October 25, 2021


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SCANdalous! (External Detection Using Network Scan Data and Automation)

SCANdalous! (External Detection Using Network Scan Data and Automation)

Real Quick

In case you’re thrown by that fantastic title, our lawyers made us
change the name of this project so we wouldn’t get sued.
SCANdalous—a.k.a. Scannah Montana a.k.a. Scanny McScanface a.k.a.
“Scan I Kick It? (Yes You Scan)”—had another name before today that,
for legal reasons, we’re keeping to ourselves. A special thanks to our
legal team who is always looking out for us, this blog post would be a
lot less fun without them. Strap in folks.


Advanced Practices is known for using primary source data obtained
through Mandiant
Incident Response
, Managed
, and product telemetry across thousands of FireEye
clients. Regular, first-hand observations of threat actors afford us
opportunities to learn intimate details of their modus operandi. While
our visibility from organic data is vast, we also derive value from
third-party data sources. By looking outwards, we extend our
visibility beyond our clients’ environments and shorten the time it
takes to detect adversaries in the wild—often before they initiate
intrusions against our clients.

In October 2019, Aaron Stephens gave his “Scan’t Touch
talk at the annual FireEye Cyber Defense Summit (slides
available on
his Github
). He discussed using network scan data for external
detection and provided examples of how to profile command and control
(C2) servers for various post-exploitation frameworks used by criminal
and intelligence organizations alike. However, manual application of
those techniques doesn’t scale. It may work if your role focuses on
one or two groups, but Advanced Practices’ scope is much broader. We
needed a solution that would enable us to track thousands of groups,
malware families and profiles. In this blog post we’d like to talk
about that journey, highlight some wins, and for the first time
publicly, introduce the project behind it all: SCANdalous.

Pre-SCANdalous Case Studies

Prior to any sort of system or automation, our team used traditional
profiling methodologies to manually identify servers of interest. The
following are some examples. The success we found in these case
studies served as the primary motivation for SCANdalous.

APT39 SSH Tunneling

After observing APT39 in a series of intrusions, we determined they
frequently created Secure
Shell (SSH) tunnels with PuTTY Link to forward Remote Desktop
connections to internal hosts within the target
environment. Additionally, they preferred using BitVise SSH servers
listening on port 443. Finally, they were using servers hosted by
WorldStream B.V.

Independent isolation of any one of these characteristics would
produce a lot of unrelated servers; however, the aggregation of
characteristics provided a strong signal for newly established
infrastructure of interest. We used this established profile and
others to illuminate dozens of servers we later attributed to APT39,
often before they were used against a target.


In February 2018, an independent researcher shared a sample of what
would later be named QUADAGENT. We had not observed it in an intrusion
yet; however, by analyzing the characteristics of the C2, we were able
to develop a strong profile of the servers to track over time. For
example, our team identified the server and domain rdppath.com within hours of it being established.
A week later, we identified a QUADAGENT dropper with the previously
identified C2. Additional examples of QUADAGENT are depicted in Figure 1.

SCANdalous! (External Detection Using Network Scan Data and Automation)

Figure 1: QUADAGENT C2 servers in the
Shodan user interface

Five days after the QUADAGENT dropper was identified, Mandiant was
engaged by a victim that was targeted via the same C2. This activity
was later attributed to APT34. During the investigation, Mandiant
uncovered APT34 using RULER.HOMEPAGE. This was the first time our
consultants observed the tool and technique used in the wild by a real
threat actor. Our team developed a profile of servers hosting HOMEPAGE
payloads and began tracking their deployment in the wild. Figure 2
shows a timeline of QUADAGENT C2 servers discovered between February
and November of 2018.

Figure 2: Timeline of QUADAGENT C2 servers discovered
throughout 2018


A month after that aforementioned intrusion, Managed Defense
discovered a threat actor using RULER.HOMEPAGE to download and execute
POSHC2. All the RULER.HOMEPAGE servers were previously identified due
to our efforts. Our team developed a profile for POSHC2 and began
tracking their deployment in the wild. The threat actor pivoted to a
novel PowerShell backdoor, POWERTON. Our team repeated our workflow
and began illuminating those C2 servers as well. This activity was
later attributed to APT33 and was documented in our OVERRULED post.


Scanner, Better, Faster, Stronger

Our use of scan data was proving wildly successful, and we wanted to
use more of it, but we needed to innovate. How could we leverage this
dataset and methodology to track not one or two, but dozens of active
groups that we observe across our solutions and services? Even if
every member of Advanced Practices was dedicated to external
detection, we would still not have enough time or resources to keep up
with the amount of manual work required. But that’s the key word:
Manual. Our workflow consumed hours of individual analyst
actions, and we had to change that. This was the beginning of
SCANdalous: An automated system for external detection using
third-party network scan data.

A couple of nice things about computers: They’re great at
multitasking, and they don’t forget. The tasks that were taking us
hours to do—if we had time, and if we remembered to do them every
day—were now taking SCANdalous minutes if not seconds. This not only
afforded us additional time for analysis, it gave us the capability to
expand our scope. Now we not only look for specific groups, we also
search for common malware, tools and frameworks in general. We deploy
weak signals (or broad signatures) for software that isn’t inherently
bad, but is often used by threat actors.

