ModTracer - ModTracer Finds Hidden Linux Kernel Rootkits And Then Make Visible Again

ModTracer Finds Hidden Linux Kernel Rootkits and then make visible again.

Another way to make an LKM visible is using the imperius trick: https://github.com/MatheuZSecurity/Imperius

JA4+ - Suite Of Network Fingerprinting Standards

JA4+ is a suite of network Fingerprinting methods that are easy to use and easy to share. These methods are both human and machine readable to facilitate more effective threat-hunting and analysis. The use-cases for these fingerprints include scanning for threat actors, malware detection, session hijacking prevention, compliance automation, location tracking, DDoS detection, grouping of threat actors, reverse shell detection, and many more.

Please read our blogs for details on how JA4+ works, why it works, and examples of what can be detected/prevented with it:
JA4+ Network Fingerprinting (JA4/S/H/L/X/SSH)
JA4T: TCP Fingerprinting (JA4T/TS/TScan)

To understand how to read JA4+ fingerprints, see Technical Details

This repo includes JA4+ Python, Rust, Zeek and C, as a Wireshark plugin.

JA4/JA4+ support is being added to:
GreyNoise
Hunt
Driftnet
DarkSail
Arkime
GoLang (JA4X)
Suricata
Wireshark
Zeek
nzyme
Netresec's CapLoader
NetworkMiner">Netresec's NetworkMiner
NGINX
F5 BIG-IP
nfdump
ntop's ntopng
ntop's nDPI
Team Cymru
NetQuest
Censys
Exploit.org's Netryx
cloudflare.com/bots/concepts/ja3-ja4-fingerprint/">Cloudflare
fastly
with more to be announced...

Examples

For more, see ja4plus-mapping.csv
The mapping file is unlicensed and free to use. Feel free to do a pull request with any JA4+ data you find.

Plugins

Wireshark
Zeek
Arkime

Binaries

Recommended to have tshark version 4.0.6 or later for full functionality. See: https://pkgs.org/search/?q=tshark

Download the latest JA4 binaries from: Releases.

JA4+ on Ubuntu

sudo apt install tshark
./ja4 [options] [pcap]

JA4+ on Mac

1) Install Wireshark https://www.wireshark.org/download.html which will install tshark 2) Add tshark to $PATH

ln -s /Applications/Wireshark.app/Contents/MacOS/tshark /usr/local/bin/tshark
./ja4 [options] [pcap]

JA4+ on Windows

1) Install Wireshark for Windows from https://www.wireshark.org/download.html which will install tshark.exe
tshark.exe is at the location where wireshark is installed, for example: C:\Program Files\Wireshark\thsark.exe
2) Add the location of tshark to your "PATH" environment variable in Windows.
(System properties > Environment Variables... > Edit Path)
3) Open cmd, navigate the ja4 folder

ja4 [options] [pcap]

Database

An official JA4+ database of fingerprints, associated applications and recommended detection logic is in the process of being built.

In the meantime, see ja4plus-mapping.csv

Feel free to do a pull request with any JA4+ data you find.

JA4+ Details

JA4+ is a set of simple yet powerful network fingerprints for multiple protocols that are both human and machine readable, facilitating improved threat-hunting and security analysis. If you are unfamiliar with network fingerprinting, I encourage you to read my blogs releasing JA3 here, JARM here, and this excellent blog by Fastly on the State of TLS Fingerprinting which outlines the history of the aforementioned along with their problems. JA4+ brings dedicated support, keeping the methods up-to-date as the industry changes.

All JA4+ fingerprints have an a_b_c format, delimiting the different sections that make up the fingerprint. This allows for hunting and detection utilizing just ab or ac or c only. If one wanted to just do analysis on incoming cookies into their app, they would look at JA4H_c only. This new locality-preserving format facilitates deeper and richer analysis while remaining simple, easy to use, and allowing for extensibility.

For example; GreyNoise is an internet listener that identifies internet scanners and is implementing JA4+ into their product. They have an actor who scans the internet with a constantly changing single TLS cipher. This generates a massive amount of completely different JA3 fingerprints but with JA4, only the b part of the JA4 fingerprint changes, parts a and c remain the same. As such, GreyNoise can track the actor by looking at the JA4_ac fingerprint (joining a+c, dropping b).

