# APT28 Under the Scope
## A Journey into Exfiltrating Intelligence and Government Information


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### Contents

##### Preface...................................................................................................................................................3

 Targeted victims................................................................................................................................ 4

 Attribution.............................................................................................................................................5

 Scanning for new targets............................................................................................................... 6

 Attack flow...........................................................................................................................................7

 Attacked victims................................................................................................................................ 9

 Appendix 1 (Target campaigns)....................................................................................................11

 Appendix 2 - The probing process.............................................................................................13

 Appendix 3 - APT28 related tools..............................................................................................19

 Appendix 4 (First stage component).......................................................................................20

 Appendix 5 (Second Stage Component)..................................................................................21

 Appendix 6 - Additional module................................................................................................24

#### Authors:

- Răzvan BENCHEA – Team leader of Malware Research

- Cristina VATAMANU – Senior Malware Researcher

- Alexandru MAXIMCIUC – Senior Malware Researcher

- Victor LUNCAŞU – Junior Malware Researcher


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##### The discovery of Stuxnet in the nuclear processing plant in Natanz, Iran laid the ground for a new family of cyber-attacks: advanced persistent threats. Although the term has since become highly popular, state-sponsored cyber-intelligence operations have been carried out since long before the advent of Stuxnet or Flamer; less known advanced persistent threats such as APT28 (or Sofacy) have been covertly running in Europe since 2007.

### Preface

When it was initially coined, the term “Advanced Persistent Threat” was used to define an attack that, unlike regular commercial-grade
malware, would focus on a particular target, its network topology and defenses. The purpose of this type of attack is exfiltration of sensitive
data over a long period of time or silently crippling their industrial processes. In recent years, APTs have started to define persistent
attacks launched by foreign state adversaries that no security company or victim government would explicitly name for fear of economic
or political repercussions. Another aspect that makes calling on attacking states is the difficulty in attribution: the Internet has no physical
boundaries and the technical skills of the attacking parties can make it look as the attack was launched by a rival state.

To connect the dots between an identified attack and the state actor(s) behind it, companies like Bitdefender look for solid evidence inside
the APT code or in the used communication infrastructure. The following report is a technical investigation of some particularities in the
APT28 payload implementation that allowed us to link the threat to its operators.


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### Targeted victims

Our recent investigation into the Sofacy operations revealed that the cyber-group is extremely active and focused on specific regions. The
primary targets of APT28 are potential victims in several countries such as Ukraine, Spain, Russia, Romania, the United States and Canada.

A particular interest was found for Ukraine. Between Tuesday, 10th of February and Saturday, 14th of February 2015, the APT28 team
scanned 8,536,272 IPs for possible vulnerabilities.

Coincidentally, during this period, in Minsk, the political leaders of Belarus, Russia, Germany, France and Ukraine were participating in a
summit, discussing the ceasefire in the Donbass region in the east of Ukraine.

After February 14[th,] the APT28 operators shifted their attention to Spain.

The map of affected countries is illustrated in Figure 1.

Figure 1

It is currently unknown what criteria the APT28 operators used to select targets, but our research identified that they are picked from a list of
vulnerable IP addresses prepared beforehand. The same research shows all targets belong to different categories: political ([removed]),
e-crime services, telecommunication services or aerospace industry.

More information about the victims can be found in Appendix 1.


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### Attribution

We have reasons to believe that the operators of the APT28 network are either Russian citizens or citizens of a neighboring country that
speak Russian. Our assumption is supported by different markers identified during analysis.

When we first analyzed the pool of files we had collected on APT28, we counted the number of binaries compiled between 08:00 and 18:00
on working days (Monday to Friday) for every time zone. It was revealed that UTC+4 stands out, as 88% of files are compiled during this
interval. Out of the other countries sharing the same time zone (Russia, Georgia, Azerbaijan), Russia is the only country that possesses the
necessary skills and resources to pull off this kind of attack.

In the graphic below (Figure 2) the samples are grouped by the hour they were compiled (UTC+4). It can be seen that most were compiled
between 08:00 and 18:00.

###### Figure 2

Another clue that supports the assumption that the authors are from a Russian-speaking country is found in a hacking tool
designed to grant system privileges. This tool was found along with files related to APT28 files. The header of the file called xp.exe
(78450806E56B1F224D00455EFCD04CE3) features a hardcoded path to a debug file (C:\Users\Пользователь\Desktop\cve-2014-4076\
cve-19abdba\Debug\CVE-2014-4076.pdb). The string Пользователь translates to users in Russian.

We have reasons to believe that this file is part of the same APT28 attack since it had its modification/creation time modified to 14/04/2008,
16:00, just like most of the other APT28 files used in the attack. All files used in the attack were compiled after 2013. Changing the date was
just a measure to throw off any victim who would perform a shallow search for new files, should they have any doubts about the security
of their system.


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### Scanning for new targets

One of the main purposes of the server we analyzed is to automate the process of finding new victims that can be marked as “vulnerable.”
This is done by exhaustively scanning and probing a pre-determined pool of IP addresses contained in a range. The server-side application
has four main components, as follows:

a python script (gen_ip.py) that generates random IPs from a certain class

a database (shodb) for centralizing all the data

the scan bots that probe and screen potential victims

a Django (sho) application for bot administration and reporting.

