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April 26, 2023

Gozi ISFB Malware Detection Insights and Analysis

Uncover how Gozi ISFB operates and how Darktrace’s detection capabilities help secure your systems against this versatile malware.
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Justin Torres
Cyber Analyst
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26
Apr 2023

Mirroring the overall growth of the cybersecurity landscape and the advancement of security tool capabilities, threat actors are continuously forced to keep pace. Today, threat actors are bringing novel malware into the wild, creating new attack vectors, and finding ways to avoid the detection of security tools. 

One notable example of a constantly adapting type of malware can be seen with banking trojans, a type of malware designed to steal confidential information, such as banking credentials, used by attackers for financial gain. Gozi-ISFB is a widespread banking trojan that has previously been referred to as ‘the malware with a thousand faces’ and, as it name might suggest, has been known under various names such as Gozi, Ursnif, Papras and Rovnix to list a few.

Between November 2022 and January 2023, a rise in Gozi-ISFB malware related activity was observed across Darktrace customer environments and was investigated by the Darktrace Threat Research team. Leveraging its Self-Learning AI, Darktrace was able to identify activity related to this banking trojan, regardless of the attack vectors or delivery methods utilized by threat actors.

We have moderate to high confidence that the series of activities observed is associated with Gozi-ISFB malware and high confidence in the indicators of compromise identified which are related to the post-compromise activities from Gozi-ISFB malware. 

Gozi-ISFB Background

The Gozi-ISFB malware was first observed in 2011, stemming from the source code of another family of malware, Gozi v1, which in turn borrowed source code from the Ursnif malware strain.  

Typically, the initial access payloads of Gozi-ISFB would require an endpoint to enable a macro on their device, subsequently allowing a pre-compiled executable file (.exe) to be gathered from an attacker-controlled server, and later executed on the target device.

However, researchers have recently observed Gozi-ISFB actors using additional and more advanced capabilities to gain access to organizations networks. These capabilities range from credential harvest, surveilling user keystrokes, diverting browser traffic from banking websites, remote desktop access, and the use of domain generation algorithms (DGA) to create command-and-control (C2) domains to avoid the detection and blocking of traditional security tools. 

Ultimately, the goal of Gozi-ISFB malware is to gather confidential information from infected devices by connecting to C2 servers and installing additional malware modules on the network. 

Darktrace Coverage of Gozi-ISFB 

Unlike traditional security approaches, Darktrace DETECT/Network™ can identify malicious activity because Darktrace models build an understanding of a device’s usual pattern of behavior, rather than using a static list of indicators of compromise (IoCs) or rules and signatures. As such, Darktrace is able to instantly detect compromised devices that deviate from their expected behavioral patterns, engaging in activity such as unusual SMB connections or connecting to newly created malicious endpoints or C2 infrastructure. In the event that Darktrace detects malicious activity, it would automatically trigger an alert, notifying the customer of an ongoing security concern. 

Regarding the Gozi-ISFB attack process, initial attack vectors commonly include targeted phishing campaigns, where the recipient would receive an email with an attached Microsoft Office document containing macros or a Zip archive file. Darktrace frequently observes malicious emails like this across the customer base and is able to autonomously detect and action them using Darktrace/Email™. In the following cases, the clients who had Darktrace/Email did not have evidence of compromise through their corporate email infrastructure, suggesting devices were likely compromised via the access of personal email accounts. In other cases, the customers did not have Darktrace/Email enabled on their networks.

Upon downloading and opening the malicious attachment included in the phishing email, the payload subsequently downloads an additional .exe or dynamic link library (DLL) onto the device. Following this download, the malware will ultimately begin to collect sensitive data from the infected device, before exfiltrating it to the C2 server associated with Gozi-ISFB. Darktrace was able to demonstrate and detect the retrieval of Gozi-ISFB malware, as well as subsequent malicious communication on multiple customer environments. 

