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Email

Threat Finds

How Antigena Email caught a fearware attack that bypassed the gateway

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11
Mar 2020
11
Mar 2020
Darktrace’s AI recently neutralized an email attack exploiting a sense of urgency that evaded the organization’s gateway security tools.

The cyber-criminals behind email attacks are well-researched and highly responsive to human behaviors and emotions, often seeking to evoke a specific reaction by leveraging topical information and current news. It’s therefore no surprise that attackers have attempted to latch onto COVID-19 in their latest effort to convince users to open their emails and click on seemingly benign links.

The latest email trend involves attackers who claim to be from the Center for Disease Control and Prevention, purporting to have emergency information about COVID-19. This is typical of a recent trend we’re calling ‘fearware’: cyber-criminals exploit a collective sense of fear and urgency, and coax users into clicking a malicious attachment or link. While the tactic is common, the actual campaigns contain terms and content that’s unique. There are a few patterns in the emails we’ve seen, but none reliably predictable enough to create hard and fast rules that will stop emails with new wording without causing false positives.

For example, looking for the presence of “CDC” in the email sender would easily fail when the emails begin to use new wording, like “WHO”. We’ve also seen a mismatch of links and their display text – with display text that reads “https://cdc.gov/[random-path]” while the actual link is a completely arbitrary URL. Looking for a pattern match on this would likely lead to false positives and would serve as a weak indicator at best.

The majority of these emails, especially the early ones, passed most of our customers’ existing defenses including Mimecast, Proofpoint, and Microsoft’s ATP, and were approved to be delivered directly to the end user’s inbox. Fortunately, these emails were immediately identified and actioned by Antigena Email, Darktrace’s Autonomous Response technology for the inbox.

Gateways: The Current Approach

Most organizations employ Secure Email Gateways (SEGs), like Mimecast or Proofpoint, which serve as an inline middleman between the email sender and the recipient’s email provider. SEGs have largely just become spam-detection engines, as these emails are obvious to spot when seen at scale. They can identify low-hanging fruit (i.e. emails easily detectable as malicious), but they fail to detect and respond when attacks become personalized or deviate even slightly from previously-seen attacks.

Figure 1: A high-level diagram depicting an Email Secure Gateway’s inline position.

SEGs tend to use lists of ‘known-bad’ IPs, domains, and file hashes to determine an email’s threat level – inherently failing to stop novel attacks when they use IPs, domains, or files which are new and have not yet been triaged or reported as malicious.

When advanced detection methods are used in gateway technologies, such as anomaly detection or machine learning, these are performed after the emails have been delivered, and require significant volumes of near-identical emails to trigger. The end result is very often to take an element from one of these emails and simply deny-list it.

When a SEG can’t make the determination on these factors, they may resort to a technique known as sandboxing, which creates an isolated environment for testing links and attachments seen in emails. Alternatively, they may turn to basic levels of anomaly detection that are inadequate due to their lack of context of data outside of emails. For sandboxing, most advanced threats now typically employ evasion techniques like an activation time that waits until a certain date before executing. When deployed, the sandboxing attempts see a harmless file, not recognizing the sleeping attack waiting within.

Figure 2: This email was registered only 2 hours prior to an email we processed.

Taking a sample COVID-19 email seen in a Darktrace customer’s environment, we saw a mix of domains used in what appears to be an attempt to avoid pattern detection. It would be improbable to have the domains used on a list of ‘known-bad’ domains anywhere at the time of the first email, as it was received a mere two hours after the domain was registered.

Figure 3: While other defenses failed to block these emails, Antigena Email immediately marked them as 100% unusual and held them back from delivery.

Antigena Email sits behind all other defenses, meaning we only see emails when those defenses fail to block a malicious email or deem an email is safe for delivery. In the above COVID-19 case, the first 5 emails were marked by MS ATP with a spam confidence score of 1, indicating Microsoft scanned the email and it was determined to be clean – so Microsoft took no action whatsoever.

The Cat and Mouse Game

Cyber-criminals are permanently in flux, quickly moving to outsmart security teams and bypass current defenses. Recognizing email as the easiest entry point into an organization, they are capitalizing on the inadequate detection of existing tools by mass-producing personalized emails through factory-style systems that machine-research, draft, and send with minimal human interaction.

