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12.2 Memory Analysis with Volatility 3: pslist, pstree, malfind, netscan & Vadinfo
Computer Hacking Forensic Investigator CHFI 312-49
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1 Chapter 1: Foundations of Digital Forensics & Cybercrime Investigation
1.1 Cybercrime Classifications, Forensic Science Principles & Legal Admissibility 1.2 RFC 3227 Order of Volatility, Live vs. Dead Acquisition & Forensic Readiness 1.3 Indicators of Compromise (IoCs), Cyber Attribution & Threat Intelligence 1.4 AI-Assisted Forensics, GitOps, Automation/Orchestration & Forensics-as-a-Service (FaaS)
2 Chapter 2: Modern Forensic Environments & Emerging Threats
2.1 Fileless Malware, In-Memory Execution & Living-off-the-Land (LotL) 2.2 IoT & Operational Technology (OT/SCADA) Forensic Fundamentals 2.3 Dark Web, Tor Network, Onion Routing & Hidden Services Forensics 2.4 Blockchain Transactions, Cryptocurrency Tracing & Digital Asset Investigations
3 Chapter 3: Legal Frameworks, Search Warrants & Evidence Rules
3.1 Federal Rules of Evidence (FRE 702, 901, 902, 1001-1003) & Daubert/Frye Standards 3.2 Fourth Amendment, Warrant Exceptions, Workplace Search & Consent Protocols 3.3 International Standards: ISO/IEC 27037, ACPO Principles & ENFSI Guidelines 3.4 Cybercrime Statutes, Privacy Compliance, CAN-SPAM & International Cooperation
4 Chapter 4: eDiscovery, Chain of Custody & Professional Ethics
4.1 Electronic Discovery Reference Model (EDRM) & Legal Spoliation Holds 4.2 Chain of Custody Documentation, Evidence Packaging & Tamper-Evident Handling 4.3 Expert Witness Testimony, Report Writing Standards & EC-Council Code of Ethics
5 Chapter 5: Storage Architecture, Boot Sequences & Media Forensics
5.1 Storage Media Architecture: Sectors, Clusters, RAM/File Slack & Unallocated Space 5.2 Partition Tables & Boot Structures: MBR, GPT, VBR, BIOS vs. UEFI Secure Boot 5.3 RAID Reconstruction (RAID 0, 1, 5, 6, 10) & SSD Forensic Challenges (TRIM, Wear Leveling)
6 Chapter 6: Windows File Systems: NTFS & FAT
6.1 NTFS Architecture: Master File Table ($MFT) Record Structure & Attribute Headers 6.2 NTFS Artifacts: Alternate Data Streams (ADS), Volume Shadow Copies (VSS) & $LogFile 6.3 FAT12/16/32 and exFAT Architecture, Directory Entries & File Deletion Mechanics
7 Chapter 7: Non-Windows File Systems & File Signatures
7.1 Linux File Systems: ext2/ext3/ext4 Inode Architecture, Superblocks & Journaling Forensics 7.2 macOS File Systems: HFS+ and Apple File System (APFS) Architecture & Snapshots 7.3 File Signatures, Magic Bytes, Hex Headers/Footers & File Carving Techniques
8 Chapter 8: Investigation Lifecycle, Lab Setup & First Response
8.1 Forensic Laboratory Accreditation: ISO/IEC 17025 Standards & Hardware/Software Controls 8.2 First Responder Duties: Crime Scene Containment, Electronic Triage & Volatile Triage 8.3 Physical Evidence Packaging, Anti-Static/Faraday Protection & Transport Security
9 Chapter 9: Forensic Acquisition & Verification Methodology
9.1 Hardware and Software Write-Blockers: Operational Validation & Bridge Verification 9.2 Bit-Stream Disk Imaging, Physical vs. Logical vs. Targeted Acquisition Methodology 9.3 Forensic Image Formats (RAW/DD, E01, AFF) & Cryptographic Hashing Verification (MD5, SHA-256)
10 Chapter 10: Anti-Forensics Techniques & Countermeasures
10.1 Steganography Detection: Spatial/Frequency Domain Steganalysis & Hidden Carrier Extraction 10.2 Timestomping Countermeasures: Comparing $STANDARD_INFORMATION vs $FILE_NAME Timestamps 10.3 Secure Wiping, Degaussing, File Shredding Countermeasures & Residual Artifact Recovery
11 Chapter 11: Windows Operating System Forensics
