performing-memory-forensics-with-volatility3-plugins

name	performing-memory-forensics-with-volatility3-plugins
description	Analyze memory dumps using Volatility3 plugins to detect injected code, rootkits, credential theft, and malware artifacts in Windows, Linux, and macOS memory images.
domain	cybersecurity
subdomain	malware-analysis
tags	["memory-forensics","volatility3","malware-analysis","incident-response","process-injection","rootkit-detection","dfir"]
version	1.0
author	mahipal
license	Apache-2.0
d3fend_techniques	["Executable Denylisting","Execution Isolation","File Metadata Consistency Validation","Content Format Conversion","File Content Analysis"]
nist_csf	["DE.AE-02","RS.AN-03","ID.RA-01","DE.CM-01"]
mitre_attack	["T1027","T1055","T1140","T1497","T1003"]
source	https://github.com/mukul975/Anthropic-Cybersecurity-Skills
source_commit	04450304b12645cb2b974ab96d28c0664758a88d

Performing Memory Forensics with Volatility3 Plugins

Overview

Volatility3 (v2.26.0+, feature parity release May 2025) is the standard framework for memory forensics, replacing the deprecated Volatility2. It analyzes RAM dumps from Windows, Linux, and macOS to detect malicious processes, code injection, rootkits, credential harvesting, and network connections that disk-based forensics cannot reveal. Key plugins include windows.malfind (detecting RWX memory regions indicating injection), windows.psscan (finding hidden processes), windows.dlllist (enumerating loaded modules), windows.netscan (active network connections), and windows.handles (open file/registry handles). The 2024 Plugin Contest introduced ETW Scan for extracting Event Tracing for Windows data from memory.

When to Use

• When conducting security assessments that involve performing memory forensics with volatility3 plugins
• When following incident response procedures for related security events
• When performing scheduled security testing or auditing activities
• When validating security controls through hands-on testing

Prerequisites

• Python 3.9+ with volatility3 framework installed
• Memory dump files (.raw, .dmp, .vmem, .lime)
• Windows symbol tables (ISF files, auto-downloaded)
• Understanding of Windows process memory architecture
• YARA integration for in-memory pattern scanning

Workflow

Step 1: Process Analysis for Malware Detection

#!/usr/bin/env python3
"""Volatility3-based memory forensics automation for malware analysis."""
import subprocess
import json
import sys
import os


class Vol3Analyzer:
    """Automate Volatility3 plugin execution for malware analysis."""

    def __init__(self, dump_path, vol3_path="vol"):
        self.dump_path = dump_path
        self.vol3 = vol3_path
        .results = {}

     ():
        
        cmd = [
            .vol3, , .dump_path,
            , , plugin,
        ]
         extra_args:
            cmd.extend(extra_args)

        :
            result = subprocess.run(
                cmd, capture_output=, text=, timeout=
            )
             result.returncode == :
                 json.loads(result.stdout)
         (subprocess.TimeoutExpired, json.JSONDecodeError)  e:
            ()
         

     ():
        
        ()
        results = .run_plugin()

        injected = []
         results:
             entry  results:
                injected.append({
                    : entry.get(),
                    : entry.get(),
                    : entry.get(),
                    : entry.get(),
                    : entry.get(, )[:],
                })
                (
                      )

        .results[] = injected
         injected

     ():
        
        ()

        pslist = .run_plugin()
        psscan = .run_plugin()

          pslist   psscan:
             []

        list_pids = {e.get()  e  pslist}
        scan_pids = {e.get()  e  psscan}

        hidden = scan_pids - list_pids
         hidden:
            ()
             entry  psscan:
                 entry.get()  hidden:
                    ()

        .results[] = (hidden)
         (hidden)

     ():
        
        ()
        results = .run_plugin()

        connections = []
         results:
             entry  results:
                conn = {
                    : entry.get(),
                    : entry.get(),
                    : ,
                    : ,
                    : entry.get(),
                    : entry.get(),
                }
                connections.append(conn)

        .results[] = connections
         connections

     ():
        
        ()
        args = [, (pid)]  pid  
        results = .run_plugin(, args)

        dlls = []
         results:
             entry  results:
                dlls.append({
                    : entry.get(),
                    : entry.get(),
                    : entry.get(),
                    : entry.get(),
                    : entry.get(),
                    : entry.get(),
                })

        .results[] = dlls
         dlls

     ():
        
        ()
        results = .run_plugin(
            ,
            [, rules_path]
        )

        matches = []
         results:
             entry  results:
                matches.append({
                    : entry.get(),
                    : entry.get(),
                    : entry.get(),
                    : entry.get(),
                })

        .results[] = matches
         matches

     ():
        
        ()
        ( * )

        .detect_process_injection()
        .find_hidden_processes()
        .analyze_network()

         .results


 __name__ == :
     (sys.argv) < :
        ()
        sys.exit()

    analyzer = Vol3Analyzer(sys.argv[])
    results = analyzer.full_triage()
    (json.dumps(results, indent=, default=))