Current methods and implementation details:
| Full Name | Short Name | Description | |---|---|---| | JA4 | JA4 | TLS Client Fingerprinting
| JA4Server | JA4S | TLS Server Response / Session Fingerprinting | JA4HTTP | JA4H | HTTP Client Fingerprinting | JA4Latency | JA4L | Latency Measurment / Light Distance | JA4X509 | JA4X | X509 TLS Certificate Fingerprinting | JA4SSH | JA4SSH | SSH Traffic Fingerprinting | JA4TCP | JA4T | TCP Client Fingerprinting | JA4TCPServer | JA4TS | TCP Server Response Fingerprinting | JA4TCPScan | JA4TScan | Active TCP Fingerprint Scanner

The full name or short name can be used interchangeably. Additional JA4+ methods are in the works...

To understand how to read JA4+ fingerprints, see Technical Details

Licensing

JA4: TLS Client Fingerprinting is open-source, BSD 3-Clause, same as JA3. FoxIO does not have patent claims and is not planning to pursue patent coverage for JA4 TLS Client Fingerprinting. This allows any company or tool currently utilizing JA3 to immediately upgrade to JA4 without delay.

JA4S, JA4L, JA4H, JA4X, JA4SSH, JA4T, JA4TScan and all future additions, (collectively referred to as JA4+) are licensed under the FoxIO License 1.1. This license is permissive for most use cases, including for academic and internal business purposes, but is not permissive for monetization. If, for example, a company would like to use JA4+ internally to help secure their own company, that is permitted. If, for example, a vendor would like to sell JA4+ fingerprinting as part of their product offering, they would need to request an OEM license from us.

All JA4+ methods are patent pending.
JA4+ is a trademark of FoxIO

JA4+ can and is being implemented into open source tools, see the License FAQ for details.

This licensing allows us to provide JA4+ to the world in a way that is open and immediately usable, but also provides us with a way to fund continued support, research into new methods, and the development of the upcoming JA4 Database. We want everyone to have the ability to utilize JA4+ and are happy to work with vendors and open source projects to help make that happen.

ja4plus-mapping.csv is not included in the above software licenses and is thereby a license-free file.

Q&A

Q: Why are you sorting the ciphers? Doesn't the ordering matter?
A: It does but in our research we've found that applications and libraries choose a unique cipher list more than unique ordering. This also reduces the effectiveness of "cipher stunting," a tactic of randomizing cipher ordering to prevent JA3 detection.

Q: Why are you sorting the extensions?
A: Earlier in 2023, Google updated Chromium browsers to randomize their extension ordering. Much like cipher stunting, this was a tactic to prevent JA3 detection and "make the TLS ecosystem more robust to changes." Google was worried server implementers would assume the Chrome fingerprint would never change and end up building logic around it, which would cause issues whenever Google went to update Chrome.

So I want to make this clear: JA4 fingerprints will change as application TLS libraries are updated, about once a year. Do not assume fingerprints will remain constant in an environment where applications are updated. In any case, sorting the extensions gets around this and adding in Signature Algorithms preserves uniqueness.

Q: Doesn't TLS 1.3 make fingerprinting TLS clients harder?
A: No, it makes it easier! Since TLS 1.3, clients have had a much larger set of extensions and even though TLS1.3 only supports a few ciphers, browsers and applications still support many more.

JA4+ was created by:

John Althouse, with feedback from:

Josh Atkins
Jeff Atkinson
Joshua Alexander
W.
Joe Martin
Ben Higgins
Andrew Morris
Chris Ueland
Ben Schofield
Matthias Vallentin
Valeriy Vorotyntsev
Timothy Noel
Gary Lipsky
And engineers working at GreyNoise, Hunt, Google, ExtraHop, F5, Driftnet and others.

Contact John Althouse at john@foxio.io for licensing and questions.

_{^{Copyright (c) 2024, FoxIO}}

VolWeb - A Centralized And Enhanced Memory Analysis Platform

VolWeb is a digital forensic memory analysis platform that leverages the power of the Volatility 3 framework. It is dedicated to aiding in investigations and incident responses.

