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During our threat hunting exercises in recent months, we’ve started to observe a distinguishing pattern of msiexec.exe usage across different endpoints. As we drilled down to individual assets, we found traces of a recently discovered malware called Raspberry Robin. The RedCanary Research Team first coined the name for this malware in their blog post, and Sekoia published a Flash Report about the activity under the name of QNAP Worm. Both articles offer great analysis of the malware’s behavior. Our findings support and enrich prior research on the topic.
Raspberry Robin is a worm that spreads over an external drive. After initial infection, it downloads its payload through msiexec.exe from QNAP cloud accounts, executes its code through rundll32.exe, and establishes a command and control (C2) channel through TOR connections.
Let’s walkthrough the steps of the kill-chain to see how this malware functions.
Raspberry Robin is delivered through infected external disks. Once attached, cmd.exe tries to execute commands from a file within that disk. This file is either a .lnk file or a file with a specific naming pattern. Files with this pattern exhibit a 2 to 5 character name with an usually obscure extension, including .swy, .chk, .ico, .usb, .xml, and .cfg. Also, the attacker uses an excessive amount of whitespace/non printable characters and changing letter case to avoid string matching detection techniques. Example command lines include:
File sample for delivery can be found in this URL:
https://www.virustotal.com/gui/file/04c13e8b168b6f313745be4034db92bf725d47091a6985de9682b21588b8bcae/relations
Next, we observe explorer.exe running with an obscure command line argument, spawned by a previous instance of cmd.exe. This obscure argument seems to take the name of an infected external drive or .lnk file that was previously executed. Some of the samples had values including USB, USB DISK, or USB Drive, while some other samples had more specific names. On every instance of explorer.exe we see that the adversary is changing the letter case to avoid detection:
After delivery and initial execution, cmd.exe spawns msiexec.exe to download the Raspberry Robin payload. It uses -q or /q together with standard installation parameter to operate quietly. Once again, mixed case letters are used to bypass detection:
As you can see above, URLs used for payload download have a specific pattern. Domains use 2 to 4 character names with obscure TLDs including .xyz, .hk, .info, .pw, .cx, .me, and more. URL paths have a single directory with a random string 11 characters long, followed by hostname and the username of the victim. On network telemetry, we also observed the Windows Installer user agent due to the usage of msiexec.exe. To detect Raspberry Robin through its URL pattern, use this regex:
^http[s]{0,1}\:\/\/[a-zA-Z0-9]{2,4}\.[a-zA-Z0-9]{2,6}\:8080\/[a-zA-Z0-9]+\/.*?(?:-|\=|\?).*?$
If we look up the WHOIS information for given domains, we see domain registration dates going as far back as February 2015. We also see an increase on registered domains starting from September 2021, which aligns with initial observations of Raspberry Robin by our peers.
| WHOIS Creation Date | Count |
| 12/9/2015 | 1 |
| … | … |
| 10/8/2020 | 1 |
| 11/14/2020 | 1 |
| 7/3/2021 | 1 |
| 7/26/2021 | 2 |
| 9/11/2021 | 2 |
| 9/23/2021 | 9 |
| 9/24/2021 | 6 |
| 9/26/2021 | 4 |
| 9/27/2021 | 2 |
| 11/9/2021 | 3 |
| 11/10/2021 | 1 |
| 11/18/2021 | 2 |
| 11/21/2021 | 3 |
| 12/11/2021 | 7 |
| 12/31/2021 | 7 |
| 1/17/2022 | 6 |
| 1/30/2022 | 11 |
| 1/31/2022 | 3 |
| 4/17/2022 | 5 |
Table 1: Distribution of domain creation dates over time
Associated domains have SSL certificates with the subject alternative name of q74243532.myqnapcloud.com, which points out the underlying QNAP cloud infra. Also, their URL scan results return login pages to QTS service of QNAP:
Once the payload is downloaded, it is executed through various system binaries. First, rundll32.exe uses the ShellExec_RunDLL function from shell32.dll to leverage system binaries such as msiexec.exe, odbcconf.exe, or control.exe. These binaries are used to execute the payload stored in C:\ProgramData\[3 chars]\
It is followed by the execution of fodhelper.exe, which has the auto elevated bit set to true. It is often leveraged by adversaries in order to bypass User Account Control and execute additional commands with escalated privileges [3]. To monitor suspicious executions of fodhelper.exe, we suggest monitoring its instances without any command line arguments.
