As a reverse engineer on the FLARE (FireEye Labs Advanced Reverse
Engineering) team, I regularly perform basic dynamic analysis of
malware samples. The goal is to quickly observe runtime
characteristics by running binaries in a safe environment. One
important task during dynamic analysis is to emulate the network
environment and trick the malware into thinking it is connected to the
Internet. When done right, the malware reveals its network signatures
such as command and control (C2) domain names, User-Agent strings,
URLs queried, and so on.
One tool of choice is FakeNet. In this blog, I will discuss a major
overhaul to FakeNet and how it helps you perform basic malware dynamic
analysis. Some of the new features include full support for Windows
Vista and later operating systems, process logging, advanced process
and host traffic filtering engine, support for third party tools (e.g.
debuggers, HTTP proxies, etc.) and many others.
This blog covers the basic installation and most common scenarios
for running FakeNet-NG. I invite you to review the complete
documentation available here.
Getting and Installing FakeNet-NG
The tool can be found on FLARE’s official Github repository here.
From the releases page, download the latest pre-compiled archive.
Next, copy the release archive to the Malware Analysis VM and extract
it in an easily accessible location.
The simplest way to run FakeNet-NG is to double click on
fakenet64.exe or fakenet32.exe for the 64-bit or 32-bit versions of
Windows, respectively, as illustrated in Figure 1.
Figure 1: Running FakeNet-NG
The tool requires Administrator access, so you will have to confirm
the UAC prompt requesting elevated privileges. Once launched you will
see a console window similar to the one in Figure 2.
Figure 2: FakeNet-NG Startup
By default, FakeNet-NG is configured to start several most commonly
- DNS Listener on UDP port 53
- HTTP Listener on TCP port
- HTTPS Listener on TCP port 443
- SMTP Listener on
TCP port 25
- Raw Binary Listener on both TCP and UDP ports
1337. This service is also used as a default listener to handle all
communications. Default listeners are explained below.
At this point you are ready to run a malware sample and observe its
behavior. Figure 3 illustrates sample malware communication to the C2 server.
Figure 3: Sample malware communication
There are quite a few things going on in the log output above so
let’s break it down into smaller components.
Once launched, the malware attempts to resolve a C2 domain
evil.mandiant.com by querying the configured DNS server 22.214.171.124.
Figure 4 illustrates how FakeNet-NG diverts the traffic from 126.96.36.199
to the local machine’s IP address 172.16.163.131.
Figure 4: Diverting DNS traffic
A major benefit of running FakeNet-NG on the same host as the
malware is that it can perform additional analysis of running
executables. For example, FakeNet-NG is capable of detecting the exact
executable name that is generating traffic. In this case, we can see
that level1_payload.exe is generating the above DNS traffic.
Continuing with the analysis, Figure 5 shows FakeNet-NG’s DNS
listener providing a fake response to the query pointing malware to a
fake C2 IP address 192.0.2.123.
Figure 5: Faking DNS response
After successfully resolving the domain, the malware proceeds to
communicate with the C2 domain name, as shown in Figure 6.
Figure 6: Faking C2 communication
FakeNet-NG implements a few popular network listeners. In this case,
the malware is communicating using the HTTP protocol on port 80. The
output above provides us with several good network indicators such as
the exact URL requested and User-Agent used in the communication, as
well as the unencrypted beacon payload containing the compromised
host’s machine name. All of these indicators can be used to create
good network signatures to detect this malware sample.
By default, FakeNet-NG captures all of the intercepted traffic in
PCAP files so you can perform additional analysis. For example, Figure
7 shows both original and diverted packets performing DNS resolution
as well as HTTP POST requests to the C2 server.
Figure 7: Wireshark PCAP
Captured PCAP files are stored in the same directory as the
FakeNet-NG’s executable. As an added logging feature, FakeNet-NG will
also preserve complete HTTP POST payloads in separate text files also
stored in the executable’s working path.
By default, FakeNet-NG is configured to cover the majority of
malware analysis scenarios. However, if you encounter a more complex
sample, then you can easily adapt the tool by editing one of the few
configuration files located in the configs directory. By default,
FakeNet-NG loads default.ini configuration file when it loads. You can
either modify that file or create a new one and point FakeNet-NG to
load it with the –c command-line parameter. Consider a sample
scenario, where you have malware communicating using a binary protocol
on port 4444. Figure 8 illustrates a sample listener configuration
that will fake this service.
Figure 8: Custom Listener Configuration
The key elements of the configuration above are Port,
Protocol and Listener. Port and Protocol
attributes define the port and protocol used to both setup the
listener service and define the rule to divert traffic. The
Listener attribute is used to define a specific listener class.
In this case, RawListener is used to handle arbitrary binary
protocols. Alternatively, if you wanted to setup a listener to handle
HTTP or HTTPS traffic you would use HTTPListener instead.
Please refer to the documentation for a complete list of supported
listeners and available options.
With the above configuration appended to the active configuration
file, we can now launch FakeNet-NG and intercept traffic destined to
TCP port 4444, as shown in Figure 9.
Figure 9: Diverting to Custom Listener
The scenario above was for a single, known port that the malware
would use for its communication. In many cases it is hard to predict
the exact port used from basic static or dynamic analysis. Instead,
let’s use another powerful feature that essentially allows you to
handle any traffic to any port by a default listener. In order to
configure the default listener, edit the [Diverter] section in the
configuration file as follows in Figure 10.
Default Listener Configuration
Now, if the same malware sample decided to communicate on another
port (e.g. 5555), it would still be intercepted and handled by the
previously defined CustomListener4444.
Figure 11: Diverting to Default Listener
The Figure 11 illustrates traffic going to the unknown port 5555
being diverted to the previously defined custom listener on port 4444.
It is important to note that any explicitly defined listeners will
take precedence over the default listener. So if you have DNS or HTTP
listeners defined on UDP port 53 and TCP port 80 respectively, then
they would handle diverted traffic instead of the default listener as expected.
New Code Base
The new FakeNet-NG is developed completely in Python so it is easy
to implement new services and features. It no longer uses the
deprecated LSP (WinSock Layered Service Provider) driver implemented
in the original FakeNet. Instead, FakeNet-NG relies on the excellent
PyDivert\WinDivert library, which comes with a WFP (Windows Filtering
Platform) driver that performs all of the traffic redirection.
This blog shares a few techniques that can be used to quickly
perform basic dynamic malware analysis and extract good network-based
indicators. FakeNet-NG is a powerful and highly configurable tool that
can be used to perform more advanced tasks such as process and traffic
filtering, aiding in automatic malware unpacking, security assessment
of thick-client applications and many others. Stay tuned for future
blog posts that demonstrate the full features of this tool.
Try out FakeNet-NG the next time you need to perform malware
analysis, security assessment or simply to divert network traffic and
fake network responses. We hope you love this tool as much as we do on
the FLARE team.
*** This is a Security Bloggers Network syndicated blog from Threat Research Blog authored by Threat Research Blog. Read the original post at: http://www.fireeye.com/blog/threat-research/2016/08/fakenet-ng_next_gen.html