Rather than taking a “shotgun” approach, the APT28 victims are hand-picked. The script has a hard-coded list of IP classes; it iterates
through these subnets and generates a list of random IPs within the range. If the operators scanned all IPs within the range, it would
increase the chances of the attacked party noticing their systems are actively probed from the outside. After the random generation
process completes, these IPs are added to the database. Each IP address is then given a priority level.

At the time of writing, the database contains the 58,624 IPs related to the incident described in Appendix 1. All these IP addresses have the
same priority level, set to 1.

The scanning bots are individual systems, all situated in different networks. We presume this was a necessary approach to make the
scanning process look organic. If a single IP or a set of IPs from the same network would scan entire subnets, it would raise suspicions or
trip intrusion detection systems and their activity would likely be interrupted.

To get their scan jobs, each bot would authenticate to the server via cookies. Upon successfully authenticating, the scan bot receives a
list of maximum 16 IPs per request. For each IP, it will scan certain ports using the nmap tool (Appendix 1). If it finds open ports, it then
contacts the server and the information retrieved by nmap is saved in the database. Subsequently, the respective IP address is marked as
vulnerable. The process is described in the figure below (Figure 3):

Figure 3


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The Django application represents a wrapper over the database and can be used to manage the bots and monitor their activity.

The main statistics displayed at the time of the writing are illustrated in table below (Figure 4):

Figure 4

The scan bots are geographically distributed as follows: 3 in the United States, 3 in United Kingdom and 3 in Bulgaria (Figure 5):

Figure 5

More information is provided in Appendix 2.

### Attack flow

The APT28 group relies on three distinct attack vectors to infect their targets: spear phishing e-mails with crafted Word and Excel documents
attached, phishing websites hosted on typosquatted domains and malicious iFrames leading to Java and Flash zero-day exploits.

The client usually gets infected by accessing an URL hosting an exploit kit. Upon successful exploitation, a first stage dropper (in our case
the name is runrun.exe) is written to the disk. Its main purpose is to drop a file (api-ms-win-downlevel-profile-l1-1-0.dll)
and execute it using rundll32.exe. This, in turn, contacts the C&C server and downloads the second stage component.

The second component is installed using the same method as described above. First, a dropper is executed (winloat.exe) which writes
one of the key components of the attack (advstoreshell.dll) to the disk, along with a configuration file (msd). The configuration
file contains essential information such as a list of three servers that the backdoor will try to contact (win*********ore.net
**,micro********er.com and** **1***.net), the interval between requests and a flag that indicates whether key logging should be**
activated or not.

At this point, the attacker takes control of the machine and deploys different tools and components to achieve his goal. In the case we


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analyzed, the attacker downloaded 3 hacking tools:

1. A tool to dump passwords from logged-in users (run.exe)

2. A tool that exploits a privilege escalation vulnerability (CVE-2014-4076) to gain system privileges (xp.exe)

3. A tool that acts as a proxy and allows the attacker to contact the system even if it is behind a router (svehost.exe)

An additional component is also downloaded (pr.dll) that is a modular component used to upload stolen data to the C&C.


###### Backdoor and Second stage downloader (advstoreshell.dll)


###### vTools: xp.exe run.exe svehost.exe


###### Additional Module: Pr.dll


The attacked flow is described in the figure below (Figure 6):

Figure 6

More information about the tools used is provided in Appendix 3. The first stage component and the second stage component are further
discussed in Appendix 4 and Appendix 5, respectively. Finally, the additional module (pr.dll) is analyzed in Appendix 6.

Right after deploying their payloads, the attacking party changed the time stamps of all the files downloaded by the second stage dropper
to 14/04/2008, 16:00. This technique allowed the attackers to hide the files in case victims would suspect the attack and attempt to list
new files written on the disk.


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The table below shows the compilation date and the file creation time for each of the files involved in the attack.

**File Name** **Compilation Date** **Creation Time**

%allusersappdata%\xp.exe 12/03/2015 12:54:57 14/04/2008 16:00

%allusersappdata%\run.exe 05/04/2013 12:51:41 14/04/2008 16:00

%allusersappdata%\svehost.exe 22/04/2015 11:49:54 14/04/2008 16:00

%localappdata%\Microsoft Help\advstorshell.dll 30/04/2015 13:13:13 14/04/2008 16:00

%allusersappdata%\msd 14/04/2008 16:00

%allusersappdata%\Pr.dll 13/05/2015 22:05:57 14/04/2008 16:00

%temp%\winloat.exe 30/04/2015 14:24:54 13/05/2015 17:23

%temp%\api-ms-win-downlevel-profile-l1-1-0.dll 08/05/2015 13:39:32 13/05/2015 16:22

%temp%\runrun.exe 08/05/2015 13:45:31 13/05/2015 16:22

Table 1

The latest creation date is 13/05/2015, which hints at the date the attack happened. Given that it took almost an hour from the moment the
first downloader got written to the disk to the arrival of the second stage downloader, this process was likely carried out manually by a human
operator. An important observation is that all of the components, except one, had been compiled before the attack. “%allusersappdata%\
Pr.dll” is the only file that was compiled 5 hours after the compromise. This suggests this file was specifically built for the target.