Raspberry Robin sets up its C2 channel through the additional execution of system binaries without any command line argument, which is quite unusual. That likely points to process injection given elevated privileges in previous steps of execution. It uses dllhost.exe, rundll32.exe, and regsvr32.exe to set up a TOR connection.
In Cisco Global Threat Alerts available through Cisco Secure Network Analytics and Cisco Secure Endpoint, we track this activity under the Raspberry Robin threat object. Image 3 shows a detection sample of Raspberry Robin:
Raspberry Robin tries to remain undetected through its use of system binaries, mixed letter case, TOR-based C2, and abuse of compromised QNAP accounts. Although we have similar intelligence gaps (how it infects external disks, what are its actions on objective) like our peers, we are continuously observing its activities.
| Type | Stage | IOC |
| Domain | Payload Delivery | k6j[.]pw |
| Domain | Payload Delivery | kjaj[.]top |
| Domain | Payload Delivery | v0[.]cx |
| Domain | Payload Delivery | zk4[.]me |
| Domain | Payload Delivery | zk5[.]co |
| Domain | Payload Delivery | 0dz[.]me |
| Domain | Payload Delivery | 0e[.]si |
| Domain | Payload Delivery | 5qw[.]pw |
| Domain | Payload Delivery | 6w[.]re |
| Domain | Payload Delivery | 6xj[.]xyz |
| Domain | Payload Delivery | aij[.]hk |
| Domain | Payload Delivery | b9[.]pm |
| Domain | Payload Delivery | glnj[.]nl |
| Domain | Payload Delivery | j4r[.]xyz |
| Domain | Payload Delivery | j68[.]info |
| Domain | Payload Delivery | j8[.]si |
| Domain | Payload Delivery | jjl[.]one |
| Domain | Payload Delivery | jzm[.]pw |
| Domain | Payload Delivery | k6c[.]org |
| Domain | Payload Delivery | kj1[.]xyz |
| Domain | Payload Delivery | kr4[.]xyz |
| Domain | Payload Delivery | l9b[.]org |
| Domain | Payload Delivery | lwip[.]re |
| Domain | Payload Delivery | mzjc[.]is |
| Domain | Payload Delivery | nt3[.]xyz |
| Domain | Payload Delivery | qmpo[.]art |
| Domain | Payload Delivery | tiua[.]uk |
| Domain | Payload Delivery | vn6[.]co |
| Domain | Payload Delivery | z7s[.]org |
| Domain | Payload Delivery | k5x[.]xyz |
| Domain | Payload Delivery | 6Y[.]rE |
| Domain | Payload Delivery | doem[.]Re |
| Domain | Payload Delivery | bpyo[.]IN |
| Domain | Payload Delivery | l5k[.]xYZ |
| Domain | Payload Delivery | uQW[.]fUTbOL |
| Domain | Payload Delivery | t7[.]Nz |
| Domain | Payload Delivery | 0t[.]yT |
This article is part of a series in which we will explore several features, principles, and the building blocks of a security detection engine within an extended detection and response (XDR) solution.
In this second installment, we will look at ways of structuring the presentation of machine-generated alerts, so that each alert offers a cohesive and compelling narrative, as if written by a human analyst, at scale and in realtime.
In cyber security, we are used to two types of stories.
The first story is common for reports written by humans. It contains sections such as “impact,” “reproduction,” and “remediation” to help us understand what is at stake and what we need to fix. For example:
IMPACT: An SSH server which supports password authentication is susceptible to brute-forcing attacks.
REPRODUCTION: Use the `ssh` command in verbose mode (`ssh -v`) to determine supported authentication methods. Look for “keyboard-interactive” and “password” methods.
REMEDIATION: Disable unneeded authentication methods.
The second story comes from machine detections. It is much terser in content and sometimes leaves us scratching our heads. “Malware,” the machine says with little explanation, followed by a horde of gibberish-looking data of network flows, executable traces, and so on.
