performing-mobile-device-forensics-with-cellebrite

name	performing-mobile-device-forensics-with-cellebrite
description	Acquire and analyze mobile device data using Cellebrite UFED and open-source tools to extract communications, location data, and application artifacts. Use when working with performing mobile device forensics with cellebrite.
domain	cybersecurity
tags	["forensics","mobile-forensics","cellebrite","smartphone-analysis","ios-forensics","android-forensics"]
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"]

Performing Mobile Device Forensics With Cellebrite

Overview

Cybersecurity skill for performing mobile device forensics with cellebrite. Follows industry best practices and security standards.

When to Use

Trigger phrases:

• "performing mobile device forensics with cellebrite"

• "Acquire and analyze mobile device data using Cellebrite UFED and open-source too"

• 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

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

• 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

# Example: IOC detection
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) for k, v in IOC_PATTERNS.items()}