In some attack chains observed, the infected device made SMB connections to the rare external endpoint ’62.173.138[.]28’ via port 445. Darktrace recognized that the device used unusual credentials for this destination endpoint and further identified it performing SMB reads on the share ‘\\62.173.138[.]28\Agenzia’. Darktrace also observed that the device downloaded the executable file ‘entrat.exe’ from this connection as can be seen in Figure 1.

Figure 1: Model breach event log showing an infected device making SMB read actions on the share ‘\\62.173.138[.]28\Agenzia’. Darktrace observed the device downloading the executable file ‘entrat.exe’ from this connection.

Subsequently, the device performed a separate SMB login to the same external endpoint using a credential identical to the device's name. Shortly after, the device performed a SMB directory query from the root share drive for the file path to the same endpoint. 

Figure 2:SMB directory query from the root share drive for the file path to the same endpoint, ’62.173.138[.]28’.

In Gozi-ISFB compromises investigated by the Threat Research team, Darktrace commonly observed model breaches for ‘Multiple HTTP POSTs to Rare Hostname’ and the use of the Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 10.0; Win64; x64)’ user agent. 

Devices were additionally observed making external connections over port 80 (TCP, HTTP) to endpoints associated with Gozi-ISFB. Regarding these connections, C2 communication was observed used configurations of URI path and resource file extension that claimed to be related to images within connections that were actually GET or POST request URIs. This is a commonly used tactic by threat actors to go under the radar and evade the detection of security teams.  

An example of this type of masqueraded URI can be seen below:

In another similar example investigated by the Threat Research team, Darktrace detected similar external connectivity associated with Gozi-ISFB malware. In this case, DETECT identified external connections to two separate hostnames, namely ‘gameindikdowd[.]ru’ and ‘jhgfdlkjhaoiu[.]su’,  both of which have been associated to Gozi-ISFB by OSINT sources. This specific detection included HTTP beaconing connections to endpoint, gameindikdowd[.]ru .

Details observed from this event: 

Destination IP: 134.0.118[.]203

Destination port: 80

ASN: AS197695 Domain names registrar REG.RU, Ltd

User agent: Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 10.0; Win64; x64

The same device later made anomalous HTTP POST requests to a known Gozi-ISFB endpoint, jhgfdlkjhaoiu[.]su. 

Details observed:

Destination port: 80

ASN: AS197695 Domain names registrar REG.RU, Ltd

User agent: Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 10.0; Win64; x64

Figure 3: Packet Capture (PCAP) with the device conducting anomalous HTTP POST requests to a Gozi-ISFB related IOC, ‘jhgfdlkjhaoiu[.]su’.

Conclusions 

With constantly changing attack infrastructure and new methods of exploitation tested and leveraged hour upon hour, it is critical for security teams to employ tools that help them stay ahead of the curve to avoid critical damage from compromise.  

Faced with a notoriously adaptive malware strain like Gozi-ISFB, Darktrace demonstrated its ability to autonomously detect malicious activity based upon more than just known IoCs and attack vectors. Despite the multitude of different attack vectors utilized by threat actors, Darktrace was able to detect Gozi-ISFB activity at various stages of the kill chain using its anomaly-based detection to identify unusual activity or deviations from normal patterns of life. Using its Self-Learning AI, Darktrace successfully identified infected devices and brought them to the immediate attention of customer security teams, ultimately preventing infections from leading to further compromise.  

The Darktrace suite of products, including DETECT/Network, is uniquely placed to offer customers an unrivaled level of network security that can autonomously identify and respond to arising threats against their networks in real time, preventing suspicious activity from leading to damaging network compromises.