11.1 Windows Registry Forensics: SYSTEM, SOFTWARE, SAM & NTUSER.DAT Hive Analysis 11.2 Execution Artifacts: Prefetch (.pf), Shimcache (AppCompatCache), Amcache.hve & UserAssist 11.3 User Activity Artifacts: ShellBags, LNK Shortcut Files, Jump Lists & Recycle Bin ($I/$R) 11.4 Windows Event Log Forensics: Security Audit IDs (4624, 4625, 4688, 4720, 7045) & EVTX Parsing
12 Chapter 12: Memory Forensics & Malware Analysis
12.1 Volatile Memory Acquisition: RAM Capture, Crash Dumps, Hiberfil.sys & Pagefile.sys 12.2 Memory Analysis with Volatility 3: pslist, pstree, malfind, netscan & Vadinfo 12.3 Static Malware Triage: Strings, PE Headers, Import Tables, Hashes & YARA Rule Matching 12.4 Dynamic Malware Analysis: Sandboxing, Process Monitor (Procmon), Regshot & Network Callouts 12.5 .NET Managed Malware Analysis & the GOOTLOADER Fileless Infection Chain
13 Chapter 13: Network, Web Application & Email Forensics
13.1 Network Forensics: PCAP Analysis, Protocol Dissection, Snort/Suricata & Flow Analysis 13.2 Web Server Forensics: Apache/IIS Access Logs, SQL Injection, XSS & Web Shell Detection 13.3 Email Forensics: RFC 5322 Headers, Authentication (SPF, DKIM, DMARC) & Mbox/PST Extraction 13.4 SIEM Event Correlation, Cross-Source Timeline Analysis & Super-Timeline Construction 13.5 Investigating Directory Traversal, Command Injection, Parameter Tampering, XXE, Cookie Poisoning & Brute Force 13.6 Network Device & Service Log Forensics: Firewall, IDS, Honeypot, Router, DHCP, Switch, VPN, DNS & SSH 13.7 Wireless Network Forensics: Rogue APs, Evil Twins, MAC Spoofing, Deauthentication & Jamming
14 Chapter 14: Mobile, Cloud & IoT Forensics
14.1 Mobile Device Forensics: Manual, Logical, File System & Physical Extraction (Android/iOS) 14.2 Mobile Artifact Analysis: Call Logs, SMS/Chat SQLite Databases, Location Data & Plists 14.3 Cloud Forensics: AWS CloudTrail, Azure Activity Logs & Multi-Tenant Evidence Collection 14.4 IoT Forensics: UART/JTAG Physical Interfaces, Firmware Extraction & Sensor Telemetry
15 Chapter 15: Commercial & Open-Source Forensic Suites
15.1 Commercial Forensic Platforms: EnCase Forensic, FTK (Forensic Toolkit) & FTK Imager 15.2 Open-Source Forensic Platforms: Autopsy, The Sleuth Kit (TSK) & SIFT/CAINE Workstations 15.3 Specialized DFIR Utilities: Eric Zimmerman Tools (EZ Tools) & Sysinternals Forensic Suite
16 Chapter 16: Analysis Toolkits, Python Scripting & Tool Validation
16.1 Reverse Engineering & Password Cracking: Ghidra, IDA Pro, Hashcat & John the Ripper 16.2 Python for Digital Forensics: Parsing Raw Artifacts, Custom Scapy Scripts & Evidence Automation 16.3 Forensic Tool Testing & Validation: NIST CFTT Methodology, ISO/IEC 17025 Standards & Daubert Compliance
17 Chapter 17: Database Forensics & Web Application Firewalls
17.1 MySQL Forensics: Internal Architecture, Data Directory, Information Schema & Utility Programs 17.2 SQL Server Forensics: SSMS, ApexSQL, SQLCMD Transaction-Log Collection, Plan Cache & Trace Files 17.3 Web Application Firewall (WAF) Architecture, Audit Logs, Benefits & Limitations
18 Chapter 18: Social, Virtualized, Multimedia & Browser Investigations
18.1 Social Media, Social Engineering, Insider Threat & Identity Theft Forensics 18.2 Virtualization, Container & Microservices Forensics, Bluetooth and Electron Chat Applications 18.3 Multimedia Forensics: Image, Audio & Video Authentication, EXIF, PRNU and Deepfake Detection 18.4 Browser Artifact Recovery, Private Browsing, USB Enumeration, Partition Recovery & Packer Unpacking Tools
12.2 Memory Analysis with Volatility 3: pslist, pstree, malfind, netscan & Vadinfo
Key Takeaways Explain This Topic AI
Volatility 3 redesigns memory forensics architecture by replacing legacy static OS profiles with platform-agnostic symbol tables (Intermediate Symbol Format - ISF) dynamically retrieved from symbol servers.