Objective

The goal of VolWeb is to enhance the efficiency of memory collection and forensic analysis by providing a centralized, visual, and enhanced web application for incident responders and digital forensics investigators. Once an investigator obtains a memory image from a Linux or Windows system, the evidence can be uploaded to VolWeb, which triggers automatic processing and extraction of artifacts using the power of the Volatility 3 framework.

By utilizing cloud-native storage technologies, VolWeb also enables incident responders to directly upload memory images into the VolWeb platform from various locations using dedicated scripts interfaced with the platform and maintained by the community. Another goal is to allow users to compile technical information, such as Indicators, which can later be imported into modern CTI platforms like OpenCTI, thereby connecting your incident response and CTI teams after your investigation.

Project Documentation and Getting Started Guide

The project documentation is available on the Wiki. There, you will be able to deploy the tool in your investigation environment or lab.

[!IMPORTANT] Take time to read the documentation in order to avoid common miss-configuration issues.

Interacting with the REST API

VolWeb exposes a REST API to allow analysts to interact with the platform. There is a dedicated repository proposing some scripts maintained by the community: https://github.com/forensicxlab/VolWeb-Scripts Check the wiki of the project to learn more about the possible API calls.

Issues

If you have encountered a bug, or wish to propose a feature, please feel free to open an issue. To enable us to quickly address them, follow the guide in the "Contributing" section of the Wiki associated with the project.

Contact

Contact me at k1nd0ne@mail.com for any questions regarding this tool.

Next Release Goals

Check out the roadmap: https://github.com/k1nd0ne/VolWeb/projects/1

MrHandler - Linux Incident Response Reporting

MR.Handler is a specialized tool designed for responding to security incidents on Linux systems. It connects to target systems via SSH to execute a range of diagnostic commands, gathering crucial information such as network configurations, system logs, user accounts, and running processes. At the end of its operation, the tool compiles all the gathered data into a comprehensive HTML report. This report details both the specifics of the incident response process and the current state of the system, enabling security analysts to more effectively assess and respond to incidents.

  $ pip3 install colorama
  $ pip3 install paramiko
  $ git clone https://github.com/emrekybs/BlueFish.git
  $ cd MrHandler
  $ chmod +x MrHandler.py 
  $ python3 MrHandler.py

PhantomCrawler - Boost Website Hits By Generating Requests From Multiple Proxy IPs

PhantomCrawler allows users to simulate website interactions through different proxy IP addresses. It leverages Python, requests, and BeautifulSoup to offer a simple and effective way to test website behaviour under varied proxy configurations.

Features:

• Utilizes a list of proxy IP addresses from a specified file.
• Supports both HTTP and HTTPS proxies.
• Allows users to input the target website URL, proxy file path, and a static port.
• Makes HTTP requests to the specified website using each proxy.
• Parses HTML content to extract and visit links on the webpage.

Usage:

• POC Testing: Simulate website interactions to assess functionality under different proxy setups.
• Web Traffic Increase: Boost website hits by generating requests from multiple proxy IPs.
• Proxy Rotation Testing: Evaluate the effectiveness of rotating proxy IPs.
• Web Scraping Testing: Assess web scraping tasks under different proxy configurations.
• DDoS Awareness: Caution: The tool has the potential for misuse as a DDoS tool. Ensure responsible and ethical use.

• You can add it from here: https://free-proxy-list.net/ these free proxies are not validated some might not work so first validate these proxies before adding.

How to Use:

1. Clone the repository:

git clone https://github.com/spyboy-productions/PhantomCrawler.git

3. Install dependencies:

pip3 install -r requirements.txt

5. Run the script:

python3 PhantomCrawler.py

Disclaimer: PhantomCrawler is intended for educational and testing purposes only. Users are cautioned against any misuse, including potential DDoS activities. Always ensure compliance with the terms of service of websites being tested and adhere to ethical standards.

Snapshots:

If you find this GitHub repo useful, please consider giving it a star! 

