Introduction

Forensic experts are frequently summoned in the aftermath of a security breach or to ascertain whether an incident has transpired. Their primary objective is to obtain a snapshot of the compromised system's RAM to capture cryptographic keys or other data that solely exists in memory. Fortunately, a group of skilled developers has devised an entire Python framework called Volatility, which is aptly suited for this purpose and is touted as an advanced memory forensics framework. Incident responders, forensic examiners, and malware analysts can employ Volatility for a plethora of other tasks as well, such as inspecting kernel objects, examining and dumping processes, and more. Let's begin!

Definition of Memory Forensics

Memory forensics refers to the analysis and investigation of a computer's volatile memory (RAM) to identify and extract digital evidence related to security incidents, cybercrime, or other computer-related crimes. This type of forensic analysis can provide valuable insights into the activities of an attacker or user, even if they have attempted to cover their tracks by deleting or altering files on the hard drive.

Memory forensics involves capturing and analyzing the contents of a computer's RAM, which can include running processes, network connections, open files, and other data. The process of memory forensics typically involves using specialized tools and techniques to create a memory dump, which is a snapshot of the current state of the computer's RAM at a specific point in time. This memory dump can then be analyzed to identify potential evidence of malicious activity, such as the presence of malware, unauthorized access attempts, or stolen data.

Memory forensics is a valuable tool for investigating security incidents and computer-related crimes, as it can provide crucial information about the actions of an attacker or user that may be difficult to obtain through other methods. However, it requires specialized knowledge and expertise in computer forensics and cybersecurity, as well as access to appropriate tools and techniques.

Tracing the Evolution of Volatility

Volatility is a free and open-source memory forensics framework that enables investigators to extract and analyze digital evidence from a computer's volatile memory (RAM). The framework provides a variety of tools and plugins for analyzing memory dumps, such as identifying running processes, open network connections, loaded drivers, and other volatile artifacts containing valuable information for forensic investigations.

Some of the notable features of Volatility include its ability to analyze memory dumps from Windows, Linux, and Mac systems, support for an extensive range of file formats, and an extensible plugin architecture that allows users to develop custom plugins for specific analysis tasks. Furthermore, Volatility incorporates several built-in plugins for common analysis tasks, including malware identification, rootkit detection, and network connection analysis.

Volatility is applicable in various forensic investigations, such as incident response, malware analysis, and digital forensics. However, it necessitates specialized knowledge and expertise in memory forensics and cybersecurity for effective utilization. Additionally, Volatility is often employed alongside other forensic tools and techniques to provide a more comprehensive understanding of a security incident or computer-related crime.

Volatility's origins can be traced back to a project initiated by AAron Walters and Nick L. Petroni, Jr. who were conducting memory forensics research. The first version of the framework, dubbed "volatools," was released in 2007. It gained traction and recognition for its innovative approach to memory analysis, which encompassed both Windows and Linux operating systems.

Walters, Aaron, and Nick L. Petroni. "Volatools: Integrating volatile memory into the digital investigation process." Black Hat DC 2007 (2007): 1-18.

Following its initial release, the Volatility project attracted other skilled developers who contributed to its expansion and refinement. The second iteration, Volatility 2, was launched in 2011, featuring significant enhancements such as improved performance, more comprehensive analysis capabilities, and support for additional operating systems like macOS.

Volatility 3, a complete rewrite of the Volatility memory forensics framework, was publicly released in 2020. This rewrite aimed to address technical and performance challenges associated with the original 2007 code base. A significant benefit of the rewrite is that Volatility 3 can now be released under the Volatility Software License (VSL), which aligns more closely with the Volatility community's objectives.

Here are some key differences between Volatility 2 and Volatility 3:

• Multi-platform support: Volatility 3 is designed to function on multiple platforms, including Windows, Linux, and macOS. This enables analysts to use the same toolset across various operating systems.

• Plugin architecture: Volatility 3 features a more modular plugin architecture that allows for enhanced extensibility and customizability. Plugins can be developed in Python and executed independently or combined with other plugins for more complex analysis.

• Improved performance: Volatility 3 boasts improved performance compared to Volatility 2, attributed to several changes, including the use of C++ for performance-critical operations, memory management improvements, and parallel memory dump analysis capabilities.

• New features: Volatility 3 incorporates new features unavailable in Volatility 2, such as support for virtual machine memory analysis, improved kernel data structure analysis, and new plugins for Chrome browser data and other application analyses.

