Category Archives: Michael Bailey

I (Matthew Haigh) recently contributed to FLARE’s FakeNet-NG
network simulator by adding content-based protocol detection and
configuration. This feature is useful for analyzing malware that uses
a protocol over a non-standard port; for example, HTTP over port 81.
The new feature also detects and adapts to SSL so that any protocol
can be used with SSL and handled appropriately by FakeNet-NG. We were
motivated to add this feature since it was a feature of the original
FakeNet and it was needed for real world malware.

What is FakeNet-NG

FakeNet-NG simulates
a network so malware analysts can run samples with network
functionality without the risks of an Internet connection. Analysts
can examine network-based indicators via FakeNet-NG’s textual and pcap
output. It is plug-and-play, configurable, and works on both Windows
and Linux. FakeNet-NG simulates common protocols to trick malware into
thinking it is connected to the Internet. FakeNet-NG supports the
following protocols: DNS, HTTP, FTP, POP, SMTP, IRC, SSL, and TFTP.

Previous Design

Previously FakeNet-NG employed Listener modules, which were bound to
configurable ports for each protocol. Any traffic on those ports was
received by the socket and processed by the Listener. 

In the previous architecture, packets were redirected using a
Diverter module that utilized WinDivert for Windows and netfilter for
Linux. Each incoming and outgoing packet was examined by the Diverter,
which kept a running list of connections. Packets destined for
outbound ports were redirected to a default Listener, which would
respond to any packet with an echo of the same data. The Diverter also
redirected packets based on whether FakeNet-NG was run in Single-Host
or Multi-Host mode, and if any applications were blacklisted or
whitelisted according to the configuration. It would simply release
the packet on the appropriate port and the intended Listener would
receive it on the socket.

New Design

My challenge was to eliminate this port/protocol dependency. In
order to disassociate the Listeners from the corresponding ports, a
new architecture was needed. The first challenge was to maintain
Listener functionality. The original architecture relied on Python
libraries that interact with the socket. Therefore, we needed to
maintain “socket autonomy” in the Listener, so we added a “taste()”
function for each Listener. The routine returns a confidence score
based on the likelihood that the packet is associated with the
protocol. Figure 1 demonstrates the taste() routine for HTTP, which
looks for the request method string at the beginning of the packet
data. It gives an additional point if the packet is on a common HTTP
port. There were several choices for how these scores were to be
tabulated. It could not happen in the Diverter because of the TCP
handshake. The Diverter could not sample data from data-less handshake
packets, and if the Diverter completed the handshake, the connection
could not easily be passed to a different socket at the Listener
without disrupting the connection.

Figure 1: HTTP taste() example

Proxy

We ultimately decided to add a proxy Listener that maintains
full-duplex connections with the client and the Listener, with both
sides unaware of the other. This solves the handshake problem and
maintains socket autonomy at the Listener. The proxy is also easily
configurable and enables new functionality. We substituted the proxy
for the echo-server default Listener, which would receive traffic
destined for unbound ports. The proxy peeks at the data on the socket,
polls the Listeners, and creates a new connection with the Listener
that returns the highest score. The echo-server always returns a score
of one, so it will be chosen if no better option is detected. The
analyst controls which Listeners are bound to ports and which
Listeners are polled by the proxy. This means that the listeners do
not have to be exposed at all; everything can be decided by the proxy.
The user can set the Hidden option in the configuration file to False
to ensure the Listener will be bound to the port indicated in the
configuration file. Setting Hidden to True will force any packets to
go through the proxy before accessing the Listener. For example, if
the analyst suspects that malware is using FTP on port 80, she can
‘hide’ HTTP from catching the traffic, and let the proxy detect FTP
and forward the packet to the FTP Listener. Additional configuration
options exist for choosing which protocols are polled by the proxy.
See Figure 2 and Figure 3 for configuration examples. Figure 2 is a
basic configuration for a Listener, and Figure 3 demonstrates how the
proxy is configurable for TCP and UDP.

Figure 2: Listener Configuration Options

Figure3: Proxy Configuration Options

The proxy also handles SSL detection. Before polling the Listeners,
the proxy examines the packet. If SSL is detected, the proxy “wraps”
the socket in SSL using Python’s OpenSSL library. With the combination
of protocol and SSL detection, each independent of the other,
FakeNet-NG can now handle just about any protocol combination.

The proxied SSL implementation also allows for improved packet
analysis. The connection between the proxy and the Listener is not
encrypted, which allows FakeNet to dump un-encrypted packets to the
pcap output. This makes it easier for the analyst to examine the
packet data. FakeNet continues to produce pcap output that includes
packet data before and after modification by FakeNet. While this
results in repetitive data, it is often useful to see the original
packet along with the modification.

Example

Figure 4 shows verbose (-v) output from FakeNet on Windows
responding to an HTTP request on port 81 from a clowncar malware
variant (SHA-256
8d2dfd609bcbc94ff28116a80cf680660188ae162fc46821e65c10382a0b44dc).
Malware such as clowncar use traditional protocols over non-standard
ports for many reasons. FakeNet gives the malware analyst the
flexibility to detect and respond to these cases automatically.

Figure 4: clowncar malware using HTTP on
port 81

Conclusion

FLARE’s FakeNet-NG tool is a powerful network-simulation tool
available for Windows and Linux. The new content-based protocol
detection and SSL detection features ensure that FakeNet-NG remains
the most useful tool for malware analysts. Configuration options give
programmers the flexibility necessary to respond to malware using most
protocols on any port.

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