The challenge is now to get the best of both worlds: to enhance machine-generated alerts with the richness of human-written reports. The following sections explain how this can be approached.
In our example of a report written by a human, the “reproduction” section would help us understand, from a factual perspective, how exactly the conclusions were derived.
On the other hand, the machine-generated horde of data provides evidence in a very nondescript way. We would need to be smart enough to spot or reverse-engineer what algorithm the machine was following on said data. Most security analysts do not wish to do this. Instead, they attempt to seek the first story type. “Surely, someone must have written a blog or something more descriptive about this already,” they would say. Then, they would copy-paste anything that looks like a searchable term – an IP address, domain, SHA checksum – and start searching it, either on a threat intelligence search site or even a general-purpose search engine.
Having such cryptic machine-generated alerts is leading us to our first two issues: first, when the story is incomplete or misunderstood, it may lead the analyst astray. For example, the security event might involve requests to communicate with an IP address, and the analyst would say, “This IP address belongs to my DNS server, so the traffic is legitimate.” However, the detection engine was really saying, “I suspect there is DNS tunnelling activity happening through your DNS server—just look at the volume.”
Second, when an analyst seeks explanations from elsewhere, the main function of an advanced detection engine — finding novel, localized, and targeted attacks — cannot work. Information on attacks is generally available only after they have been discovered and analyzed, not when they happen initially.
A common approach to remedy this situation is to include a short description of the algorithm. “This detector works by maintaining a baseline of when during the day a user is active and then reports any deviations,” a help dialog would say. “Okay, that’s clever,” an analyst would reply. But this is not enough. “Wait, what is the baseline, and how was it violated in this particular security event?” To find the answer, we need to go back to the horde of data.
To mimic the “reproduction” section of the human-written report, our security events are enriched with an annotation—a short summary of the behavior described by the event. Here are a few examples of such annotated events:
In the first and second cases, the story is relatively straightforward: in the horde of data, successful communication with said hostnames was observed. An inference through threat intelligence associates these hostnames to the Sality malware.
The third line informs us that, on a factual basis, only a communication with an IP address was observed. Further chain of inferences is that this IP address was associated by a passive DNS mechanism to a hostname which is in turn associated to the Sality malware.
In the fourth event, we have an observation of full HTTP URL requests, and inference through a pattern matcher associates this URL to the Sality malware. In this case, neither the hostname nor the IP address is important to the detector.
In all these annotated events, an analyst can easily grasp the factual circumstances and what the detection engine infers and thinks about the observations. Note that whether these events describe benign, malicious, relevant, or irrelevant behavior, or whether they lead to true or false positives, is not necessarily the concern. The concern is to be specific about the circumstances of the observed behavior and to be transparent about the inferences.
When we eventually succeed in explaining the security events, we might not be finished with the storytelling yet. The analyst would face another dilemma. They would ask: “What relevance does this event have in my environment? Is it part of an attack, an attack technique perhaps? What should I look for next?”
In the human-written report, the “impact” section provides a translation between the fact-based technical language of “how” and the business language of “what.” In this business language, we talk about threats, risks, attacker objectives, their progress, and so on.
This translation is an important part of the story. In our previous example about DNS tunnelling, we might want to express that “an anomaly in DNS traffic is a sign of an attacker communicating with their command-and-control infrastructure,” or that “it is a sign of exfiltration,” or perhaps both. The connotation is that both techniques are post-infection, and that there is probably already a foothold that the attacker has established. Perhaps other security events point to this, or perhaps it needs to be sought after by the analyst.
When it is not explicit, the analyst needs to mentally perform the translation. Again, an analyst might look up some intelligence in external sources and incorrectly interpret the detection engine’s message. Instead, they might conclude that “an anomaly in DNS traffic is a policy violation, user error, or reconnaissance activity,” leading them astray from pivoting and searching for the endpoint foothold that performs the command-and-control activity.
We take special attention not to mix these two different dictionaries. Rather, we express separately the factual observations versus the conclusions in the form of threats and risks. Inbetween, there are the various chains of inferences. Based on the complexity, the depth of the story varies, but the beginning and the end will always be there: facts versus conclusions.