### Attacked victims

The victims we have identified belong to different industries and sectors. Our research shows that the attack targeted political figures,
government institutions, telecommunication and e-crime services, as well as aerospace companies from Germany, Ukraine, Russia and
Romania.

Some of the victims could be identified by analyzing the information stored on the central server we have investigated. The stored
information contained traces of stolen information in the form of e-mails. Some were reported by our internal systems.

The e-mails we have identified on the central server revealed victims from the first two categories.

**Political figures**

On 13[th] May 2015 two .pst files named ‘C:\ProgramData\backup.pst’ and ‘C:\ProgramData\backup2.pst’ were copied to
the server. The e-mails seem to be addressed to the [redacted].

**Aerospace industry**

On July 9[th] 2015, fourteen archives were copied on the server. Among them were two .dbx (Outlook Express database) files. The e-mails
seem related to [redacted].

On June 18th the file “F:\Outlook Express\AC_VTS_ÐÊÒ.dbx” was transferd to the central server. The emails belong to **[removed]**

Also on June 18[th,] the file “F:\Outlook Express\Kydla.dbx” was transfered on the server. The emails belong toSecond stage dropper (winloat.exe) **[redacted]**

|File Name|Compilation Date|Creation Time|
|---|---|---|
|%allusersappdata%\xp.exe|12/03/2015 12:54:57|14/04/2008 16:00|
|%allusersappdata%\run.exe|05/04/2013 12:51:41|14/04/2008 16:00|
|%allusersappdata%\svehost.exe|22/04/2015 11:49:54|14/04/2008 16:00|
|%localappdata%\Microsoft Help\advstorshell.dll|30/04/2015 13:13:13|14/04/2008 16:00|
|%allusersappdata%\msd||14/04/2008 16:00|
|%allusersappdata%\Pr.dll|13/05/2015 22:05:57|14/04/2008 16:00|
|%temp%\winloat.exe|30/04/2015 14:24:54|13/05/2015 17:23|
|%temp%\api-ms-win-downlevel-profile-l1-1-0.dll|08/05/2015 13:39:32|13/05/2015 16:22|
|%temp%\runrun.exe|08/05/2015 13:45:31|13/05/2015 16:22|


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All these victims seem related to the aerospace industry or aircraft research programs. The context also supports the Russian origin, given
the huge media coverage of the Russian “smartplane” PAK FA T-50 **Fighter and America’s F-35. We presume the APT28 authors**
might have attempted to explore new technologies being developed in the aerospace industry for integration.

Our internal services also flagged a number of computers as under attack, such as those belonging to [redacted]. In Romania, these
flagged computers are part of government infrastructures or are closely related to the Romanian government.


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### Appendix 1 (Target campaigns)

Analyzing the APT28 command and control server identified two ongoing attempts to find new victims.

The process consists of scanning the ports of a large IP list, extracted from a database. The port scanning process is carried out via
the free tool “nmap”. If one of the ports is found open, the IP is marked as “vulnerable” and the corresponding information is saved. The
command used for probing is illustrated in figure below:

```
nmap

```
```
-T5
-p 21,22,23,25,80,110,143,443,465,993,995,8080,7071,3389,5900
-sV
-O
--version-light
--script=banner
--script=http-headers
-oX out_name

###### -iL targets_file

```

Figure 7

Where:

**-T5:** scan will map a host in very little time, provided you are on a very fast network or don’t mind losing some information along the way.

**-p: scan specific ports**

**-sV: detect remote services (server / daemon) version numbers**

**-O: identify a remote host apps and OS**

**--version-light: enable light mode that makes version scanning much faster, but it is slightly less likely to identify services**

**--script=banner: shows ftp banner responses**

**--script=http-headers: performs a HEAD request for the root folder (“/”) of a web server and displays the HTTP headers returned.**

-oX: logs the results of the scan in XML form into the file specified as an argument

**-iL: reads target specifications from a given configuration file rather than from the command line; the file should contain a list of host or**
network expressions separated by spaces, tabs or newlines.

The first attempt we have identified started on 10[th] of February 2015 when a list of 8.762.102 IPs were scanned. Out of this large list,
1.712.363 IPs were marked as “vulnerable.” The probing process lasted until February 14th. Most of the IPs were from Ukraine (8.536.272


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The number of scanned IPs is exhaustive, almost equal to the total number of IPs allocated to Ukraine. Searching through the major events
regarding Ukraine in that time frame, we realized that the probing took place during the summit in Minsk regarding the ceasefire in the
Donbass region of Eastern Ukraine. On February 11[, ]the leaders of Belarus, Russia, Germany, France and Ukraine met to negotiate the new
set of peace measures that were announced on February 12 and signed on February 15.

The targeted countries in these campaigns are illustrated in table below (Table 2):

###### Country Number of vulnerable IPs

 Ukraine 1704837

 Russian Federation 4666

 Romania 1287

 Bulgaria 1272

 United States 150

 Canada 149

 Italy 2

Table 2

On February 14, a backup of the database was saved in a file (file.csv) and the attacker’s focus moved from Ukraine to Spain.