Credit to: Paul Jennings, Principal Analyst Consultant and the Threat Research Team

Appendices

List of IOCs

134.0.118[.]203 - IP Address - Gozi-ISFB C2 Endpoint

62.173.138[.]28 - IP Address - Gozi-ISFB  C2 Endpoint

45.130.147[.]89 - IP Address - Gozi-ISFB  C2 Endpoint

94.198.54[.]97 - IP Address - Gozi-ISFB C2 Endpoint

91.241.93[.]111 - IP Address - Gozi-ISFB  C2 Endpoint

89.108.76[.]56 - IP Address - Gozi-ISFB  C2 Endpoint

87.106.18[.]141 - IP Address - Gozi-ISFB  C2 Endpoint

35.205.61[.]67 - IP Address - Gozi-ISFB  C2 Endpoint

91.241.93[.]98 - IP Address - Gozi-ISFB  C2 Endpoint

62.173.147[.]64 - IP Address - Gozi-ISFB C2 Endpoint

146.70.113[.]161 - IP Address - Gozi-ISFB  C2 Endpoint 

iujdhsndjfks[.]ru - Hostname - Gozi-ISFB C2 Hostname

reggy505[.]ru - Hostname - Gozi-ISFB  C2 Hostname

apr[.]intoolkom[.]at - Hostname - Gozi-ISFB  C2 Hostname

jhgfdlkjhaoiu[.]su - Hostname - Gozi-ISFB  C2 Hostname

gameindikdowd[.]ru - Hostname - Gozi-ISFB  Hostname

chnkdgpopupser[.]at - Hostname – Gozi-ISFB C2 Hostname

denterdrigx[.]com - Hostname – Gozi-ISFB C2 Hostname

entrat.exe - Filename – Gozi-ISFB Related Filename

Darktrace Model Coverage

Anomalous Connection / Multiple HTTP POSTs to Rare Hostname

Anomalous Connection / Posting HTTP to IP Without Hostname

Anomalous Connection / New User Agent to IP Without Hostname

Compromise / Agent Beacon (Medium Period)

Anomalous File / Application File Read from Rare Endpoint

Device / Suspicious Domain

Mitre Attack and Mapping

Tactic: Application Layer Protocol: Web Protocols

Technique: T1071.001

Tactic: Drive-by Compromise

Technique: T1189

Tactic: Phishing: Spearphishing Link

Technique: T1566.002

Model Detection

Anomalous Connection / Multiple HTTP POSTs to Rare Hostname - T1071.001

Anomalous Connection / Posting HTTP to IP Without Hostname - T1071.001

Anomalous Connection / New User Agent to IP Without Hostname - T1071.001

Compromise / Agent Beacon (Medium Period) - T1071.001

Anomalous File / Application File Read from Rare Endpoint - N/A

Device / Suspicious Domain - T1189, T1566.002

References

https://threatfox.abuse.ch/browse/malware/win.isfb/

https://www.cisa.gov/news-events/cybersecurity-advisories/aa22-216a

https://www.fortinet.com/blog/threat-research/new-variant-of-ursnif-continuously-targeting-italy#:~:text=Ursnif%20(also%20known%20as%20Gozi,Italy%20over%20the%20past%20year

https://medium.com/csis-techblog/chapter-1-from-gozi-to-isfb-the-history-of-a-mythical-malware-family-82e592577fef

Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Justin Torres
Cyber Analyst

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June 22, 2026

Advancing the Use of Frontier AI in Cybersecurity: Darktrace Joins the OpenAI Daybreak Cyber Partner Program to Explore Defensive AI Integrations

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Darktrace joins the OpenAI Daybreak Cyber Partner Program

Today, we announced that Darktrace is joining the OpenAI Daybreak Cyber Partner Program. We’ll be partnering with OpenAI to explore how their cyber capabilities can be integrated within Darktrace products and services to bring new capabilities to our customers.

This partnership is an exciting opportunity to bring together Darktrace’s behavioral AI modelling of the organization with OpenAI’s advanced contextual capabilities to create a new level of understanding for security teams. To understand the impact, it’s helpful to start with how we think about the problem.  

