# Volatility — Memory Forensics Framework for Incident Response

> An advanced memory forensics framework that extracts digital artifacts from RAM dumps to support incident response, malware analysis, and security investigations.

## Install

Save in your project root:

# Volatility — Memory Forensics Framework for Incident Response

## Quick Use
```bash
# Install Volatility 3
pip install volatility3

# Or clone from source
git clone https://github.com/volatilityfoundation/volatility3.git
cd volatility3 && pip install -e .

# List processes from a memory dump
vol -f memory.dmp windows.pslist

# Scan for network connections
vol -f memory.dmp windows.netscan

# Extract command line history
vol -f memory.dmp windows.cmdline
```

## Introduction
Volatility is the leading open-source memory forensics framework, used by incident responders, malware analysts, and law enforcement to extract digital artifacts from volatile memory (RAM) images. It can reconstruct process trees, network connections, registry hives, and loaded modules from memory dumps captured during security incidents.

## What Volatility Does
- Enumerates running processes including hidden and injected processes that evade disk-based detection
- Reconstructs network connections, sockets, and DNS cache from memory snapshots
- Extracts loaded DLLs, drivers, kernel modules, and their in-memory contents
- Recovers registry hives, environment variables, and command-line arguments from process memory
- Detects code injection, API hooking, and rootkit techniques by analyzing kernel and process structures

## Architecture Overview
Volatility 3 is a complete rewrite in Python 3 with a modular plugin architecture. The framework uses symbol tables (ISF format) to interpret OS-specific memory structures without hardcoded offsets. A translation layer handles virtual-to-physical address mapping across different memory image formats (raw, ELF core, crash dump, VMware). Plugins operate on an abstract object model, making them portable across Windows, Linux, and macOS memory images with minimal changes.

## Self-Hosting & Configuration
- Install via pip with `pip install volatility3` for the latest stable release
- Download symbol tables separately for the target OS version from the Volatility Foundation repository
- Place symbol packs in the `volatility3/symbols/` directory or set the `VOLATILITY_SYMBOLS` environment variable
- Supports memory image formats: raw/dd, ELF core, Microsoft crash dump, VMware snapshot, LiME, and VirtualBox
- No external services required; runs entirely on the local machine against captured memory images

## Key Features
- Automatic OS and version detection from memory images without manual profile specification
- Plugin system allows community-contributed analysis modules for specialized artifact extraction
- Supports Windows (XP through 11), Linux (2.6+), and macOS memory image analysis
- Timeline generation correlates events across processes, files, and network activity
- YARA rule scanning searches memory contents for malware signatures and indicators of compromise

## Comparison with Similar Tools
- **Rekall** — a Google-developed fork now unmaintained; Volatility 3 is the actively maintained standard
- **MemProcFS** — provides a virtual filesystem view of memory; Volatility offers deeper plugin-based analysis with YARA integration
- **Redline** — a free GUI tool by FireEye for Windows memory analysis; Volatility supports multiple OSes and is more extensible
- **Autopsy/Sleuth Kit** — focuses on disk forensics; Volatility specializes in volatile memory analysis which captures runtime state

## FAQ
**Q: What is the difference between Volatility 2 and Volatility 3?**
A: Volatility 3 is a complete rewrite with automatic symbol resolution (no manual profiles), Python 3 support, and a cleaner plugin API. Volatility 2 required OS-specific profiles built separately.

**Q: How do I capture a memory image for analysis?**
A: Use tools like LiME (Linux), WinPmem (Windows), or osxpmem (macOS) to acquire memory images. VMware and VirtualBox snapshots also contain usable memory dumps.

**Q: Can Volatility detect rootkits?**
A: Yes, Volatility includes plugins to detect DKOM (Direct Kernel Object Manipulation), SSDT hooking, IDT modification, and other rootkit techniques by cross-referencing kernel structures.

**Q: Is Volatility suitable for live memory analysis?**
A: Volatility is designed for offline analysis of captured memory images. For live analysis, capture the memory first using an acquisition tool, then analyze with Volatility.

## Sources
- https://github.com/volatilityfoundation/volatility3
- https://volatilityfoundation.org/

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Author: AI Open Source