On February 16, a new attempt to find new victims was made. The new database was loaded with 58,624 new IP addresses, out of which
6,146 were found vulnerable after probing. The targeted countries in this case are illustrated in Table 3:

###### Country Number of vulnerable IPs

 Spain 5511

 United Kingdom 584

 Portugal 47

 United States 2

 Mexico 2

Table 3

Since in this second attempt the set of IPs is rather small, we presume that this attack was highly targeted to specific institutions. By
analyzing the target IP ranges, we were able to identify some of the victims.

The decision logic related to which victims should be infected is unknown to us at the time of writing, but we presume the vulnerable
targets are next engaged by a human attacker rather than in an automated way.

|Country|Number of vulnerable IPs|
|---|---|
|Ukraine|1704837|
|Russian Federation|4666|
|Romania|1287|
|Bulgaria|1272|
|United States|150|
|Canada|149|
|Italy|2|

|Country|Number of vulnerable IPs|
|---|---|
|Spain|5511|
|United Kingdom|584|
|Portugal|47|
|United States|2|
|Mexico|2|


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### Appendix 2 - The probing process

The screening process for finding new possible victims is carried out via multiple bots managed by a central command and control server.
The operational flow can be broken down as follows:

1. Generating new victims

Potential victims are identified by port-scanning a list of IPs belonging to different subnets. This list is generated by a script, gen_ip.py,
which iterates through IP classes and randomly picks IPs for port-probing. At the time of writing, the script has 11 IP classes hardcoded,
which leads us to believe that victim organizations are picked manually.

The script generates random integer numbers that represent IPs belonging to these classes. The fact that it doesn’t iterate consecutively
through all the IPs shows that the attackers have taken all precautions not to trigger alarms in the victims’ intrusion detection system scanning all the IPs from a subnet may raise suspicions.

All generated IPs are added to a database on the central server. Most of these IPs are from Spain and belong to political institutions
([redacted]), telecommunication companies and public affairs institutions.

2. Centralizing all information

The command and control server’s most important asset is the shodb database. After restoring the database, the following relations were
found (Figure 9):

Figure 9

**-** **The sho_bot table contains information regarding the active bots (Table 5)**

###### id uid    code_id  info  last_activity

 8 345051441193 1 2015-02-12 16:09:53.698382+02

 9 345051780961 1 2015-02-12 16:09:55.111999+02

 7 345051783097 1 2015-02-12 16:09:59.12588+02

 5 161343679352 1 2015-02-12 16:10:01.145779+02

 6 161343570082 1 2015-02-12 16:10:01.382175+02

 4 345051780666 1 2015-02-12 16:10:01.405667+02

 3 161340745510 1 2015-02-12 16:10:02.940085+02

|The|sho_bot table contains|s information re|egarding the|e active bots (Table 5)|
|---|---|---|---|---|
|id|uid|code_id|info|last_activity|
|8|345051441193|1||2015-02-12 16:09:53.698382+02|
|9|345051780961|1||2015-02-12 16:09:55.111999+02|
|7|345051783097|1||2015-02-12 16:09:59.12588+02|
|5|161343679352|1||2015-02-12 16:10:01.145779+02|
|6|161343570082|1||2015-02-12 16:10:01.382175+02|
|4|345051780666|1||2015-02-12 16:10:01.405667+02|


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###### id uid    code_id  info  last_activity

 1 207376762666 1 2015-02-12 16:10:09.403215+02

 2 345051770354 1 2015-02-12 16:10:09.97157+02

Table 4

There are 9 bots, each one identified through a cookie (uid). This cookie is used by the bot to register on the main server. If the server has
a reference to that cookie, the process continues. The priority (code_id) is set to 1 for all the bots. The info table column is empty for
all the entries.

**-** **sho_cookie table contains information about the bots’ activity (Table 6)**

**id** **cookie** **active bot_id** **last_activity       ip_addr** **get_num**

2015-02-12
24 5PZLO3CDVTIN3VXCKTKEACIOC8960CDI t 2 16:09:05.202448 87.***.***.18 3

2015-02-10
12 LZJMU3UUCWTR3B3MDQZUHHP12T9RF2Y1 t 3 19:36:45.144918 5.***.***.223 3

2015-02-10
22 5FIRR9LY5PAWAJE0ROJ0WJT2UE2XTYRD t 5 22:02:36.856878 5.***.***.160 3

2015-02-12
26 VL1JCPSHSJGENP71CCLF9AOLUYXJN8IJ t 8 16:08:57.869532 192.*.**.215 3

2015-02-12
25 6DFLVNT0E6GFQGIOWLL2BNE378KYF5OG t 1 16:08:59.608116 23.**.**.98 3

2015-02-10
23 R814WUWHBFEJZP8K4RMFCUTP49U90IQO t 6 22:03:17.29154 5.***.***.150 3

2015-06-06
7 345051770354 t 2 07:02:03.173137 87.***.***.18 8285348

2015-06-09
9 207376762666 t 1 00:28:13.25646 23.**.**.98 7046287

2015-02-12
20 9DPR3K6NI77WU48S1ECAH61YYUZE2QAJ t 7 16:09:00.011016 87.***.***.155 3

2015-04-18
31 STGKT18882NZTA24YP9FCFNPE6EBCFBO t 5 05:36:10.383518 5.***.***.160 3

2015-04-18
33 UB7M7VLR5I8S4V8H4CCKNEWG9JHD89QD t 3 05:53:33.533398 5.***.***.223 3

2015-02-12
19 7JU8XX4IEKOIAYVBVM27IYS6G39ZINYO t 9 16:08:54.475175 5.**.***.36 3

2015-06-09
35 UO2D3O5H91BMZZYNN7T8Q5ZA9BE0XIKM t 1 00:28:13.257237 23.**.**.98 3

2015-02-12
21 S7JWM4N6LFBX18NFL21J15JYLWFBQYTZ t 4 16:09:05.202402 87.***.***.12 3

|id|uid|code_id|info|last_activity|
|---|---|---|---|---|
|1|207376762666|1||2015-02-12 16:10:09.403215+02|
|2|345051770354|1||2015-02-12 16:10:09.97157+02|