Domains are cheap, proxies are cheap, and morphing files slightly to change the entire fingerprint of a file is easy – rendering any list of ‘known-bads’ as outdated within seconds.

Cyber AI: The New Approach

A new approach is required that relies on business context and an inside-out understanding of a corporation, rather than analyzing emails in isolation.

An Immune System Approach

Darktrace’s core technology uses AI to detect unusual patterns of behavior in the enterprise. The AI is able to do this successfully by following the human immune system’s core principles: develop an innate sense of ‘self’, and use that understanding to detect abnormal activity indicative of a threat.

In order to identify threats across the entire enterprise, the AI is able to understand normal patterns of behavior beyond just the network. This is crucial when working towards a goal of full business understanding. There’s a clear connection between activity in, for example, a SaaS application and a corresponding network event, or an event in the cloud and a corresponding event elsewhere within the business.

There’s an explicit relationship between what people do on their computers and the emails they send and receive. Having the context that a user has just visited a website before they receive an email from the same domain lends credibility to that email: it’s very common to visit a website, subscribe to a mailing list, and then receive an email within a few minutes. On the contrary, receiving an email from a brand-new sender, containing a link that nobody in the organization has ever been to, lends support to the fact that the link is likely no good and that perhaps the email should be removed from the user’s inbox.

Enterprise-Wide Context

Darktrace’s Antigena Email extends this interplay of data sources to the inbox, providing unique detection capabilities by leveraging full business context to inform email decisions.

The design of Antigena Email provides a fundamental shift in email security – from where the tool sits to how it understands and processes data. Unlike SEGs, which sit inline and process emails only as they first pass through and never again, Antigena Email sits passively, ingesting data that is journaled to it. The technology doesn’t need to wait until a domain is fingerprinted or sandboxed, or until it is associated with a campaign that has a famous name and all the buzz.

Antigena Email extends its unique position of not sitting inline to email re-assessment, processing emails millions of times instead of just once, enabling actions to be taken well after delivery. A seemingly benign email with popular links may become more interesting over time if there’s an event within the enterprise that was determined to have originated via an email, perhaps when a trusted site becomes compromised. While Antigena Network will mitigate the new threat on the network, Antigena Email will neutralize the emails that contain links associated with those found in the original email.

Figure 4: Antigena Email sits passively off email providers, continuously re-assessing and issuing updated actions as new data is introduced.

When an email first arrives, Antigena Email extracts its raw metadata, processes it multiple times at machine speed, and then many millions of times subsequently as new evidence is introduced (typically based on events seen throughout the business). The system corroborates what it is seeing with what it has previously understood to be normal throughout the corporate environment. For example, when domains are extracted from envelope information or links in the email body, they’re compared against the popularity of the domain on the company’s network.

Figure 5: The link above was determined to be 100% rare for the enterprise.

Dissecting the above COVID-19 linked email, we can extract some of the data made available in the Antigena Email user interface to see why Darktrace thought the email was so unusual. The domain in the ‘From’ address is rare, which is supplemental contextual information derived from data across the customer’s entire digital environment, not limited to just email but including network data as well. The emails’ KCE, KCD, and RCE indicate that it was the first time the sender had been seen in any email: there had been no correspondence with the sender in any way, and the email address had never been seen in the body of any email.

Figure 6: KCE, KCD, and RCE scores indicate no sender history with the organization.

Correlating the above, Antigena Email deemed these emails 100% anomalous to the business and immediately removed them from the recipients’ inboxes. The platform did this for the very first email, and every email thereafter – not a single COVID-19-based email got by Antigena Email.

Conclusion

Cyber AI does not distinguish ‘good’ from ‘bad’; rather whether an event is likely to belong or not. The technology looks only to compare data with the learnt patterns of activity in the environment, incorporating the new email (alongside its own scoring of the email) into its understanding of day-to-day context for the organization.

By asking questions like “Does this email appear to belong?” or “Is there an existing relationship between the sender and recipient?”, the AI can accurately discern the threat posed by a given email, and incorporate these findings into future modelling. A model cannot be trained to think just because the corporation received a higher volume of emails from a specific sender, these emails are all of a sudden considered normal for the environment. By weighing human interaction with the emails or domains to make decisions on math-modeling reincorporation, Cyber AI avoids this assumption, unless there’s legitimate correspondence from within the corporation back out to the sender.

