Volatility — Memory Forensics and Analysis

Volatility is a powerful, open-source memory forensics framework used to analyze volatile data captured from a system’s RAM. It allows investigators to dig into what was happening in memory at the time of acquisition — processes, network connections, registry hives, loaded DLLs, and even hidden malware.

Unlike disk forensics, memory forensics gives a snapshot of the live system state, including activities that never touch the disk — making Volatility essential for malware detection, incident response, and threat hunting.

What Is Memory Forensics?

Memory forensics focuses on analyzing the contents of RAM captured during or shortly after an incident. This volatile data reveals:

• Running processes and their hierarchy
• Command-line history and open files
• Network connections (open ports, remote IPs)
• Injected code, in-memory malware, or rootkits
• User activity, clipboard data, cached credentials
• Decrypted content from encrypted sessions (sometimes)

Memory evidence is often the missing link in understanding advanced attacks — ransomware, APTs, and live malware infections.

What Is Volatility?

Volatility is a Python-based framework created to analyze memory dumps from Windows, Linux, and macOS systems. It extracts forensic artifacts using plugins (modular commands) and supports multiple image formats like .raw, .vmem, .mem, .dmp, or .lime.

note

The Volatility project has two main versions:

• Volatility 2 (stable, Python 2/3 compatible)
• Volatility 3 (next-generation rewrite, Python 3 only, improved plugin structure and speed)

Installing Volatility

Windows / macOS / Linux (Python environment)

Volatility 3 (recommended)

# Clone the Volatility3 repository
git clone https://github.com/volatilityfoundation/volatility3.git
cd volatility3

# Install dependencies
python3 -m venv venv
source venv/bin/activate  # or .\venv\Scripts\activate on Windows
pip install -r requirements.txt

Run Volatility:

python3 vol.py -h

Volatility 2 (legacy)

git clone https://github.com/volatilityfoundation/volatility.git
cd volatility
python vol.py -h

Acquiring Memory Dumps

Volatility works on memory images captured with tools like:

OS	Memory Capture Tools
Windows	FTK Imager, DumpIt, WinPMem
Linux	LiME, AVML, fmem
macOS	osxpmem

Example:

winpmem.exe -o memory_dump.raw

Once captured, verify the hash (MD5/SHA256) for integrity before analysis.

Basic Workflow: Analyzing Memory with Volatility

1. Identify the image information

   python3 vol.py -f memory_dump.raw windows.info

   → Determines OS version, architecture, and build number.

2. List running processes

   python3 vol.py -f memory_dump.raw windows.pslist

   → Displays process IDs, parent processes, and creation times.

3. Detect hidden/malicious processes

   python3 vol.py -f memory_dump.raw windows.psscan

   → Scans for processes not linked in standard structures (often malware).

4. View network connections

   python3 vol.py -f memory_dump.raw windows.netstat

   → Shows active TCP/UDP connections, local/remote IPs, and ports.

5. Analyze DLLs loaded by processes

   python3 vol.py -f memory_dump.raw windows.dlllist --pid 1234

6. Dump suspicious processes for malware analysis

   python3 vol.py -f memory_dump.raw windows.memmap --pid 1234
   python3 vol.py -f memory_dump.raw windows.dumpfiles --pid 1234

7. Check for registry keys in memory

   python3 vol.py -f memory_dump.raw windows.registry.printkey --key "Software\\Microsoft\\Windows\\CurrentVersion\\Run"

8. Command history (useful for attack reconstruction)

   python3 vol.py -f memory_dump.raw windows.cmdline

Advanced Plugins & Use Cases

Malware Detection

• malfind — Identify injected code in process memory.
• modules — List kernel modules to find unauthorized drivers.
• ldrmodules — Detect unlinked DLLs (common stealth technique).
• svcscan — List running services.

User Activity

• consoles — View console commands entered by users.
• clipboard — Retrieve clipboard content.
• shellbags — Track folder browsing activity.
• iehistory — Analyze Internet Explorer browsing data.

Network & Persistence

• netscan — Discover network connections (Volatility 3).
• autoruns — Detect persistence mechanisms (startup entries, services).
• handles — Examine open files, mutexes, and registry handles.

Example Investigation Scenario

Scenario: A suspicious binary is executed on a user machine. The system is imaged and a memory dump is captured.

Step-by-step:

1. Identify system info:

   python3 vol.py -f infected.raw windows.info

2. List all processes:

   python3 vol.py -f infected.raw windows.pslist

   You find suspicious.exe running under explorer.exe (PID 3260).

3. Check for injected code:

   python3 vol.py -f infected.raw windows.malfind --pid 3260

   → Volatility reports injected memory sections.

4. Dump the process memory:

   python3 vol.py -f infected.raw windows.dumpfiles --pid 3260

   → Extract to analyze with antivirus or YARA.

5. Review network connections:

   python3 vol.py -f infected.raw windows.netstat

   → Detects a connection to a suspicious external IP: 185.142.213.50:443.

6. Generate a quick timeline:

   python3 vol.py -f infected.raw timeliner

   → Cross-reference activity times with other forensic data.

Volatility 3 Highlights

Volatility 3 brings:

• Python 3 support and modular architecture
• Cleaner plugin design and better cross-platform coverage
• Faster scanning and reduced memory footprint
• Easier creation of custom plugins

note

Example Volatility 3 command:

python3 vol.py -f mem.raw windows.pslist

Hands-On Lab Ideas

Lab 1: Process Enumeration

• Capture memory from a Windows VM with several open programs.
• Use Volatility to list processes, identify parents/children, and spot anomalies.

Lab 2: Hidden Malware Discovery

• Run a sample malware in a sandbox VM (e.g., Meterpreter payload).
• Dump memory and use malfind and psscan to detect hidden processes.

Lab 3: Command History & Network Tracing

• Execute commands in PowerShell and open web connections.
• Analyze with cmdline, netscan, and consoles plugins.

Combining Volatility with Other Tools

Purpose	Tool
Disk Analysis	Autopsy / Sleuth Kit
Malware Sandboxing	Cuckoo Sandbox
YARA Rule Scanning	YARA
Memory Imaging	WinPMem / LiME
Log Correlation	ELK / Timesketch

Volatility data integrates well with Autopsy (via case linking), enabling a full DFIR workflow: disk + memory + network.

Legal & Operational Best Practices

• Capture memory immediately after isolating the system — delays can lose critical data.
• Always hash memory dumps before and after transfer to preserve integrity.
• Use write-blockers and read-only mounts when analyzing extracted files.
• Document every action (commands, timestamps, tool versions) for chain of custody.

Summary

Concept	Key Point
Tool	Volatility (memory forensics framework)
Input	RAM image (.raw, .vmem, .dmp, .lime)
Output	Processes, network, code injections, registry, user activity
Use Case	Malware investigation, IR, APT detection
Strength	Visibility into volatile, in-memory evidence
Skill Level	Intermediate to advanced (with practice)

Volatility gives unparalleled visibility into what was actually happening at runtime — information that disk forensics alone can’t provide. Mastering Volatility means you can reconstruct live attacks, identify stealthy implants, and make evidence-based decisions in your IR workflows.