|Col1|- sho_cookie table contains information|about th|he bots’ ac|ctivity (Table 6)|Col6|Col7|
|---|---|---|---|---|---|---|
|id|cookie|active|bot_id|last_activity|ip_addr|get_num|
|24|5PZLO3CDVTIN3VXCKTKEACIOC8960CDI|t|2|2015-02-12 16:09:05.202448|87.***.***.18|3|
|12|LZJMU3UUCWTR3B3MDQZUHHP12T9RF2Y1|t|3|2015-02-10 19:36:45.144918|5.***.***.223|3|
|22|5FIRR9LY5PAWAJE0ROJ0WJT2UE2XTYRD|t|5|2015-02-10 22:02:36.856878|5.***.***.160|3|
|26|VL1JCPSHSJGENP71CCLF9AOLUYXJN8IJ|t|8|2015-02-12 16:08:57.869532|192.*.**.215|3|
|25|6DFLVNT0E6GFQGIOWLL2BNE378KYF5OG|t|1|2015-02-12 16:08:59.608116|23.**.**.98|3|
|23|R814WUWHBFEJZP8K4RMFCUTP49U90IQO|t|6|2015-02-10 22:03:17.29154|5.***.***.150|3|
|7|345051770354|t|2|2015-06-06 07:02:03.173137|87.***.***.18|8285348|
|9|207376762666|t|1|2015-06-09 00:28:13.25646|23.**.**.98|7046287|
|20|9DPR3K6NI77WU48S1ECAH61YYUZE2QAJ|t|7|2015-02-12 16:09:00.011016|87.***.***.155|3|
|31|STGKT18882NZTA24YP9FCFNPE6EBCFBO|t|5|2015-04-18 05:36:10.383518|5.***.***.160|3|
|33|UB7M7VLR5I8S4V8H4CCKNEWG9JHD89QD|t|3|2015-04-18 05:53:33.533398|5.***.***.223|3|
|19|7JU8XX4IEKOIAYVBVM27IYS6G39ZINYO|t|9|2015-02-12 16:08:54.475175|5.**.***.36|3|
|35|UO2D3O5H91BMZZYNN7T8Q5ZA9BE0XIKM|t|1|2015-06-09 00:28:13.257237|23.**.**.98|3|


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**id** **cookie** **active bot_id** **last_activity       ip_addr** **get_num**

2015-03-17
2 345051441193 t 8 07:46:35.012289 192.*.**.215 3073574

2015-04-18
32 CLQS319XK8UBN6AMNPZOG89J2YHNUWB4 t 6 05:31:43.481397 5.***.***.150 3

2015-04-18
8 161340745510 t 3 05:53:33.532651 5.***.***.223 5445010

2015-03-18
4 345051780666 t 4 09:40:52.701857 87.***.***.12 2344761

2015-02-14
3 345051783097 t 7 15:26:34.879942 87.***.***.155 208586

2015-03-18
30 W301ZX83BLGN0BAY6XU8QANKYOJYHKPT t 4 09:40:52.702595 87.***.***.12 3

2015-04-18
5 161343679352 t 5 05:36:10.382767 5.***.***.160 5651159

2015-03-17
28 HPKFFT7ACLL89YLIXCYD9WFSZTLOZI3A t 8 07:46:35.013037 192.*.**.215 3

2015-02-14
29 XWUHUJ3P6SEV02UN9O4C188MQGBTM2TL t 7 15:26:34.880691 87.***.***.155 3

2015-03-04
27 7CH9P1PGXHPWH9J1ENU8XPAL27RC0GCL t 9 11:47:35.429737 5.**.***.36 3

2015-06-06
34 7SEE8AJRY56OOCAHURYT14FSYIRJ69VX t 2 07:02:03.173882 87.***.***.18 3

2015-04-18
6 161343570082 t 6 05:31:43.480646 5.***.***.150 6785223

2015-03-04
1 345051780961 t 9 11:47:35.428945 5.**.***.36 1664623

###### Table 6

The cookie column represents the authentication code, last_activity is the last date when the bot has reached the server, ip_addr
is IP of the bot, and get_num column represents the number of connections done by the bot with the central server.

This research also shows that the bots are duplicated and that there are two types of cookies: one using letters and numbers and one
using only numbers. The activity of the first type is very poor (3 connection for each cookie), which leads us into believing that, over time,
there were two versions of bots and plugins running in parallel. This can be explained also by the fact that the first type of cookie doesn’t
appear in the sho_bot table.