The inbox has traditionally been the easiest point of entry into an organization. But the fundamental differences in approach offered by Cyber AI drastically increase Antigena Email’s detection capability when compared with gateway tools. Customers with and without email gateways in place have therefore seen a noticeable curbing of their email problem. In the continuous cat-and-mouse game with their adversaries, security teams augmenting their defenses with Cyber AI are finally regaining the advantage.

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.
AUTHOR
ABOUT ThE AUTHOR
Dan Fein
VP, Product

Based in New York, Dan joined Darktrace’s technical team in 2015, helping customers quickly achieve a complete and granular understanding of Darktrace’s product suite. Dan has a particular focus on Darktrace/Email, ensuring that it is effectively deployed in complex digital environments, and works closely with the development, marketing, sales, and technical teams. Dan holds a Bachelor’s degree in Computer Science from New York University.

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Blog

Cloud

Securing the cloud: Using business context to improve visibility and prioritize cyber risk

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26
Mar 2024

Why are businesses shifting to the cloud?

Businesses are increasingly migrating to cloud, due to its potential to streamline operations, reduce costs, and enhance scalability and flexibility. By shifting their infrastructure to the cloud, either as a whole or, more commonly in a hybrid model, organizations can access a wide array of services, such as storage, compute and software applications, without the need for extensive on-premises hardware. However, this transition isn't without challenges.  

Security challenges of cloud migration

Data security, compliance, integration with existing systems, and ensuring consistent performance are critical concerns that need to be addressed. Therefore, companies must develop robust oversight, implement comprehensive security measures, and invest in staff training to successfully navigate the transition to the cloud all while minimizing potential disruptions.

Implementing security measures within a company, however, is a complex endeavour that involves coordination among numerous internal stakeholders two of the most pivotal players involved in cloud security investment, are the security team, entrusted with crafting a business's defensive strategy, and the DevOps engineering team, architects of the infrastructure underpinning the organization's business operations.

Key questions to ask when securing the cloud

Which team is responsible for maintaining the application?  

What do they consider normal?  

How are potential misconfigurations increasing the potential risk of an incident?

Best practices of cloud security

Contextual awareness of the business is a crucial facet for securing a company's cloud infrastructure, as it enables organizations to align security measures with specific business objectives, risks, and regulatory requirements. Understanding the context of the business operations, its goals, critical assets, and compliance obligations, allows security teams to tailor their strategies and controls accordingly.

How does Darktrace help secure the cloud?

In response to the difficulties outlined above, Darktrace has adopted a holistic approach to security with an ActiveAI security platform that is context-aware. This platform enables stakeholders to effectively detect and respond to threats that may arise within their cloud or on premises environments.  

By monitoring your network and identity activity, Darktrace can identify what is considered “normal” within your organization. This however doesn’t tell the whole story. It is also important to understand where these actions are occurring within the context of the business.  

Visibility in the cloud

Without visibility into the individual assets that make up the cloud environment, how these are configured, and how they operate at run time, security is incredibly difficult to maintain. Visibility allows security teams to identify potential vulnerabilities, misconfigurations, or unauthorized access points that could be exploited by malicious actors. It enables proactive monitoring and rapid response to security incidents, ensuring that any threats are promptly identified and mitigated before they can cause significant damage.  

Building architecture diagrams

The cornerstone of our strategy lies in the architecture diagrams, which serve as a framework for organizing resources within our cloud environment. An architecture comprises of interconnected resources governed by access controls and network routing mechanisms. Its purpose is to logically group these resources into the applications they support.  

Achieving this involves compiling a comprehensive inventory of the cloud environment, analyzing resource permissions—including both outbound and inbound access—and considering any overarching organizational policies. For networked devices, we delve into route tables, firewalls, and subnet access control policies. This information is then utilized to build a graph of interconnected assets, wherein each resource constitutes a node, and the possible connections between resources are represented as edges.

Once we have built up an inventory of all the resources within your environments, we can then start building architectures based on the graph. We do this by selecting distinct starting points for graph traversal, which we infer from our deep understanding of the cloud, an example would be a Virtual Private Cloud (VPC) - A VPC is a virtual network that closely resembles a traditional network that you'd operate in your own data center.  

All networked devices are usually housed within a VPC, with applications typically grouped into one or more VPCs. If multiple VPCs are detected with peering connections between them, we consider them as distinct parts of the same system. This approach enables us to comprehend applications across regions and accounts, rather than solely from the isolated viewpoint of a single VPC.