This is very similar to how an analyst would set up their investigation board to organize what they know about the case. Here is an elaborate example:
In this case, from top to bottom:
In all points, the “what” and “how” languages are distinguished from each other. Finally, the whole story is stitched together into one alert by using the alert fusion algorithm described in the Intelligent alert management blog post.
Have we bridged the storytelling gap between machine-generated and human-generated reports?
Threat detections need to be narrated in sufficient detail, so that our users can understand them. Previously, we relied on the human aspect—we would need to document, provide support, and even reverse-engineer what the detection algorithms said.
The two solutions, distinguishing the “what/how” languages and the annotated events, provide the bandwidth to transmit the details and the expert knowledge directly from the detection algorithms. Our stories are now rich with detail and are built automatically in real time.
The result allows for quick orientation in complex detections and lowers the time to triage. It also helps to correctly convey the message, from our team, through the detection engine, and towards the analyst, lowering the possibility of misinterpretation.
This capability is part of Cisco Global Threat Alerts, currently available within Cisco Secure Network Analytics and Cisco Secure Endpoint, and has been continually improved based on customer feedback. In the future, it will also be available in Cisco SecureX XDR.
We’d love to hear what you think. Ask a Question, Comment Below, and Stay Connected with Cisco Secure on social!
Cisco Secure Social Channels
Below research is reflecting our observations during month of March 2022. We also would like to thank Maria Jose Erquiaga for her contribution in introduction and support during the process of writing.
As the Russian-Ukrainian war continues over conventional warfare, cybersecurity professionals witnessed their domain turning into a real frontier. Threat actors picking sides [1], group members turning against each other [2], some people handing out DDoS tools [3], some people blending in to turn it into profit [4], and many other stories, proving that this new frontier is changing daily, and its direct impact is not limited to geographical boundaries.
While attacks seem to be evolving daily, it is challenging for one to stay up to date with all that is going around. Therefore, we believe that it is important to distinguish between information and actionable intelligence. In Cisco Global Threat Alerts, we would like to share our observations related to this conflict during March of 2022 and discover how we can turn them into actionable intelligence together.
Since the rapid escalation of the conflict in 2022, security researchers and analysts have been gathering information regarding the adversarial groups, malware, techniques, and types of attacks implemented [1, 5, 6]. Some of the groups and malware related to the conflict are described in Table 1:
| Threat Actor | Malware | Location |
| Gamaredon [7] | Pteranodon [8] | Crimea |
| Sandworm [9] | CyclopsBlink [10] | Russia |
| WizardSpider [11] | Cobalt Strike [12], Emotet [13], Conti [14], Ryuk [15], Trickbot [16] | Russia |
Table 1: Threat actors and their relations
Gamaredon group, also known as Primitive Bear, Shuckworm and ACTINIUM, is an advanced persistent threat (APT) based in Russia. Their activities can be traced back as early as 2013, prior to Russia’s annexation of the Crimean Peninsula. They are known to target state institutions of Ukraine and western government entities located in Ukraine. Ukrainian officials attribute them to Russian Federal Security Service, also known as FSB [17].
Gamaredon often leverages malicious office files, distributed through spear phishing as the first stage of their attacks. They are known to use a PowerShell beacon called PowerPunch to download and execute malware for ensuing stages of attacks. Pterodo and QuietSieve are popular malware families that they deploy for stealing information and various actions on objective [18].
We were able to collect network IoC’s related to Gamaredon infrastructure. During our initial analysis, most of the indicators were not attributed directly to any specific malware and they were rather listed as part of Gamaredon’s infrastructure. Therefore, we wanted to analyze their infrastructure to understand their arsenal and deployment in greater detail.
The first part of this research is focused on WHOIS record analysis. We observed that Gamaredon domains were dominantly registered by REG[.]RU. Creation dates are going back as early as February 2019 and have a changing pattern for the registrant email. Until August 2020, we observed that message-yandex.ru@mail[.]ru was the main registrant email. Later, it shifted to macrobit@inbox[.]ru, mixed with the occasional usage of message-yandex.ru@mail[.]ru and tank-bank15@yandex[.]ru. Domain creation dates in some of the WHOIS records are as recent as March 2022.