        - **sho_ip table contains the list of IPs generated by the gen_ip.py script**

        - **sho_plugin table contains code, not data. It stores the script plugin-nmap-0.43.py. This script’s main role is to**
fetch the targeted IPs (up to 16 IPs per request) and execute the scanning command (Figure 10)

|id|cookie|active|bot_id|last_activity|ip_addr|get_num|
|---|---|---|---|---|---|---|
|2|345051441193|t|8|2015-03-17 07:46:35.012289|192.*.**.215|3073574|
|32|CLQS319XK8UBN6AMNPZOG89J2YHNUWB4|t|6|2015-04-18 05:31:43.481397|5.***.***.150|3|
|8|161340745510|t|3|2015-04-18 05:53:33.532651|5.***.***.223|5445010|
|4|345051780666|t|4|2015-03-18 09:40:52.701857|87.***.***.12|2344761|
|3|345051783097|t|7|2015-02-14 15:26:34.879942|87.***.***.155|208586|
|30|W301ZX83BLGN0BAY6XU8QANKYOJYHKPT|t|4|2015-03-18 09:40:52.702595|87.***.***.12|3|
|5|161343679352|t|5|2015-04-18 05:36:10.382767|5.***.***.160|5651159|
|28|HPKFFT7ACLL89YLIXCYD9WFSZTLOZI3A|t|8|2015-03-17 07:46:35.013037|192.*.**.215|3|
|29|XWUHUJ3P6SEV02UN9O4C188MQGBTM2TL|t|7|2015-02-14 15:26:34.880691|87.***.***.155|3|
|27|7CH9P1PGXHPWH9J1ENU8XPAL27RC0GCL|t|9|2015-03-04 11:47:35.429737|5.**.***.36|3|
|34|7SEE8AJRY56OOCAHURYT14FSYIRJ69VX|t|2|2015-06-06 07:02:03.173882|87.***.***.18|3|
|6|161343570082|t|6|2015-04-18 05:31:43.480646|5.***.***.150|6785223|
|1|345051780961|t|9|2015-03-04 11:47:35.428945|5.**.***.36|1664623|


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Figure 10

After parsing the nmap results, the data is sent back to the server and saved to the database in the sho_ip table.

        - **sho_code** table contains code, not data. It stores the scan-bot-03.py script. This script describes the behavior
of the system. The bot connects to the server and registers itself using a cookie. Once authenticated, the bot requests the last
version of the code from the sho_code table (update mechanism) and the last version of the plugin (sho_plugin) that has to
be executed. Once updated, it starts a list of threads that will use the latest plugin to scan the targeted IPs.

        - **sho_log table is empty**

        - **sho_setting table is empty**

A Django application is also running on the server. It serves as an administrative and reporting interface for the bots and targets. The
figure below (figure 11) illustrates the admin panel. This interface allows a human operator to view statistics, add users, targets or bots
or delete them.


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Figure 11


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The interface is very simple, representing just a wrapper over the database (figure 12: add user (up) add targets (below)):

Figure 12

1. _The bots_

The machines used to scan the potential victims are in different networks. This helps the attackers conceal that scans originating from
these IPs are part of the same operation. The table below shows the IPs of the bots found in the database at the time of writing (Table 7):

###### BOT IP Country

 192.*.**.215  US

 5.**.***.36  US

 87.***.***.155  UK

 5.***.***.160 Bulgaria

 5.***.***.150 Bulgaria

 87.***.***.12 UK

 5.***.***.223 Bulgaria

 23.**.**.98 US

  87.***.***.18 UK

Table 7

|BOT IP|Country|
|---|---|
|192.*.**.215|US|
|5.**.***.36|US|
|87.***.***.155|UK|
|5.***.***.160|Bulgaria|
|5.***.***.150|Bulgaria|
|87.***.***.12|UK|
|5.***.***.223|Bulgaria|
|23.**.**.98|US|
|87.***.***.18|UK|


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### Appendix 3 - APT28 related tools

###### Proxy tool (svehost.exe)

This executable file can be either used with arguments passed via the command line or without. When executed without arguments, it
attempts to contact the IP address: 176.**.***.10 on port 443.

It can also be started with the following command line:

svehost.exe start <ipaddress> <port>

The file uses an old version of the OpenSSL library (OpenSSL 1.0.1e). In fact, the reason this file is 1038 kilobytes in size is because of the
inclusion of this library.

The main purpose of the tool is to allow an attacker to contact systems behind a router, which otherwise would not be accessible from
outside the network.

###### Privilege escalation tool (xp.exe)

The tool is built around a vulnerability discovered in 2014 (CVE-2014-4076) and works by sending a specially crafted package to the \\.\\
TCP device through the use of DeviceIoControl function. The vulnerability was fixed in late 2014.

The tool works by receiving one executable file as argument. The tool will then run this executable file with system privileges.

The file is compiled using the debug configuration. Because of this, a path to a pdb file is also hardcoded into the file. The path references
C:\Users\Пользователь\Desktop\cve-2014-4076\cve-19abdba\Debug\CVE-2014-4076.pdb

The string Пользователь means users in Russian. This is one of the reasons we suspect the authors are Russian speakers.

###### Password dumping tool (run.exe)

This seems to be based on the source of a public tool (mimikatz) for dumping passwords out of WDigest through LSASS. More information
about the tool can be found by visiting the author’s page: http://blog.gentilkiwi.com/mimikatz.