Our external detection was further improved by automating additional
collection tasks, executed by SCANdalous upon a discovery—we call them
follow-on actions. For example, if an interesting open directory is
identified, acquire certain files. These actions ensure the team never
misses an opportunity during “non-working hours.” If SCANdalous finds
something interesting on a weekend or holiday, we know it will perform
the time-sensitive tasks against the server and in defense of our clients.

The data we collect not only helps us track things we aren’t seeing
at our clients, it allows us to provide timely and historical context
to our incident responders and security analysts. Taking observations
from Mandiant Incident Response or Managed Defense and distilling them
into knowledge we can carry forward has always been our bread and
butter. Now, with SCANdalous in the mix, we can project that knowledge
out onto the Internet as a whole.

Collection Metrics

Looking back on where we started with our manual efforts, we’re
pleased to see how far this project has come, and is perhaps best
illustrated by examining the numbers. Today (and as we write these
continue to grow), SCANdalous holds over five thousand signatures
across multiple sources, covering dozens of named malware families and
threat groups. Since its inception, SCANdalous has produced over two
million hits. Every single one of those, a piece of contextualized
data that helps our team make analytical decisions. Of course, raw
volume isn’t everything, so let’s dive a little deeper.

When an analyst discovers that an IP address has been used by an
adversary against a named organization, they denote that usage in our
knowledge store. While the time at which this observation occurs does
not always correlate with when it was used in an intrusion, knowing
when we became aware of that use is still valuable. We can
cross-reference these times with data from SCANdalous to help us
understand the impact of our external detection.

Looking at the IP addresses marked by an analyst as observed at a
client in the last year, we find that 21.7% (more than one in five)
were also found by SCANdalous. Of that fifth, SCANdalous has an
average lead time of 47 days. If we only consider the IP addresses
that SCANdalous found first, the average lead time jumps to 106 days.
Going even deeper and examining this data month-to-month, we find a
steady upward trend in the percentage of IP addresses identified by
SCANdalous before being observed at a client (Figure 3).

Figure 3: Percentage of IP addresses
found by SCANdalous before being marked as observed at a client by a
FireEye analyst

A similar pattern can be seen for SCANdalous’ average lead time over
the same data (Figure 4).

Figure 4: Average lead time in days for
SCANdalous over the same data shown in Figure 3

As we continue to create signatures and increase our external
detection efforts, we can see from these numbers that the
effectiveness and value of the resulting data grow as well.

SCANdalous Case Studies

Today in Advanced Practices, SCANdalous is a core element of our
external detection work. It has provided us with a new lens through
which we can observe threat activity on a scale and scope beyond our
organic data, and enriches our workflows in support of Mandiant. Here
are a few of our favorite examples:


In early 2019, SCANdalous identified a Cobalt Strike C2 server that
we were able to associate with FIN6. Four hours later, the server was
used to target a Managed Defense client, as discussed in our blog
post, Pick-Six:
Intercepting a FIN6 Intrusion, an Actor Recently Tied to Ryuk and
LockerGoga Ransomware


In late 2019, SCANdalous identified a BOOSTWRITE C2 server and
automatically acquired keying material that was later used to decrypt
files found in a FIN7 intrusion worked by Mandiant consultants, as
discussed in our blog post, Mahalo
FIN7: Responding to the Criminal Operators’ New Tools and Techniques

UNC1878 (financially motivated)

Some of you may also remember our recent
blog post on UNC1878
. It serves as a great case study for how we
grow an initial observation into a larger set of data, and then use
that knowledge to find more activity across our offerings. Much of the
early work that went into tracking that activity (see the section
titled “Expansion”) happened via SCANdalous. The quick response from
Managed Defense gave us just enough information to build a profile of
the C2 and let our automated system take it from there. Over the next
couple months, SCANdalous identified numerous servers matching
UNC1878’s profile. This allowed us to not only analyze and attribute
new network infrastructure, it also helped us observe when and how
they were changing their operations over time.


There are hundreds more stories to tell, but the point is the same.
When we find value in an analytical workflow, we ask ourselves how we
can do it better and faster. The automation we build into our tools
allows us to not only accomplish more of the work we were doing
manually, it enables us to work on things we never could before. Of
course, the conversion doesn’t happen all at once. Like all good
things, we made a lot of incremental improvements over time to get
where we are today, and we’re still finding ways to make more.
Continuing to innovate is how we keep moving forward – as Advanced
Practices, as FireEye, and as an industry.

Example Signatures

The following are example Shodan queries; however, any source of
scan data can be used.

Used to Identify APT39 C2 Servers

  • product:“bitvise” port:“443” org:“WorldStream

Used to Identify QUADAGENT C2 Servers

  • “PHP/7.2.0beta2”


  • html:“clsid:0006F063-0000-0000-C000-000000000046”