Other than WHOIS information, the domains we observed that were related to Gamaredon campaigns had a distinguishing naming convention. While dataset consisted of domain names (without TLDs) varying between 4 to 16 characters, 70% percent of them were between 7 to 10 characters. Combined with a limited group of top-level domains (TLDs) used (see Table 2), this leads us to a naming pattern for further attribution. Additionally, the usage of TLDs on domain creation seems to be rotating.
| TLD | Distribution | TLD Usage |
| online | 42.07% | 08/2020-02/2021,02/2022 |
| xyz | 29.47% | 06/2022-08/2022, 02/2022-03/2022 |
| ru | 14.22% | 08/2020, 05/2021-02/2022 |
| site | 8.94% | 07/2020-02/2021 |
| space | 2.64% | 02/2019-06/2020 |
Table 2: TLD distribution and time in use
In the case of domain resolutions, we aimed to analyze the distribution of autonomous system numbers (ASN) used by resolved IP addresses (see Table 3). Once more, the owner REG[.]RU is leading the list, owning most of the domains. TimeWeb was the second this time, with 28% of the domains we found to be related to Gamaredon activities. Domains having ‘. online’ and ‘.ru’ TLDs are regularly updating their IP resolutions, almost daily.
| Owner | ASN | Popular Networks | Distribution |
| REG.RU, Ltd | AS197695 | 194.67.71.0/24 194.67.112.0/24 194.58.100.0/24 194.58.112.0/24 194.58.92.0/24 89.108.81.0/24 |
45.93% |
| TimeWeb Ltd. | AS9123 | 185.104.114.0/24 188.225.77.0/24 188.225.82.0/24 94.228.120.0/24 94.228.123.0/24 |
28.25% |
| EuroByte LLC | AS210079 | 95.183.12.42/32 | 10.56% |
| AS-CHOOPA | AS20473 | 139.180.196.149/32 | 5.08% |
| LLC Baxet | AS51659 | 45.135.134.139/32 91.229.91.124/32 |
2.23% |
| System Service Ltd. | AS50448 | 109.95.211.0/24 | 1.82% |
Table 3: Distribution of IP addresses per ASN and owner
After understanding the infrastructure, let’s proceed with their arsenal. We looked at associated file samples for the domains through Umbrella and Virustotal. A sample of the results can be seen below. Referring to a file type, we can see that the Gamaredon group prefers malicious office documents with macros. Also, they are known to use Pterodo, which is a constantly evolving custom backdoor [8, 18].
| Domain | Hash | Type | Malware |
| acetica[.]online | 4c12713ef851e277a66d985f666ac68e73ae21a82d8dcfcedf781c935d640f52 | Office Open XML Document | Groooboor |
| arvensis[.]xyz | 03220baa1eb0ad80808a682543ba1da0ec5d56bf48391a268ba55ff3ba848d2f | Office Open XML Document | Groooboor |
| email-smtp[.]online | 404ed6164154e8fb7fdd654050305cf02835d169c75213c5333254119fc51a83 | Office Open XML Document | Groooboor |
| gurmou[.]site | f9a1d7e896498074f7f3321f1599bd12bdf39222746b756406de4e499afbc86b | Office Open XML Document | Groooboor |
| mail-check[.]ru | 41b7a58d0d663afcdb45ed2706b5b39e1c772efd9314f6c1d1ac015468ea82f4 | Office Open XML Document | Groooboor |
| office360-expert[.]online | 611e4b4e3fd15a1694a77555d858fced1b66ff106323eed58b11af2ae663a608 | Office Open XML Document | Groooboor |
| achilleas[.]xyz | f021b79168daef8a6359b0b14c0002316e9a98dc79f0bf27e59c48032ef21c3d | Office Open XML Document | Macro enabled Word Trojan |
| anisoptera[.]online | 8c6a3df1398677c85a6e11982d99a31013486a9c56452b29fc4e3fc8927030ad | MS Word Document | Macro enabled Word Trojan |