The file was compiled on 05/05/2013 and does not contain any version info. This suggests the authors used a custom build.

The tool receives a file as argument. The retrieved password will then be dumped into the file passed as argument.


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### Appendix 4 (First stage component)

This is the first component that gets on the computer after infection. Its purpose is to contact the C&C server and ask for further instructions.

The first stage payload is made of two items: a dropper and a first stage backdoor. The dropper, which in our case is named runrun.
**exe, has embedded the file api-ms-win-downlevel-profile-l1-1-0.dll** in the data section. The dropper uses many custom
encryption algorithms to protect itself from reverse-engineering.

The following algorithm (Algorithm 1) is used to decrypt its APIs:

Algorithm1:

```
b[idxbuff+1] = b[idxbuff+1] ^ b[idxbuff] ^ key[(idxbuff+1)%keylen]

```

The payload (api-ms-win-downlevel-profile-l1-1-0.dll) is encrypted using RTL and a custom encryption algorithm that uses
a 10 byte key (Algorithm 2):

Algorithm2:

```
)unsigned char encrypt1(int pos
{
;xor = 0
)++for(int i=0;i<key_length;i
;)xor ^= key[i] + (unsigned char)(i*pos
return xorl
}

```

The dropped file is a downloader that contacts the C&C to get the second stage component. It contacts the domain: www.msc****vw.com
and the IP address: 91.***.**.249.

The requests are performed via GET over HTTP. They are randomly generated. Each request has 1 to 5 groups of 1 to 6 randomly generated
chars and numbers. Each group is separated by a slash. An example of a request is given below:
```
    /ue/VHghm/ihXAIK/qpi/1c9.xml/?XK1=VrLYQndXGXwzURh9RBE=

```
The xml extension present above is actually picked from 4 available extensions (xml, pdf, html, zip).

The final argument is an encrypted key, probably used by the downloader to authenticate itself to the server.

This file works as a rudimentary backdoor that has the following commands available:

- Download

- Execute

- Delete files

Since it took more than an hour for the second stage component to be downloaded to the infected system, we presume this is downloaded


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### Appendix 5 (Second Stage Component)

The purpose of the second stage component is to open a backdoor to allow the attacker to assess the target and download additional
components.

a. Dropper:

As the First Stage Component, the important file is being installed using a dropper. (winloat.exe)

The main steps followed by the dropper are:

1. Decrypt its overlay (containing the backdoor: advstoreshell.dll and the configuration file: msd)

2. Write payload on disk

3. Call the InitW method of advstoreshell.dll

b. Configuration File:

To encrypt/decrypt the configuration file and the APIs used by advstoreshell.dll the component uses a custom stream cypher with
key length of 6. The function given below is used to retrieve the corresponding byte used to XOR at an index in the buffer.

Algorithm 3:

```
unsigned char decrypt(unsigned char key[6], uint32& buffer_pos)
{
       ixk = buffer_pos % 6 +2;
       ixb = (unsigned char)(ixk);
       buffer_pos++;
       v_10 = key[(ixk -2)%6]^byte((uint32(ixb)*uint32(key[ixk-2])) >> 7)
       v_11 = key[(ixk+1)%6] ^ ixb;
       xc = (key [ixk%6] & v_11) + v10;
       return xc
}

```

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The configuration file (msd) file is encrypted using the algorithm described above and is 122 bytes long. It contains the following information:

**Computer** **Two domain** **Keylogging** **Main**
**Timeout1** **Timeout2** **Name** **names** **Campaign id** **?** **?** **flag** **domain**
**name**

Where:

- Timeout1 = 60000 ; represents the number of miliseconds until timeout is reached when contacting the C&C

- Timeout2 = 60000 ; represents the number of miliseconds until timeout is reached when using contacting the C&C using encryption

- Computer name = DAE13WS01204030501 ; The name of the infected computer

- DomainName1= micro**********ter.info

- DomainName2=dri********te.info

- Campaign id = rhbp ; we are still investigating the meaning of this field, but it is very likely a campaign identifier

- ? = 1000 ; we are still investigating the role of this field, although preliminary information leads us to believe that it is used as a counter

- ? = 600000; we are still investigating the meaning of this field, but it is very likely another timeout value

- Keylogging flag = 1; this indicates whether the backdoor should intercept keystrokes and send them to the C&C server.

- Main domain name = 1***.net ; this is the first domain that the malware tries to connect

c. Backdoor Component:

The most important component of APT28 is the backdoor, in this case, the file called advstoreshell.dll. This file reads the configuration
file (msd) and contacts the C&C servers indicated in the configuration file. It can also read the configuration file from the registry. The
configuration stored in the registry is probably obtained via update from one of the C&C servers.

On the system we have analyzed, there were two configurations:

1. The first one being the msd file that was located in the same path with advstoreshell.dll

2. The second one was found in registry (HKCU\Software\Microsoft\Windows\CurrentVersion\Explorer\
**Path). It contains almost the same data as the msd file except for the first two domain names that are changed to win[redacted]**
ore.net and micr[redacted]er.com.

