Malcom - Malware Communication Analyzer

Malcom is a tool designed to analyze a system's network communication using graphical representations of network traffic, and cross-reference them with known malware sources. This comes handy when analyzing how certain malware species try to communicate with the outside world.

Malcom can help you:

detect central command and control (C&C) servers
understand peer-to-peer networks
observe DNS fast-flux infrastructures
quickly determine if a network artifact is 'known-bad'

The aim of Malcom is to make malware analysis and intel gathering faster by providing a human-readable version of network traffic originating from a given host or network. Convert network traffic information to actionable intelligence faster.

Check the wiki for a Quickstart with some nice screenshots and a tutorial on how to add your own feeds.

If you need some help, or want to contribute, feel free to join the mailing list or try to grab someone on IRC (#malcom on freenode.net, it's pretty quiet but there's always someone around). You can also hit me up on twitter @tomchop_

Here's an example graph for host tomchop.me

Dataset view (filtered to only show IPs)

Quick how-to

Install
Make sure mongodb and redis-server are running
Elevate your privileges to root (yeah, I know, see disclaimer)
Start the webserver using the default configuration with ./malcom.py -c malcom.conf (or see options with ./malcom.py --help) ** For an example configuration file, you can copy malcom.conf.example to malcom.conf ** Default port is 8080 ** Alternatively, run the feeds from celery. See the feeds section for details on how to to this.

Installation

Malcom is written in python. Provided you have the necessary libraries, you should be able to run it on any platform.

The following was tested on Ubuntu server 14.04 LTS:

Install git, python and libevent libs, mongodb, redis, and other dependencies

  apt-get install git python-dev libevent-dev mongodb libxml2-dev libxslt-dev zlib1g-dev redis-server libffi-dev libssl-dev libadns1-dev

Get virtualenv and scapy

  wget https://pypi.python.org/packages/source/v/virtualenv/virtualenv-1.11.5.tar.gz
  wget http://www.secdev.org/projects/scapy/files/scapy-latest.tar.gz
  tar xvzf virtualenv-1.11.5.tar.gz
  tar xvzf scapy-latest.tar.gz

Clone the Git repo

  git clone https://github.com/tomchop/malcom.git malcom

Create your virtualenv and activate it

  cd malcom
  python ../virtualenv-1.11.5/virtualenv.py env-malcom
  source env-malcom/bin/activate

Install scapy, without elevating your privs to root
```
  cd ../scapy-2.1.0
  python setup.py install
```

still from your virtualenv, install necessary python packages

  pip install flask pymongo pygeoip geoip2 gevent-websocket python-dateutil netifaces lxml twisted pyopenssl redis service_identity flask-login pycrypto passlib dnspython

Launch the webserver from the malcom directory using ./malcom.py. Check ./malcom.py --help for listen interface and ports.

Quick note on TLS interception

Malcom now supports TLS interception. For this to work, you need to generate some keys in Malcom/networking/tlsproxy/keys. See the KEYS.md file there for more information on how to do this.

Make sure you also have IPtables (you already should) and permissions to do some port forwarding with it (you usually need to be root for that). You can to this using the convenient forward_port.sh script. For example, to intercept all TLS communications towards port 443, use forward_port.sh 443 9999. You'll then have to tell malcom to run an interception proxy on port 9999.

Expect this process to be automated in future releases.

Environment

Malcom was designed and tested on a Ubuntu Server 14.04 LTS VM.

If you're used to doing malware analysis, you probably already have tons of virtual machines running on a host OS. Just install Malcom on a new VM, and route your other VM's connections through Malcom. Use enable_routing.sh to activate routing / NATing on the VM Malcom is running on. You'll need to add an extra network card to the guest OS.

As long as it's getting layer-3 network data, Malcom can be deployed anywhere. Although it's not recommended to use it on high-availability networks (it wasn't designed to be fast, see disclaimer), you can have it running at the end of your switch's mirror port or on your gateway.

Feeds

To launch an instance of Malcom that ONLY fetches information from feeds, run Malcom with the --feeds option.

Your database should be populated automatically. If you can dig into the code, adding feeds is pretty straightforward (assuming you're generating Evil objects). You can find an example feed in /feeds/zeustracker. A more detailed tutorial is available here.

You can also use celery to run feeds. Make sure celery is installed by running $ pip install celery from your virtualenv. You can then use celery worker -E --config=celeryconfig --loglevel=DEBUG --concurrency=12 to launch the feeding process with 12 simultaneous workers.

Technical specs

Malcom was written mostly from scratch, in Python. It uses the following frameworks to work:

flask - a lightweight python web framework
mongodb - a NoSQL database. It interfaces to python with pymongo
redis - An advanced in-memory key-value store
d3js - a JavaScript library that produces awesome force-directed graphs (https://github.com/mbostock/d3/wiki/Gallery)
bootstrap - a CSS framework that will eventually kill webdesign, but makes it extremely easy to quickly "webize" applications that would only work through a command prompt.

Roadmap

Collaboration - The main direction I want this tool to take is to become collaborative. I have a few ideas for this, and I think it will become 100x more useful once data sharing is implemented.

Extendability - The other thing I want to include in the tool is the ability to more easily extend it. I don't have the same needs as everyone else, and this tool was conceived having my needs in mind. You can now customize Malcom by adding new feeds.

Once collaboration and extension are up and running, I think this will be helpful for more than one incident responder out there. :-)

Disclaimer

This tool was coded during my free time. Like a huge number of tools we download and use daily, I wouldn't recommend to use it on a production environment where data stability and reliability is a MUST.

It may be broken, have security gaps (running it as root in uncontrolled environments is probably not a good idea), or not work at all.
It's written in python, so don't expect it to be ultra-fast or handle huge amounts of data easily.
I'm no coder, so don't expect to see beautiful pythonic code everywhere you look. Or lots of comments.

It's in early stages of development, meaning "it works for me". You're free to share it, improve it, ask for pull requests.

License

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.

Please note that Redis, MongoDB, d3js, Maximind and Bootstrap (and other third party libraries included in Malcom) may have their own GPL compatible licences.

loveshell/malcom