At Darktrace, we built our AI in support of the core belief that cybersecurity needs to understand the business it is defending. That's why our Self-Learning AI is designed to help organizations understand normal and abnormal behavior for each organization across their digital environment, including users and identities, networks and cloud, email and collaboration tools, and now AI systems and agents with the rollout of Darktrace / SECURE AI™.  

Our goal was never simply to spot known attacks faster. It was to help defenders understand how their organization behaves, potential risks and impact, and where disruption could take hold so they could prepare for the unknown threats that they may not have seen or even imagined before.  

That’s exactly what is happening across the threat landscape today. Attacks keep changing; techniques shift, infrastructure evolves, and attackers move with more speed, precision, and context. And now they have even more AI and automation on their side. Attackers are exploiting identities, trusted services, SaaS applications, and business workflows. They are not always breaking in; often, the threat may come from within the organization in the form of insider threat or even rogue agents.  

In this reality, defenders need a combination of deep AI modelling of the organization and AI that can connect identified threats to concrete business context, translating this information into real world value, and allow action before risk becomes disruption.

That is the opportunity we see in partnering with OpenAI.  

What is the OpenAI Daybreak Cyber Partner Program and why is Darktrace joining

The OpenAI Daybreak Cyber Partner Program is focused on advancing the safe use of AI for cybersecurity. As part of the program’s next phase, OpenAI is working with a select group of trusted partners including Darktrace on scoped product integrations, managed services, and partner-delivered defensive capabilities. We’ll be exploring how OpenAI’s advanced frontier AI capabilities can support defenders in the tools and workflows they already use each day.

For Darktrace, this is a natural extension of our expertise and the work we have been doing for a decade: safely and securely applying the most effective AI techniques in combination to understand organizations, detecting malicious activity at the earliest indicators, and helping cyber defenders act faster.  

By using the advanced models and more precise safeguards available in the OpenAI Daybreak Cyber Partner Program, Darktrace and OpenAI will combine Darktrace’s real-time behavioral understanding of an organization's digital estate with OpenAI's ability to interpret wider business context.  

This is a unique and powerful combination of insights that could give organizations deeper context on technical risk and help them prioritize workloads and investigations based on potential impact to revenue, operations, and resilience. It can also provide security teams and executives with intelligence into which events matter most to the business, why they matter, and what action to take. Not just finding, for instance, that an agent is compromised, but highlighting that the compromised agent could shut down order fulfilment within the next three hours.  

Why the Darktrace and OpenAI partnership matters for defenders

Security teams today have more attack surface, more complex environments to protect, and an increasing volume of threats. The ability to act quickly is critical, but they also need to be able to focus on the risks that could have the greatest business impact.

That is especially important as attackers use AI to scale phishing, automate reconnaissance, find weaknesses, and blend into normal business activity. At the same time, organizations and their employees are using AI to innovate, which introduces an even broader attack surface and new set of risks. Defenders need AI that can operate across the same complexity, but safely, transparently, and in service of building more resilience. And they need a way to safely adopt, govern, and defend AI across their organizations.

Joining the OpenAI Daybreak Cyber Partner Program is another step in that direction. We are still early in this work, and we will take a careful, disciplined approach. But the direction is clear: protecting organizations requires AI that understands the business, not just the attack.

At Darktrace, that is exactly where we remain focused and why we are so excited about this partnership with OpenAI.  

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June 16, 2026

Hola VPN Abuse: From Proxy Traffic to Malware and Cryptomining

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Introduction

In enterprise environments, non-compliant software traffic can introduce unexpected exposure by creating unmanaged paths for outbound connectivity. Hola VPN is a notable example because of its peer-to-peer design, which can effectively turn user devices into routing or exit nodes for other parties’ traffic, shifting the risk profile from that of a traditional virtual private network (VPN) to something closer to a distributed proxy.

As a result, the appearance of Hola-related activity, whether from prior installation or unintended background connections, should be treated with caution.  Such activity may provide a foothold for malicious behavior, including lateral movement or command-and-control communication.

