers.contacts/databases/contacts2.db"
        return self._query_db(str(db_path), """
            SELECT display_name, data1 AS phone_or_email, mimetype
            FROM raw_contacts rc
            JOIN data d ON rc._id = d.raw_contact_id
            WHERE mimetype IN (
                'vnd.android.cursor.item/phone_v2',
                'vnd.android.cursor.item/email_v2'
            ) ORDER BY display_name LIMIT 5000
        """)

    def extract_whatsapp_messages(self):
        """Extract WhatsApp messages from msgstore.db."""
        db_path = self.extraction_dir / "data/data/com.whatsapp/databases/msgstore.db"
        return self._query_db(str(db_path), """
            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 5000
        """)

    def extract_browser_history_android(self):
        """Extract Chrome browser history from Android."""
        db_path = self.extraction_dir / "data/data/com.android.chrome/app_chrome/Default/History"
        return self._query_db(str(db_path), """
            SELECT url, title, visit_count,
                   datetime(last_visit_time/1000000 - 11644473600, 'unixepoch') AS visit_time
            FROM urls ORDER BY last_visit_time DESC LIMIT 2000
        """)

    def extract_wifi_history(self):
        """Extract saved WiFi networks."""
        if self.platform == "android":
            wifi_conf = self.extraction_dir / "data/misc/wifi/WifiConfigStore.xml"
            if wifi_conf.exists():
                content = wifi_conf.read_text(errors="ignore")
                import re
                ssids = re.findall(r'"SSID"[^>]*>([^<]+)', content)
                return [{"ssid": s} for s in ssids]
        return []

    def extract_installed_apps(self):
        """List installed applications."""
        apps = []
        if self.platform == "android":
            app_dir = self.extraction_dir / "data/data"
            if app_dir.exists():
                for pkg in sorted(app_dir.iterdir()):
                    if pkg.is_dir():
                        apps.append({
                            "package": pkg.name,
                            "has_databases": (pkg / "databases").exists(),
                        })
        return apps

    def search_keyword(self, keyword):
        """Search across extracted databases for a keyword."""
        hits = []
        for db_file in self.extraction_dir.rglob("*.db"):
            try:
                conn = sqlite3.connect(str(db_file))
                cursor = conn.cursor()
                cursor.execute("SELECT name FROM sqlite_master WHERE type='table'")
                tables = [row[0] for row in cursor.fetchall()]
                for table in tables:
                    if not _SAFE_TABLE_RE.match(table):
                        continue
                    try:
                        cursor.execute(f"SELECT * FROM [{table}] LIMIT 1")
                        columns = [desc[0] for desc in cursor.description]
                        for col in columns:
                            if not _SAFE_TABLE_RE.match(col):
                                continue
                            cursor.execute(
                                f"SELECT [{col}] FROM [{table}] WHERE [{col}] LIKE ?",
                                [f"%{keyword}%"]
                            )
                            matches = cursor.fetchall()
                            if matches:
                                hits.append({
                                    "database": str(db_file.relative_to(self.extraction_dir)),
                                    "table": table,
                                    "column": col,
                                    "match_count": len(matches),
                                })
                    except sqlite3.Error:
                        continue
                conn.close()
            except sqlite3.Error:
                continue
        return hits

    def generate_report(self):
        """Generate comprehensive mobile forensics report."""
        report = {
            "extraction_dir": str(self.extraction_dir),
            "platform": self.platform,
            "report_date": datetime.utcnow().isoformat(),
        }

        if self.platform == "android":
            report["sms"] = {"count": len(self.extract_sms_android())}
            report["call_log"] = {"count": len(self.extract_call_log_android())}
            report["contacts"] = {"count": len(self.extract_contacts_android())}
            report["whatsapp"] = {"count": len(self.extract_whatsapp_messages())}
            report["browser_history"] = {"count": len(self.extract_browser_history_android())}
        elif self.platform == "ios":
            report["imessage_sms"] = {"count": len(self.extract_sms_ios())}

        report["wifi_networks"] = self.extract_wifi_history()
        report["installed_apps"] = {"count": len(self.extract_installed_apps())}

        report_path = self.output_dir / "mobile_forensics_report.json"
        with open(report_path, "w") as f:
            json.dump(report, f, indent=2)

        print(json.dumps(report, indent=2))
        return report


def main():
    if len(sys.argv) < 3:
        print("Usage: agent.py <extraction_dir> <output_dir> [android|ios]")
        sys.exit(1)

    extraction_dir = sys.argv[1]
    output_dir = sys.argv[2]
    platform = sys.argv[3] if len(sys.argv) > 3 else "android"

    agent = MobileForensicsAgent(extraction_dir, output_dir, platform)
    agent.generate_report()


if __name__ == "__main__":
    main()
Keep exploring