Douglas-042 - Powershell Script To Help Speed ​​Up Threat Hunting Incident Response Processes

DOUGLAS-042 stands as an ingenious embodiment of a PowerShell script meticulously designed to expedite the triage process and facilitate the meticulous collection of crucial evidence derived from both forensic artifacts and the ephemeral landscape of volatile data. Its fundamental mission revolves around providing indispensable aid in the arduous task of pinpointing potential security breaches within Windows ecosystems. With an overarching focus on expediency, DOUGLAS-042 orchestrates the efficient prioritization and methodical aggregation of data, ensuring that no vital piece of information eludes scrutiny when investigating a possible compromise. As a testament to its organized approach, the amalgamated data finds its sanctuary within the confines of a meticulously named text file, bearing the nomenclature of the host system's very own hostname. This practice of meticulous data archival emerges not just as a systematic convention, but as a cornerstone that paves the way for seamless transitions into subsequent stages of the Forensic journey.

Content Queries

• General information
• Accountand group information
• Network
• Process Information
• OS Build and HOTFIXE
• Persistence
• HARDWARE Information
• Encryption information
• FIREWALL INFORMATION
• Services
• History
• SMB Queries
• Remoting queries
• REGISTRY Analysis
• LOG queries
• Instllation of Software
• User activity

Advanced Queries

• Prefetch file information
• DLL List
• WMI filters and consumers
• Named pipes

Usage

Using administrative privileges, just run the script from a PowerShell console, then the results will be saved in the directory as a txt file.

$ PS >./douglas.ps1

Advance usage

$ PS >./douglas.ps1 -a

ICS-Forensics-Tools - Microsoft ICS Forensics Framework

Microsoft ICS Forensics Tools is an open source forensic framework for analyzing Industrial PLC metadata and project files.
it enables investigators to identify suspicious artifacts on ICS environment for detection of compromised devices during incident response or manual check.
open source framework, which allows investigators to verify the actions of the tool or customize it to specific needs.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

git clone https://github.com/microsoft/ics-forensics-tools.git

Prerequisites

• Install Python >= 3.9: https://www.python.org/downloads

Installing

• Install python requirements

  pip install -r requirements.txt
  

Usage

General application arguments:

Specific plugin arguments:

Executing examples in the command line

 python driver.py -s -v PluginName --ip ips.csv
 python driver.py -s -v PluginName --analyzer AnalyzerName
 python driver.py -s -v -c config.json --multiprocess

Import as library example

from forensic.client.forensic_client import ForensicClient
from forensic.interfaces.plugin import PluginConfig
forensic = ForensicClient()
plugin = PluginConfig.from_json({
    "name": "PluginName",
    "port": 123,
    "transport": "tcp",
    "addresses": [{"ip": "192.168.1.0/24"}, {"ip": "10.10.10.10"}],
    "parameters": {
    },
    "analyzers": []
})
forensic.scan([plugin])

Architecture

Adding Plugins

When developing locally make sure to mark src folder as "Sources root"

• Create new directory under plugins folder with your plugin name
• Create new Python file with your plugin name
• Use the following template to write your plugin and replace 'General' with your plugin name

from pathlib import Path
from forensic.interfaces.plugin import PluginInterface, PluginConfig, PluginCLI
from forensic.common.constants.constants import Transport


class GeneralCLI(PluginCLI):
    def __init__(self, folder_name):
        super().__init__(folder_name)
        self.name = "General"
        self.description = "General Plugin Description"
        self.port = 123
        self.transport = Transport.TCP

    def flags(self, parser):
        self.base_flags(parser, self.port, self.transport)
        parser.add_argument('--general', help='General additional argument', metavar="")


class General(PluginInterface):
    def __init__(self, config: PluginConfig, output_dir: Path, verbose: bool):
        super().__init__(config, output_dir, verbose)

    def connect(self, address):
        self.logger.info(f"{self.config.name} connect")

    def export(self,    extracted):
        self.logger.info(f"{self.config.name} export")

• Make sure to import your new plugin in the __init__.py file under the plugins folder
• In the PluginInterface inherited class there is 'config' parameters, you can use this to access any data that's available in the PluginConfig object (plugin name, addresses, port, transport, parameters).
  there are 2 mandatory functions (connect, export).
  the connect function receives single ip address and extracts any relevant information from the device and return it.
  the export function receives the information that was extracted from all the devices and there you can export it to file.
• In the PluginCLI inherited class you need to specify in the init function the default information related to this plugin.
  there is a single mandatory function (flags).
  In which you must call base_flags, and you can add any additional flags that you want to have.