Process enumeration through windows.pslist follows the kernel EPROCESS circular doubly-linked list (ActiveProcessLinks), whereas windows.pstree maps parent-child process hierarchies (PPID to PID).
Adversaries employ Direct Kernel Object Manipulation (DKOM) to unlink malicious processes from ActiveProcessLinks; windows.psscan bypasses this evasion by carving physical memory for unlinked _EPROCESS pool tags.
Code injection, process hollowing, and reflective DLL loading are detected using windows.malfind (identifying executable, non-file-backed VAD memory pages with PAGE_EXECUTE_READWRITE permissions) and windows.ldrmodules (cross-referencing PEB module lists).
Network telemetry is reconstructed using windows.netscan, which carves memory for TCP endpoint and UDP listener pool structures, exposing active and terminated C2 network sockets.
Last updated: September 2026
12.2 Memory Analysis with Volatility 3: pslist, pstree, malfind, netscan & Vadinfo
Quick Answer: The Volatility Framework is the industry-standard open-source platform for volatile memory forensics. Volatility 3 eliminates legacy Volatility 2 operating system "profiles" by adopting Intermediate Symbol Format (ISF) JSON symbol tables derived from Microsoft Program Database (PDB) debugging symbols. In Volatility 3, running processes are enumerated using windows.pslist (which traverses the ActiveProcessLinks doubly-linked list in the EPROCESS block) and windows.pstree (which displays parent-child process hierarchies). To defeat Direct Kernel Object Manipulation (DKOM) rootkits that unlink processes from ActiveProcessLinks , examiners run windows.psscan to carve unallocated memory for _EPROCESS pool tags. Injected code and reflective DLLs are uncovered using windows.malfind (flagging non-file-backed memory pages with PAGE_EXECUTE_READWRITE permissions hosting executable code) and windows.ldrmodules (cross-referencing PEB loader lists), while network connections are carved via windows.netscan .
Volatility Architecture: Volatility 2 vs. Volatility 3
Analyzing a physical memory dump requires understanding the precise internal structures of the target operating system's kernel. Operating system kernels frequently shift data structure offsets between minor service packs, build numbers, and monthly cumulative updates. +-------------------------------------------------------------------------+
- | VOLATILITY 2 vs. VOLATILITY 3 ARCHITECTURE |
- +-------------------------------------------------------------------------+
- | Architectural Dimension | Volatility 2 (Legacy) | Volatility 3 (Current) |
- |-------------------------|------------------------|------------------------------|
- | **Symbol Resolution** | Hardcoded static | Dynamic Intermediate Symbol |
- | | profiles (e.g., | Format (ISF) JSON tables; |
- | | Win7SP1x64) | automated PDB symbol download|
- |-------------------------|------------------------|------------------------------|
- | **Operating System** | Tightly bound to OS | Platform-agnostic core |
- | **Abstraction** | versions; requires manual| engine; auto-detects Windows, |
- | | profile generation | Linux, and macOS kernels |
- |-------------------------|------------------------|------------------------------|
- | **Codebase Base** | Python 2.7 (Deprecated)| Python 3 (Object-oriented |
- | | | modular plugin architecture) |
- |-------------------------|------------------------|------------------------------|
- | **Execution Syntax** | vol.py -f mem.raw | vol.py -f mem.raw |
- | | --profile=Win10x64... | windows.pslist |
- +-------------------------------------------------------------------------+
- The Intermediate Symbol Format (ISF)
In Volatility 2, if an examiner analyzed a memory dump from a newly patched Windows 10 build that lacked a predefined profile, analysis failed until the examiner manually generated a custom profile using debugging tools.
Volatility 3 resolves this structural limitation through Intermediate Symbol Format (ISF) :
When ingesting a raw memory image, Volatility 3 scans the dump for the Windows kernel image ( ntkrnlmp.exe or ntoskrnl.exe ).