| erythrocephala[.]online | 4acfb73e121a49c20423a6d72c75614b438ec53ca6f84173a6a27d52f0466573 | Office Open XML Document | Macro enabled Word Trojan |
| hamadryas[.]online | 9b6d89ad4e35ffca32c4f44b75c9cc5dd080fd4ce00a117999c9ad8e231d4418 | Office Open XML Document | Macro enabled Word Trojan |
| intumescere[.]online | 436d2e6da753648cbf7b6b13f0dc855adf51c014e6a778ce1901f2e69bd16360 | MS Word Document | Macro enabled Word Trojan |
| limosa[.]online | 0b525e66587e564db10bb814495aefb5884d74745297f33503d32b1fec78343f | MS Word Document | Macro enabled Word Trojan |
| mesant[.]online | 936b70e0babe7708eda22055db6021aed965083d5bc18aad36bedca993d1442a | MS Word Document | Macro enabled Word Trojan |
| sufflari[.]online | 13b780800c94410b3d68060030b5ff62e9a320a71c02963603ae65abbf150d36 | MS Word Document | Macro enabled Word Trojan |
| apusa[.]xyz | 23d417cd0d3dc0517adb49b10ef11d53e173ae7b427dbb6a7ddf45180056c029 | Win32 DLL | Pterodo |
| atlanticos[.]site | f5023effc40e6fbb5415bc0bb0aa572a9cf4020dd59b2003a1ad03d356179aa1 | VBA | Pterodo |
| barbatus[.]online | 250bd134a910605b1c4daf212e19b5e1a50eb761a566fffed774b6138e463bbc | VBA | Pterodo |
| bitsadmin2[.]space | cfa58e51ad5ce505480bfc3009fc4f16b900de7b5c78fdd2c6d6c420e0096f6b | Win32 EXE | Pterodo |
| bitsadmin3[.]space | 9c8def2c9d2478be94fba8f77abd3b361d01b9a37cb866a994e76abeb0bf971f | Win32 EXE | Pterodo |
| bonitol[.]online | 3cbe7d544ef4c8ff8e5c1e101dbdf5316d0cfbe32658d8b9209f922309162bcf | VBA | Pterodo |
| buhse[.]xyz | aa566eed1cbb86dab04e170f71213a885832a58737fcab76be63e55f9c60b492 | Office Open XML Document | Pterodo |
| calendas[.]ru | 17b278045a8814170e06d7532e17b831bede8d968ee1a562ca2e9e9b9634c286 | Win32 EXE | Pterodo |
| coagula[.]online | c3eb8cf3171aa004ea374db410a810e67b3b1e78382d9090ef9426afde276d0f | MS Word Document | Pterodo |
| corolain[.]ru | 418aacdb3bbe391a1bcb34050081bd456c3f027892f1a944db4c4a74475d0f82 | Win32 EXE | Pterodo |
| gorigan[.]ru | 1c7804155248e2596ec9de97e5cddcddbafbb5c6d066d972bad051f81bbde5c4 | Win32 EXE | Pterodo |
| gorimana[.]site | 90cb5319d7b5bb899b1aa684172942f749755bb998de3a63b2bccb51449d1273 | MS Word Document | Pterodo |
| krashand[.]ru | 11d6a641f8eeb76ae734951383b39592bc1ad3c543486dcef772c14a260a840a | Win32 EXE | Pterodo |
| libellus[.]ru | 4943ca6ffef366386b5bdc39ea28ad0f60180a54241cf1bee97637e5e552c9a3 | Win32 EXE | Pterodo |
| melitaeas[.]online | 55ad79508f6ccd5015f569ce8c8fcad6f10b1aed930be08ba6c36b2ef1a9fac6 | Office Open XML Document | Pterodo |
| mullus[.]online | 31afda4abdc26d379b848d214c8cbd0b7dc4d62a062723511a98953bebe8cbfc | Win32 EXE | Pterodo |
| upload-dt[.]hopto[.]org | 4e72fbc5a8c9be5f3ebe56fed9f613cfa5885958c659a2370f0f908703b0fab7 | MS Word Document | Pterodo |
Table 4: Domains, files (hash and type), and malware name associated to the Gamaredon group
After reviewing the behaviors of the associated malicious samples, it is easier to build attribution between the malicious domain and the corresponding sample. IP addresses resolved by the domain are later used to establish raw IP command and control (C2) communication with a distinguishing URL pattern. The following example shows how 1c7804155248e2596ec9de97e5cddcddbafbb5c6d066d972bad051f81bbde5c4 resolves gorigan[.]ru and uses its IP address to build a C2 URL (http|https<IP>/<random alphanumerical string>). Therefore, DNS and outgoing web traffic is crucial for its detection.