This blog explores how Hola-associated activity appeared as part of broader patterns of suspicious behavior observed across the Darktrace customer base.

The campaign

In February and March 2026, Darktrace observed similar anomalous activity across multiple customer environments, with affected devices showing consistent behavioral patterns. These included connections to multiple *.hola[.]org endpoints using Hola-related user agents, suggesting interaction with Hola infrastructure rather than isolated or incidental traffic.

Following these connections, affected customer environments showed downloads of suspicious executable files from rare external endpoints 188.241.219[.]55 and 184.241.218[.]111. Both endpoints have been flagged as potentially malicious by open-source intelligence (OSINT) [1][2].

These downloads were conducted using consistent user agents across impacted customers, specifically ‘Hola svc_js_win32/1.249.408’ and ‘Hola svc_js_win32/1.251.389’, suggesting a possible association with Hola-related activity.

Notably, this pattern aligns with recent reporting that, in some cases, Hola distributed an undeclared executable component, me[.]exe, which was later assessed to be a likely Monero-mining binary introduced via a compromised delivery pipeline [3].

Case Study 1

Darktrace first observed a new device on January 19, 2026, within a customer environment based in the Europe, Middle East, and Africa (EMEA) region. On the same day it appeared on the network, the device communicated with multiple pieces of Hola VPN-linked infrastructure before downloading a binary from a hola[.]org subdomain.

Cyber AI Analyst investigation highlighting Hola VPN service activity potentially associated with subsequent HTTP command-and-control (C2) connections.
Figure 1: Cyber AI Analyst investigation highlighting Hola VPN service activity potentially associated with subsequent HTTP command-and-control (C2) connections.

Subsequent Darktrace telemetry revealed a recurring pattern of activity from the day the device was first observed through to March 4, 2026. During this period, the device repeatedly issued HTTP GET requests to the URI /bwfile?size=1048576, each returning a 200 OK response, indicating successful file retrieval.

This behavior was accompanied by a POST request to /bwfile, followed by an additional GET request for a significantly larger file at /bwfile?size=26214400, suggesting a deliberate and structured file transfer pattern.

Notably, the binary download activity was not tied to a single static host. Instead, it was observed across multiple URLs that changed over time while remaining within the same hola[.]org domain. This pattern suggests the use of rotating or distributed delivery infrastructure rather than a fixed endpoint.

Variation in URLs over time within the same hola[.]org domain, indicating the use of dynamically changing endpoints.
Figure 2: Variation in URLs over time within the same hola[.]org domain, indicating the use of dynamically changing endpoints.

Across these events, the activity was consistently associated with the user agent Hola svc_js_win32/1.249.408, further linking the traffic to Hola-related service components. Amid these persistent and unusual connections, on February 22, Darktrace observed the device connecting to 188.241.219[.]55/proxy-peer-windows-amd64[.]exe, resulting in the download of an executable file.

 File transfer event showing the download of an executable  from the rare external endpoint 188.241.219[.]55.
Figure 3: File transfer event showing the download of an executable  from the rare external endpoint 188.241.219[.]55.

Based on its file hash, the downloaded file was assessed as a likely Trojan downloader [4], with import hash (imphash) values showing similarities to samples linked to Vidar, Rhadamanthys, and Stealc according to OSINT [5]. Overall, this sequence of activity suggests that Hola-related connectivity may have been leveraged as part of a broader malware delivery chain.

Darktrace’s Autonomous Response

Due to the highly unusual activity observed, Darktrace Autonomous Response was triggered by the device’s behavior. However, as the customer deployment was configured in “Human Confirmation” mode, manual approval was required before any action could be taken.