It extracts the GUID and age of the corresponding Microsoft Program Database ( PDB ) debugging symbol file embedded within the kernel header.
Volatility 3 automatically connects to Microsoft's public symbol server, downloads the exact PDB file corresponding to that specific kernel build, and converts it into an ISF JSON symbol table.
This symbol table provides byte-exact offsets for critical structures ( _EPROCESS , _KPROCESS , _VAD , _PEB , _ETHREAD ), ensuring complete analytical accuracy across all operating system updates.
Process Enumeration Internals & DKOM Detection
In the Windows NT kernel, every executing process is represented by an Executive Process Block ( EPROCESS ) , an opaque kernel-mode data structure residing in system pool memory. Understanding how Windows links and tracks these blocks is essential for identifying stealth rootkits. Kernel ActiveProcessLinks Doubly-Linked Circular List:
- +------------------+ +------------------+ +------------------+
- | EPROCESS: PID 4 | | EPROCESS: PID 480| | EPROCESS: PID 720|
- | (System) | | (smss.exe) | | (csrss.exe) |
- | | | | | |
- | [Flink] -------->|-----> | [Flink] -------->|-----> | [Flink] -------->|--+ (Loops to)
- | [Blink] <--------|<----- | [Blink] <--------|<----- | [Blink] <--------|<-+ (Head)
- +------------------+ +------------------+ +------------------+
- | (Attacker unlinks pointers via DKOM)
- ▼
- +------------------+
- | HIDDEN PROCESS |
- | (rootkit.exe) |
- | PID 9999 |
- | [Flink] points |
- | to itself |
- +------------------+
- 1. windows.pslist : Walking the ActiveProcessLinks List
The windows.pslist plugin walks the ActiveProcessLinks member of the _EPROCESS structure. ActiveProcessLinks is a circular doubly-linked list ( LIST_ENTRY ) containing a Forward Pointer ( Flink ) pointing to the next process's _EPROCESS structure and a Backward Pointer ( Blink ) pointing to the previous process.
Limitations: Because windows.pslist relies entirely on pointer traversal, it only sees processes currently linked into the kernel scheduler's active list. If an attacker unlinks an entry, pslist will never encounter it.
2. windows.pstree : Reconstructing Parent-Child Hierarchies
The windows.pstree plugin enumerates processes and arranges them into an indented tree hierarchy based on each process's Process ID (PID) and Inherited From Unique Process ID (PPID) :
Spotted Anomalies: Standard Windows processes exhibit immutable parent-child relationships. For example, services.exe must always be the parent of svchost.exe , and smss.exe must spawn wininit.exe and csrss.exe . If pstree reveals an instance of svchost.exe whose PPID traces to cmd.exe or powershell.exe , process masquerading or exploitation has occurred.
PPID Spoofing: Modern malware frequently leverages the UpdateProcThreadAttribute API with the PROC_THREAD_ATTRIBUTE_PARENT_PROCESS flag to spoof its parent process ID in user mode. While user-mode tools are deceived by this attribute, memory analysis cross-referencing process creation timestamps and handle tables reveals the true lineage.
3. windows.psscan : Uncovering Rootkits via Pool Tag Carving
Advanced rootkits utilize Direct Kernel Object Manipulation (DKOM) to unlink a malicious process from ActiveProcessLinks :
The rootkit driver modifies the Flink pointer of the preceding process to point directly to the succeeding process, and adjusts the Blink pointer accordingly.
The unlinked malicious process continues to execute because the Windows thread scheduler schedules individual Threads ( _KTHREAD ) , not processes.
The Countermeasure ( windows.psscan ): Rather than walking linked lists, windows.psscan performs pool carving . It scans physical memory pages for the unique 4-byte pool tag signature associated with process allocations ( Proc in older Windows builds or 0x636f7250 in modern systems). When it discovers an _EPROCESS block, it parses the structure regardless of whether it is linked into ActiveProcessLinks .