In Cisco Global Threat Alerts, we are tracking the Gamaredon group under the Gamaredon Activity threat object. The threat description is enriched with MITRE references (see Figure 3).
Figure 4 shows a detection sample of Gamaredon activity. Observe that the infected device attempted to communicate with the domains alacritas[.]ru, goloser[.]ru, and libellus[.]ru, which seemed to be sinkholed to the OpenDNS IP address of 146.112.61.[.]107.
We’ve walked through the steps of producing intelligence from information we’ve collected. We began our analysis with an unattributed list of network IoC’s and were able to identify unique patterns in their metadata. Then, we pivoted to endpoint IoC’s and attributed domains to malware families. Next, we showed how we turned it into a detection of the Gamaredon group displayed in the Cisco Global Threat Alerts portal.
For your convenience, here’s a summary of the intelligence we developed in this blog post:
| Aliases | Primitive Bear, Shuckworm, ACTINIUM |
| Type | Threat Actor |
| Originating From | Russia |
| Targets | Ukranian State Organizations |
| Malware used | Pterodo, Groooboor |
| File Type | Macro enabled office files, Win32 Exe, VBA |
| TLD’s used | .online, .xyz, .ru, .site, .space |
| ASN’s used | REG.RU, Ltd, TimeWeb Ltd., EuroByte LLC, AS-CHOOPA, LLC Baxet, System Service Ltd. |
[1] Cyber Group Tracker: https://cyberknow.medium.com/update-10-2022-russia-ukraine-war-cyber-group-tracker-march-20-d667afd5afff
[2] Conti ransomware’s internal chats leaked after siding with Russia: https://www.bleepingcomputer.com/news/security/conti-ransomwares-internal-chats-leaked-after-siding-with-russia/
[3] Hackers sound call to arms with digital weapon aimed at Russian websites: https://cybernews.com/news/hackers-sound-call-to-arms-with-digital-weapon-aimed-at-russian-websites/
[4] Threat advisory: Cybercriminals compromise users with malware disguised as pro-Ukraine cyber tools: https://blog.talosintelligence.com/2022/03/threat-advisory-cybercriminals.html
[5] Ukraine-Cyber-Operations: https://github.com/curated-intel/Ukraine-Cyber-Operations
[6] What You Need to Know About Russian Cyber Escalation in Ukraine: https://socradar.io/what-you-need-to-know-about-russian-cyber-escalation-in-ukraine/
[7] Gamaredon: https://attack.mitre.org/groups/G0047/
[8] Pteranodon: https://attack.mitre.org/software/S0147/
[9] Sandworm: https://attack.mitre.org/groups/G0034/
[10] Threat Advisory: Cyclops Blink: https://blog.talosintelligence.com/2022/02/threat-advisory-cyclops-blink.html
[11] Wizard Spider: https://attack.mitre.org/groups/G0102/
[12] Cobalt Strike: https://attack.mitre.org/software/S0154
[13] Emotet: https://attack.mitre.org/software/S0367
[14] Conti: https://attack.mitre.org/software/S0575
[15] Ryuk: https://attack.mitre.org/software/S0446
[16] TrickBot: https://attack.mitre.org/software/S0446
[17] Technical Report Gamaredon/Armageddon group: https://ssu.gov.ua/uploads/files/DKIB/Technical%20report%20Armagedon.pdf
[18] ACTINIUM targets Ukrainian organizations: https://www.microsoft.com/security/blog/2022/02/04/actinium-targets-ukrainian-organizations/
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