Had the deployment been set to “Fully Autonomous” mode, Darktrace would have automatically:

  1. Blocked connections to the associated ports and external endpoints
  2. Prevented all outgoing network connections from the device
  3. Enforced the device’s established ‘pattern of life’, allowing normal activity to continue while restricting any anomalous behavior
Figure 4: Example of a Darktrace Autonomous Response model highlighting the action that would have been taken, demonstrating how the system identifies anomalous behavior and applies targeted containment measures to restrict suspicious network activity.

Case Study 2

While the first case focused on anomalous activity from a newly observed device, Darktrace also identified cases in which devices had already been communicating with Hola-related endpoints prior to the suspected campaign. This may suggest pre-existing Hola usage within the environment, potentially increasing exposure and creating an avenue for subsequent suspicious activity.

One case involved three devices within a customer network based in the Americas (AMS). In this instance, a different payload was identified: me[.]exe, a potentially malicious cryptocurrency miner also referred to as HolaMonitorService[.]exe [6][7]. The downloads were observed from infrastructure similar to that seen in Case 1, including an IP address within the same 188.241.0.0/16 subnet.

Connections to *.hola[.]org, alongside the use of potential Hola-related user agents consistent with those in Case 1, were also identified, further suggesting a link between the observed activity and Hola-associated infrastructure.

Darktrace observed activity indicative of unusual VPN usage on the first affected device on February 2, followed by telemetry suggesting potential Tor usage. This was later followed by the download of me[.]exe on March 10 from 188.241.218[.]111. Notably, this device was the earliest among the three within the deployment to exhibit the presence of the suspicious executable.

Figure 5: Cyber AI Analyst detection highlighting the download of a suspicious executable from a similar external endpoint in a separate deployment.

On March 5, 2026, the second affected device exhibited a slightly different progression, initiating connections to http-test1[.]hola[.]org using the user agent ‘hola_get’. This activity was followed by the download of me[.]exe from the same endpoint on March 13, consistent with the broader pattern of Hola-related downloads observed across the environment.

 Example of Hola VPN-related connectivity observed on the network prior to the suspected campaign, indicating pre-existing usage that may have contributed to subsequent activity.
Figure 6: Example of Hola VPN-related connectivity observed on the network prior to the suspected campaign, indicating pre-existing usage that may have contributed to subsequent activity.

The final affected device within this customer’s network demonstrated a more limited but related pattern, also downloading me[.]exe on March 17 using the same ‘hola_get’ user agent.

While the earlier Hola VPN usage observed across the deployment may not have been directly related to the suspected malware campaign, it may nonetheless have contributed to reduced visibility. The presence of pre-existing Hola-related traffic could have obscured malicious activity, making it more difficult to distinguish legitimate usage from attacker-driven behavior and, in turn, hindering the timely identification of the emerging compromise.

Darktrace’s Autonomous Response

For this deployment, the customer had their Autonomous Response capability configured in “Fully Autonomous” mode, allowing Darktrace to take action without human intervention. As a result, the system was able to autonomously disrupt the activity as soon as relevant events were identified through model detections.

Figure 7: Darktrace Autonomous Response actions taken against suspicious activity linked to Hola VPN.

Suspected cryptomining activity

As previously noted, some of the observed executable payloads appear to be linked to cryptomining malware. Across a subset of affected customer environments, this assessment was further supported by subsequent device activity consistent with Monero mining. Affected devices established follow-on connections to multiple external endpoints aligned with known mining infrastructure, indicating post-download execution.

Considering the broader sequence of activity, this pattern may point to a wider form of abuse in which legitimate VPN-related traffic is used to mask or facilitate malicious behavior following compromise.

On several devices, the download of executable files, including a newly observed peer[.]exe, was followed by alerts indicative of cryptocurrency mining activity. Mining-related credentials such as ‘x’ were observed using the Minergate protocol to communicate with endpoints within the 89.125.255.0/24 subnet and 188.241.218[.]111, the same endpoint involved in earlier download activity. Additional credentials appeared to reflect device-specific CPU identifiers, for example ‘12th Gen Intel(R) Core (TM) i5-1235U’.