Command Primary Data Source Detects DKOM Unlinked Processes? Detects Terminated / Exited Processes?
windows.pslist ActiveProcessLinks doubly-linked list No (Bypassed by unlinking) No (Unlinked upon termination)
windows.pstree PID and PPID relationships in active list No (Bypassed by unlinking) No (Unlinked upon termination)
windows.psscan Physical memory pool tag carving ( Proc ) Yes (Identifies orphaned blocks) Yes (Remnants persist in deallocated RAM) # Enumerate active processes via doubly-linked list
python3 vol.py -f /evidence/mem.raw windows.pslist
- # Display parent-child process hierarchy
- python3 vol.py -f /evidence/mem.raw windows.pstree
- # Carve memory for unlinked and terminated processes
- python3 vol.py -f /evidence/mem.raw windows.psscan
- Injected Code & Hidden Module Detection: Malfind & Ldrmodules
Adversaries rarely leave malicious executables running as independent, easily recognizable processes. Instead, they inject malicious shellcode or DLLs into legitimate system processes (e.g., explorer.exe , svchost.exe , lsass.exe ) through techniques such as DLL Injection , Process Hollowing (RunPE) , and Reflective DLL Injection . Legitimate Process Virtual Address Space:
- +-------------------------------------------------------------+
- | 0x00400000 - Legitimate PE Executable Image (PAGE_READONLY) |
- +-------------------------------------------------------------+
- | 0x7FFF0000 - ntdll.dll / kernel32.dll (PAGE_EXECUTE_READ) |
- +-------------------------------------------------------------+
- | 0x02AB0000 - INJECTED CODE REGION |
- | • Memory Protection: PAGE_EXECUTE_READWRITE |
- | • Memory Type: MEM_PRIVATE (Not mapped to disk) |
- | • First Bytes: 4D 5A (MZ Header) or Shellcode |
- | ===> FLAGGED BY windows.malfind |
- +-------------------------------------------------------------+
- 1. windows.malfind : Identifying Injected Memory Ranges
The windows.malfind plugin is the premier tool for detecting process injection in memory. It scans the Virtual Address Descriptor (VAD) tree of every process, evaluating memory ranges against three specific criteria:
Memory Protection Mask: The memory page must be marked with executable and writable permissions—predominantly PAGE_EXECUTE_READWRITE ( 0x40 / RWX).
Memory Allocation Type: The memory allocation must be MEM_PRIVATE or MEM_COMMIT rather than MEM_IMAGE (memory mapped directly from a legitimate executable or DLL file on disk). Legitimate code is virtually always mapped from a file on disk.
Content Signature: The memory range contains executable code, characterized by an unmapped MZ ( 4D 5A ) header , a Portable Executable stub, or common shellcode assembly instructions (e.g., NOP sleds 0x90 , stack setup 0x55 0x8B 0xEC , or call instructions 0xE8 ).
When malfind flags a suspicious region, it outputs the process name, PID, memory start address, hex dump, and disassembled assembly instructions. # Scan memory for injected code and dump suspicious regions to disk
- python3 vol.py -f /evidence/mem.raw windows.malfind --dump
- 2. windows.ldrmodules : Detecting Unlinked / Reflective DLLs
In standard Windows execution, whenever a process loads a DLL, the OS loader updates three circular doubly-linked lists inside the Process Environment Block ( PEB_LDR_DATA ) :
InLoadOrderModuleList : Tracks DLLs in the sequence they were loaded into memory.
InMemoryOrderModuleList : Tracks DLLs ordered by their virtual memory base addresses.
InInitializationOrderModuleList : Tracks DLLs in the order their initialization routines ( DllMain ) executed.
Reflective DLL Injection bypasses the standard Windows API loader entirely. The malware allocates private memory, maps its own DLL sections, and manually resolves import addresses without calling LoadLibrary . Consequently, the injected DLL exists in the process's Virtual Address Descriptors (VAD) but is completely absent from all three PEB loader lists .
The windows.ldrmodules plugin cross-references the VAD tree with the three PEB loader lists: # Identify unlinked modules by cross-referencing VAD and PEB lists
python3 vol.py -f /evidence/mem.raw windows.ldrmodules
If InLoad , InInit , and InMem are False , but the module appears in the MappedPath or VAD with executable permissions, the DLL was injected reflectively or deliberately unlinked to evade detection.
- Network Sockets, Process Arguments & Handles
- 1. windows.netscan : Reconstructing Network Activity
Network sockets reside in volatile memory and provide direct forensic evidence of Command and Control (C2) communication, lateral movement, and data exfiltration.
The windows.netscan plugin carves physical memory for network structures ( _TCP_ENDPOINT , _TCP_LISTENER , _UDP_ENDPOINT ).