Adding Analyzers

• Create new directory under analyzers folder with the plugin name that related to your analyzer.
• Create new Python file with your analyzer name
• Use the following template to write your plugin and replace 'General' with your plugin name

from pathlib import Path
from forensic.interfaces.analyzer import AnalyzerInterface, AnalyzerConfig


class General(AnalyzerInterface):
    def __init__(self, config: AnalyzerConfig, output_dir: Path, verbose: bool):
        super().__init__(config, output_dir, verbose)
        self.plugin_name = 'General'
        self.create_output_dir(self.plugin_name)

    def analyze(self):
      pass

• Make sure to import your new analyzer in the __init__.py file under the analyzers folder

Resources and Technical data & solution:

Microsoft Defender for IoT is an agentless network-layer security solution that allows organizations to continuously monitor and discover assets, detect threats, and manage vulnerabilities in their IoT/OT and Industrial Control Systems (ICS) devices, on-premises and in Azure-connected environments.

Section 52 under MSRC blog
ICS Lecture given about the tool
Section 52 - Investigating Malicious Ladder Logic | Microsoft Defender for IoT Webinar - YouTube

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

MemTracer - Memory Scaner

MemTracer is a tool that offers live memory analysis capabilities, allowing digital forensic practitioners to discover and investigate stealthy attack traces hidden in memory. The MemTracer is implemented in Python language, aiming to detect reflectively loaded native .NET framework Dynamic-Link Library (DLL). This is achieved by looking for the following abnormal memory region’s characteristics:

• The state of memory pages flags in each memory region. Specifically, the MEM_COMMIT flag which is used to reserve memory pages for virtual memory use.
• The type of pages in the region. The MEM_MAPPED page type indicates that the memory pages within the region are mapped into the view of a section.
• The memory protection for the region. The PAGE_READWRITE protection to indicate that the memory region is readable and writable, which happens if Assembly.Load(byte[]) method is used to load a module into memory.
• The memory region contains a PE header.

The tool starts by scanning the running processes, and by analyzing the allocated memory regions characteristics to detect reflective DLL loading symptoms. Suspicious memory regions which are identified as DLL modules are dumped for further analysis and investigation.
Furthermore, the tool features the following options:

• Dump the compromised process.
• Export a JSON file that provides information about the compromised process, such as the process name, ID, path, size, and base address.
• Search for specific loaded module by name.

Example

python.exe memScanner.py [-h] [-r] [-m MODULE]
-h, --help show this help message and exit
-r, --reflectiveScan Looking for reflective DLL loading
-m MODULE, --module MODULE Looking for spcefic loaded DLL

The script needs administrator privileges in order incepect all processes.

Forensia - Anti Forensics Tool For Red Teamers, Used For Erasing Footprints In The Post Exploitation Phase

Anti Forensics Tool For Red Teamers, Used For Erasing Some Footprints In The Post Exploitation Phase.

Reduces Payload Burnout And Increases Detection Countdown. Can Be Used To Test The capabilities of Your Incident Response / Forensics Teams.

Capabilities

• Unloading Sysmon Driver.
• Gutmann Method File Shredding.
• USNJrnl Disabler.
• Prefetch Disabler.
• Log Eraser and Event log Disabler.
• User Assist Update Time Disabler.
• Access Time Disabler.
• Clear Recent Items
• Clear Shim Cache
• Clear RecentFileCache
• Clear ShellBag
• Delete Windows Defender Quarantine Files
• File Melting Capabilities.

Important Update

Added:

• Clear Recent Items
• Clear Shim Cache
• Clear RecentFileCache
• Clear ShellBag
• Clear Quanatine Files

TODO

• USNJRnl Execution On All Disk Drives.

• Unallocated Space ReWriting.

• A Bit of Polishing.

Credits

https://github.com/Naranbataar/Corrupt

https://github.com/LloydLabs/delete-self-poc

https://github.com/OsandaMalith/WindowsInternals/blob/master/Unload_Minifilter.c

https://stackoverflow.com/users/15168/jonathan-leffler

https://github.com/GiovanniDicanio/WinReg