Observed mining methods included login, submit, and job, consistent with active participation in a pool-based mining workflow rather than passive or incidental contact. The login method indicates that the host authenticated to the mining service as a worker, job reflects the assignment of computational tasks, and submit shows completed work being returned to the pool [8]. This sequence suggests that affected devices were actively contributing processing resources as part of an unauthorized distributed mining operation.

The presence of unauthorized cryptominers can lead to degraded system performance and reduced device stability. Beyond the immediate resource impact, such activity often serves as an indicator of a broader compromise rather than an isolated issue. This may increase the risk of further malware deployment, persistence mechanisms, and lateral movement, particularly in environments where the initial intrusion has not been fully contained.

Conclusion

Across affected environments, detections such as unusual VPN usage, connections to Hola infrastructure, anomalous HTTP activity, suspicious file downloads, and subsequent cryptomining behavior were linked into a single, evolving incident narrative. This aggregation provided a clearer view of attack progression, enabling security teams to understand not just isolated alerts, but the full sequence of compromise from initial contact through to post-exploitation.

Ultimately, these activities show that the risk posed by non-compliant software such as Hola VPN can extend far beyond simple policy violations. What began as traffic to Hola-related infrastructure was, in multiple cases, followed by behavior suggesting deliberate misuse, including suspicious executable downloads using Hola-related user agents and, in some instances, evidence of active cryptomining. These were not isolated anomalies, but elements of a broader pattern in which seemingly benign proxy or VPN-related communications may have created a pathway for malicious delivery and unauthorized resource exploitation.

The significance of this activity lies not only in the downloads or mining, but in what it reveals about an attacker’s ability to blend malicious operations into traffic associated with software that may already have a foothold in the environment. When unapproved software operates within an enterprise, it can reduce visibility, blur the distinction between legitimate and malicious traffic, and create opportunities to extend compromise in ways that are persistent and difficult to detect. Darktrace’s anomaly-based approach enables these behavioral distinctions to be identified, regardless of whether the device is new or long established within the network.

Credit to Min Kim (Associate Principal Analyst), Priya Thapa (Senior Cyber Analyst)
Edited by Ryan Traill (Content Manager)

Appendices

References

[1] https://www.virustotal.com/gui/ip-address/188.241.219.55

[2]  https://www.virustotal.com/gui/ip-address/188.241.218.111

[3] https://www.sophos.com/en-us/blog/you-do-surprise-me-exe-an-unexpected-executable-in-hola-browser

[4] https://www.virustotal.com/gui/file/d275abca286cd75af971d0459fdf1df37c7b19c514abafae5d0b04bf42ccfb45/detection

[5] https://bazaar.abuse.ch/sample/d275abca286cd75af971d0459fdf1df37c7b19c514abafae5d0b04bf42ccfb45/

[6] https://any.run/report/4cdeb5df217764a8b6a20d518b76ccb30cbe623365a13d9dcd40900950f1ed99/de3a756a-3101-4369-8922-52c586c939fb

[7] https://www.virustotal.com/gui/file/e3541caf708c075f0bb22fc68b03acd8457fea7cf0732ea935b1eb016d1c7721/community

[8] https://bitcoinwiki.org/wiki/stratum

Darktrace Model Detections

·      Anomalous File / EXE from Rare External Location

·      Anomalous File / Multiple EXE from Rare External Locations

·      Compromise / Crypto Currency Mining Activity

·      Compromise / High Priority Crypto Currency Mining (EM)

·      Device / New User Agent

·      Anomalous Connection / New User Agent to IP Without Hostname

·      Antigena / Network / Significant Anomaly / Antigena Controlled and Model Alert

·      Antigena / Network / Significant Anomaly / Antigena Alerts Over Time Block

·      Antigena / Network / External Threat / Antigena Tor Block

·      Antigena / Network / External Threat / Antigena File then New Outbound Block