This was the only component that was set to run at startup. This was achieved by creating the value AdvancedStorageShell in HKCU\
**Software\Microsoft\Windows\CurrentVersion\Run with the contents:**

**“RUNDLL32.EXE “C:\Documents and Settings\<user-name>\Local Settings\Application Data\Microsoft Help\advstorshell.dll”, InitW”**

|Timeout1|Timeout2|Computer Name|Two domain names|Campaign id|?|?|Keylogging flag|Main domain name|
|---|---|---|---|---|---|---|---|---|


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The backdoor is highly advanced and supports 26 commands. We analyzed some of the supported commands, while the analysis for the
rest is still in progress.

5 find file

6 get file content and timestamps

9 enumerate registry keys and values

10 set registry value

11 delete registry value

13 start process using CreateProcess

14 kill process by id

16 start process using cmd /c

17 list drives and types

19 read file from offset

21 get system info (run systeminfo in cmd)

25 create thread

When running a command using the cmd /c option, the output is saved in a file named tmp.dat

Communication between this component and the C&C server is carried out on port HTTP (443). If this is not possible, it attempts to
connect on port 80. In some cases, the communication is encrypted using 3DES and RSA.

One important function of this backdoor is that it can load external components. It can achieve this goal by calling the init function of
any dll. This is how the s plugin is loaded.

|ll in progress.|Col2|
|---|---|
|5|find file|
|6|get file content and timestamps|
|9|enumerate registry keys and values|
|10|set registry value|
|11|delete registry value|
|13|start process using CreateProcess|
|14|kill process by id|
|16|start process using cmd /c|
|17|list drives and types|
|19|read file from offset|
|21|get system info (run systeminfo in cmd)|
|25|create thread|


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### Appendix 6 - Additional module

The final component installed by APT28 is a module named pr.dll. This file was compiled 5 hours after the system was compromised.
This discrepancy in time prompts us that the file is individually crafted for each target system.

The file seems to be a modular framework that can accommodate different modules. It stores its configuration in an encrypted format, and
saves it in the Registry key “HKU\S-1-5-19_Classes\Software\Microsoft\MediaPlayer\{some_clsid}\chnnl” .

Once executed, the malware checks for the presence of a mutex (XSQWERSystemCriticalSection_for_1232321) to make sure that another
instance of itself is not already running. After that, it contacts the server (the IP: 198.[redacted].74 or the domain name sec[redacted]win.
com). During the initial communication, the backdoor sends an encrypted buffer to the C&C server that contains a RC4 key and the IDs of
the plugins it has installed. The buffer is shown below:

###### 03 33 02 03 33 23 03 21 23 06 21 23 3A 66 95 64 4B 56 FF 22 4D 81 4F

We can identify plugins with the following ids from the buffer above: 0x3302, 0x3303, 0x2103, 0x2106

An interesting aspect of the pr.dll is that it creates two pipes that are presumably used to collect data sent by other processes.

The **pr.dll seems to be a Windows version of a Linux file found on the server. The server hosts 4 elf files, two built for the x86**
architecture (075b6695ab63f36af65f7ffd45cccd39, **f3bf929a35c3f198226b88537d9ccb1a) and two for the x64 architecture**
(2683624eacc490238e98c449bddbb573, 5bf524a4860f3c33e3ad77b6b625db37). The elf file uses SQLite3 rather than Registry
to store its configuration. The database named My_BD holds two values chnnl and prms.

The files attempt to contact the server by constructing random requests. Each request is made of one of the verbs (watch/, search/,
results/, search/, close) and two to nine arguments (selected from the list: text=, from=, itwm=, ags=, oe=, aq=, btnG=, oprnd=, utm=,
channel=). The values of the arguments are randomly generated.

An example of a request is given below:

**_h[tt]p://198.***.***.74/watch/?aq=JTfJRp-s&oprnd=Dwtee&itwm=niKMuGE9Mp9Md9vHdggZMS16YlSTx&btnG=t&oprnd=FbLtw&AVVAT=m_**
**_8I2tN_**

The elf files contain the following modules:
1. AgentModule
2. KernelProvider
3. AgentKernel
4. ChannelController
5. Cryptor
6. LocalStorage (sqlite3)
7. ReserverApi
8. AgentChannel
9. HttpChannel
10. FSModule
11. RemoteShell
12. RemoteKeylogger

As stated above, there are two versions for each architecture. The only difference between them is that one version does not include the
last two modules.


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### About the Authors

**Răzvan Benchea has been working as a malware researcher for more than eight years. He is an associate professor at the Alexandru Ioan**
Cuza university of Iaşi where he teaches operating systems and assembly language. Răzvan’s areas of expertise includes APT forensics
and reverse engineering. His academic accomplishments include a PhD in machine learning algorithms for malware detection.

**Alexandru Maximciuc spent the past 10 years doing antimalware research at Bitdefender. He is an expert in reverse engineering and**
cryptanalysis. Alexandru has played a key role in the analysis and mapping of the PushDO botnet and is in charge with the automation of
sinkholing operations.

**Cristina Vatamanu is a malware researcher with a focus on botnets. With more than six years of experience in reverse engineering, exploit**
analysis and cryptography, Cristina has brought her contribution to the threat intelligence scene at conferences such as AVAR and Virus
Bulletin.

**Victor Luncasu is a junior malware researcher focused on algorithms and data structures. His experience includes reverse engineering and**
payload analysis as well as custom communication protocols between bots and command & control servers.


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