Overall, Volatility 3 represents a significant update to the Volatility memory forensics framework, offering improved performance, new features, and enhanced multi-platform support.

Installing Volatility 3: A Step-by-Step Guide

In this section, I will provide a comprehensive, step-by-step guide to installing Volatility 3, enabling you to harness the power of this advanced memory forensics framework.

Before we begin, please note that Volatility 3 requires Python 3.7 or newer to function correctly. Ensure that you have the appropriate Python version installed on your system.

Install Python 3:

Volatility 3 is a Python-based tool, so you will need to install Python 3 first. You can download the latest version of Python 3 from the official website (https://www.python.org/downloads/). Make sure to select the appropriate version for your operating system (Windows in this case).

Download the Volatility 3 source code:

You can download the latest version of the Volatility 3 source code from the official GitHub repository (https://github.com/volatilityfoundation/volatility3). Click on the "Code" button and select "Download ZIP" to download the source code as a ZIP file. Once the ZIP file is downloaded, extract the contents to a location on your machine where you want to install Volatility 3.

You can also clone the official repository from GitHub by executing the following command in your terminal or command prompt:

git clone https://github.com/volatilityfoundation/volatility3.git

Install Volatility 3 dependencies:

Open a command prompt and navigate to the location where you extracted the Volatility 3 source code. Run the command to install the necessary dependencies for Volatility 3.

cd volatility3/
pip3 install -r requirements.txt

Verify the installation:

Once the installation is complete, you can verify that Volatility 3 is installed correctly by running the command.

python3 vol.py -h

This will display information about the available plugins and other information related to Volatility 3.

python3 vol.py -h

Volatility 3 Framework 2.4.2

usage: volatility [-h] [-c CONFIG] [--parallelism [{processes,threads,off}]] [-e EXTEND] [-p PLUGIN_DIRS] [-s SYMBOL_DIRS] [-v] [-l LOG] [-o OUTPUT_DIR] [-q] [-r RENDERER] [-f FILE] [--write-config]

[--save-config SAVE_CONFIG] [--clear-cache] [--cache-path CACHE_PATH] [--offline] [--single-location SINGLE_LOCATION] [--stackers [STACKERS ...]]

[--single-swap-locations [SINGLE_SWAP_LOCATIONS ...]]

plugin ...

An open-source memory forensics framework

optional arguments:

-h, --help Show this help message and exit, for specific plugin options use 'volatility <pluginname> --help'

-c CONFIG, --config CONFIG

Load the configuration from a json file

--parallelism [{processes,threads,off}]

Enables parallelism (defaults to off if no argument given)

-e EXTEND, --extend EXTEND

Extend the configuration with a new (or changed) setting

-p PLUGIN_DIRS, --plugin-dirs PLUGIN_DIRS

Semi-colon separated list of paths to find plugins

-s SYMBOL_DIRS, --symbol-dirs SYMBOL_DIRS

Semi-colon separated list of paths to find symbols

-v, --verbosity Increase output verbosity

-l LOG, --log LOG Log output to a file as well as the console

-o OUTPUT_DIR, --output-dir OUTPUT_DIR

Directory in which to output any generated files

-q, --quiet Remove progress feedback

-r RENDERER, --renderer RENDERER

Determines how to render the output (quick, none, csv, pretty, json, jsonl)

-f FILE, --file FILE Shorthand for --single-location=file:// if single-location is not defined

--write-config Write configuration JSON file out to config.json

--save-config SAVE_CONFIG

Save configuration JSON file to a file

--clear-cache Clears out all short-term cached items

--cache-path CACHE_PATH

Change the default path (/Users/cpuu/.cache/volatility3) used to store the cache

--offline Do not search online for additional JSON files

--single-location SINGLE_LOCATION

Specifies a base location on which to stack

--stackers [STACKERS ...]

List of stackers

--single-swap-locations [SINGLE_SWAP_LOCATIONS ...]

Specifies a list of swap layer URIs for use with single-location

Congratulations! You have successfully installed Volatility 3. You can now use the tool to analyze memory dumps and perform forensic investigations. With Volatility 3 successfully installed, you are now ready to explore its powerful features and utilize its advanced memory forensics capabilities.

In the upcoming post, a demonstration will be provided for conducting memory forensics using Volatility 3 on various operating systems, including Windows, Linux, and macOS.