It extracts the protocol (TCP/UDP), local IP address and port, foreign/remote IP address and listening port, socket operational state ( ESTABLISHED , LISTENING , TIME_WAIT ), owning Process ID (PID), process owner name, and the timestamp when the connection was created.
Forensic Advantage: Because netscan carves pool tags, it can recover closed or terminated network connections whose memory structures have not yet been overwritten by kernel reallocations. # Scan memory for active and closed network connections
- python3 vol.py -f /evidence/mem.raw windows.netscan
- 2. windows.cmdline : Extracting Command-Line Execution Arguments
Malware often executes system utilities (Living-off-the-Land Binaries - LotLBs) with complex arguments. The windows.cmdline plugin traverses the _RTL_USER_PROCESS_PARAMETERS structure located within each process's _PEB to extract the full command-line string used during process invocation.
Reveals encoded PowerShell commands ( powershell.exe -nop -w hidden -enc JAB... ), discovery commands ( whoami /all , net group "Domain Admins" /domain ), and credential dumping parameters ( rundll32.exe comsvcs.dll, MiniDump ). # Extract process execution command-line parameters
- python3 vol.py -f /evidence/mem.raw windows.cmdline
- 3. windows.handles : Inspecting Open Kernel Handles
Processes interact with system resources through Handles managed by the kernel. The windows.handles plugin enumerates open handles for a given process:
Mutants (Mutexes): Malware frequently creates unique named mutexes (e.g., Global\\Injector_Mutex_v1 ) to prevent multiple instances from executing simultaneously. Discovering a known malicious mutex identifies malware families instantly.
Opened Files & Named Pipes: Identifies open handles to sensitive files, encrypted ransom staging archives, or named pipes utilized by C2 frameworks (e.g., named pipes like msagent_12 used by Cobalt Strike).
Registry Keys: Identifies registry persistence keys currently held open by malicious threads. # Enumerate Mutex (Mutant) handles across all processes
- python3 vol.py -f /evidence/mem.raw windows.handles --object-type Mutant
- 4. windows.vadinfo : Virtual Address Descriptor Analysis
The Windows kernel uses a balanced self-balancing binary search tree (the VAD Tree ) to track virtual memory address ranges allocated to each process.
The windows.vadinfo plugin traverses the VAD tree of a target process.
It details the start and end virtual addresses, allocation protection flags ( PAGE_NOACCESS , PAGE_READONLY , PAGE_EXECUTE_READWRITE ), commit status, private memory flags, and the mapped file path (if the allocation maps an image from disk).
Forensic Utility: Essential for deep reverse engineering of complex rootkits and confirming memory hollowings by inspecting memory protection permissions across the process's allocated address space.
- Practical Forensic Case Scenario: Investigating In-Memory Beaconing
- The Incident
A defense contractor's security operations center (SOC) detects sporadic, beacon-like HTTPS traffic originating from an internal engineering workstation to a foreign IP address. Disk-based antivirus scans return zero detections.
Volatility 3 Analysis Workflow
Process Lineage Examination ( windows.pstree ): The examiner runs windows.pstree and notes a suspicious instance of spoolsv.exe (Print Spooler) with PID 4128. However, instead of being a child of services.exe , PID 4128 was spawned by powershell.exe (PID 3104).
Network Connection Mapping ( windows.netscan ): Running windows.netscan reveals that PID 4128 ( spoolsv.exe ) maintains an active, established TCP connection to 198.51.100[.]88 over port 8443 . Legitimate print spoolers never establish external internet connections.
Injected Code Detection ( windows.malfind ): The examiner targets PID 4128 with windows.malfind . The plugin immediately flags a memory range at virtual address 0x0000018f2a100000 with PAGE_EXECUTE_READWRITE protection. The hex dump reveals an unmapped MZ header ( 4D 5A 90 00 ) followed by string references to ReflectiveLoader .
Handle Inspection ( windows.handles ): Inspecting mutant handles for PID 4128 reveals an active mutex named Global\\MSSE-8492-Enc . Threat intelligence databases correlate this exact mutex string to a customized Cobalt Strike Beacon payload.
Remediation: Armed with the remote C2 IP address and injected payload offsets, the incident response team blocks the C2 communication at the perimeter firewall and purges the persistence mechanisms established via PowerShell.