·      Antigena / Network / External Threat / Antigena Suspicious Activity Block

·      Antigena / Network / External Threat / Antigena Suspicious File Pattern of Life Block

·      Antigena / Network / External threat / Antigena Suspicious File Block

Indicators of Compromise (IoCs)

IoC –Type -Description + Confidence

188.241.219[.]55 - IP Address - Malware distribution source

188.241.218[.]111 - IP Address -Malware distribution source

hxxp://188.241.218[.]111:8080/me[.]exe - URI - Malicious payload

hxxp://188.241.219[.]55:9000/proxy-peer-windows-amd64[.]exe - URI - Malicious payload

hxxp://188.241.219[.]55:9000/peer[.]exe - URI - Malicious payload

C8088f3c8bc3542eb1ad78a7cc5306d866c8ac81 - SHA1 - Malicious payload, me[.]exe

b595a6de0f6a18975b29e6f8ebe604956a173478 - SHA1 - Malicious payload, me[.]exe

e9139a2e0839e8b9e5c9787ea936347ae56e5460 - SHA1 - Possible malicious payload

c2e80073e4cafe757d5643bd8fd45f28ad89bff9 - SHA1 - Possible malicious payload

695355eceedcdd337d8fcbd35e6a531cda75b847 - SHA1 - Possible malicious payload

f0b0d8068a1b9ab5d68a8a46842d72b870b292e7 - SHA1 - Possible malicious payload

a21c8b8cabc7670ea45bc175e185a0f9bfcf4733 - SHA1 - Malicious payload, me[.]exe

0353ca44b9f397d8f492db0b2f7a1d00a9e4406a - SHA1 - Possible malicious payload

56824c8a110e35ab303dc27a6c758cd50c36174c - SHA1 - Malicious payload, peer[.]exe

c141fa0fa505fe7f9ad5dd21d9d4d6d411739682 - SHA1 - Malicious payload, peer[.]exe

0417ec988b16f1267065185a6eea98f0bd2e17cd - SHA1 - Possible malicious payload

c54f7eaaeb3e0b528cd2584bdcb3a4b13cc0f8a2 - SHA1 - Malicious payload, peer[.]exe

11c78f15fafd53f8cc5a52b828d7cbf2a99e0b09 - SHA1 - Malicious payload, peer[.]exe

0258bf7dbb0123247db29e8799991140bbdbd9bb - SHA1 - Malicious payload, proxy-peer-windows-amd64[.]exe

b46043a06dd9bbd63e4214d5fbc7fd56e1ff0618 - SHA1 - Possible malicious payload

753afdecd9f5402d004e8e5f768170ae9a468ca5 - SHA1 - Possible malicious payload

8f533c7cb1524b00f7b0311c2ea8603298d6b2ca - SHA1 - Possible malicious payload

3a3bc6a5b4db1a4e961abcb002d26fe9d5e5c349 - SHA1 - Possible malicious payload

897f70eb41d302b045fcb05ed0693675e778ce57 - SHA1 - Possible malicious payload

6ddd5644809606e3dc1e2cc06059c3f5e6176f85 - SHA1 - Malicious payload, proxy-peer-windows-amd64[.]exe

68a94f7cdcaf8853ea99251c1ecc67ae9b32eba8 - SHA1 - Malicious payload, proxy-peer-windows-amd64[.]exe

MITRE ATT&CK Mapping

T1659 -Initial Access, Command and Control -Content Injection

T1588.001 -Resource Development -Malware

T1189 -Initial Access -Drive-by Compromise

T1105 -Command and Control -Ingress Tool Transfer

T1657 -Impact -Financial Theft

T1497.001 -Impact -Compute Hijacking

T1496 -Impact -Resource Hijacking

T1210 -Lateral Movement -Exploitation of Remote Services

T1036.012 -Stealth -Browser Fingerprint

T1071.001 -Command and Control -Web Protocols

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About the author
Min Kim
Cyber Security Analyst
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