However, the cloud isn’t all about compute instances, serverless is a popular architecture. In fact, for many developers serverless architectures offer greater scalability and flexibility. Reviewing prevalent serverless architecture patterns, we've chosen some common fundamental resources as our starting point, Lambda functions and Elastic Container Service (ECS) clusters are prime examples, serving as crucial components in various serverless systems with distinct yet similar characteristics.

Prioritize risk in the cloud

Once we have built up an inventory of all the cloud asset, Darktrace/Cloud utilizes an ‘outlier’ detection machine learning model. This looks to categorize all the assets and identifies the ones that look different or ‘odd’ when compared with the assets around it, this is based on a wide range of characteristics some of which will include, Name, VPC ID, Host Region etc, whilst also incorporating contextual knowledge of where these assets are found, and how they fit into the architecture they are in.  

Once outliers are identified, we can use this information to assess the potential risk posed by the asset. Context plays a crucial role in this stage, as incorporating observations about the asset enables effective scoring. For instance, detecting a misconfiguration, anomalous network connections, or unusual user activity can significantly raise the asset's score. Consequently, the architecture it belongs to can be flagged for further investigation.

Adapting to a dynamic cloud environment

The cloud is incredibly dynamic. Therefore, Darktrace does not see architectures as fixed entities. Instead, we're always on the lookout for changes, driven by user and service activity. This prompts us to dive back in, update our architectural view, and keep a living record of the cloud's ever-changing landscape, providing near real-time insights into what's happening within it.  

Darktrace/Cloud doesn’t just consider isolated detections, it identifies assets that have misconfigurations and anomalous activity across the network and management plane and adjusts the priority of the alerting to match the potential risk that these assets could be leveraged to enable an attack.  

While in isolation misconfigurations don’t have much meaningful impact, when they are combined with real time updates and anomaly detection within the context of the architecture you see a very important and impactful perspective.  

Combining all of this into one view where security and dev ops teams can collaborate ensures continuity across teams, playing a vital role in providing effective security.

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About the author
Adam Stevens
Analyst Technical Director

Blog

Inside the SOC

Socks5Systemz: How Darktrace’s Anomaly Detection Unraveled a Stealthy Botnet

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22
Mar 2024

What are botnets?

Although not a recent addition to the threat landscape, botnets persist as a significant concern for organizations, with many threat actors utilizing them for political, strategic, or financial gain. Botnets pose a particularly persistent threat to security teams; even if one compromised device is detected, attackers will likely have infected multiple devices and can continue to operate. Moreover, threat actors are able to easily replace the malware communication channels between infected devices and their command-and-control (C2) servers, making it incredibly difficult to remove the infection.

Botnet example: Socks5Systemz

One example of a botnet recently investigated by the Darktrace Threat Research team is Socks5Systemz. Socks5Systemz is a proxy-for-rent botnet, whereby actors can rent blocks of infected devices to perform proxying services.  Between August and November 2023, Darktrace detected indicators of Socks5Systemz botnet compromise within a cross-industry section of the customer base. Although open-source intelligence (OSINT) research of the botnet only appeared in November 2023, the anomaly-based approach of Darktrace DETECT™ allowed it to identify multiple stages of the network-based activity on affected customer systems well before traditional rules and signatures would have been implemented.

Darktrace’s Cyber AI Analyst™ complemented DETECT’s successful identification of Socks5Systemz activity on customer networks, playing a pivotal role in piecing together the seemingly separate events that comprised the wider compromise. This allowed Darktrace to build a clearer picture of the attack, empowering its customers with full visibility over emerging incidents.

In the customer environments highlighted in this blog, Darktrace RESPOND™ was not configured to operate autonomously. As a result, Socks5Systemz attacks were able to advance through their kill chains until customer security teams acted upon Darktrace’s detections and began their remediation procedures.

What is Socks5Systemz?

The Socks5Systemz botnet is a proxy service where individuals can use infected devices as proxy servers.

These devices act as ‘middlemen’, forwarding connections from malicious actors on to their intended destination. As this additional connectivity conceals the true origin of the connections, threat actors often use botnets to increase their anonymity. Although unauthorized proxy servers on a corporate network may not appear at first glance to be a priority for organizations and their security teams, complicity in proxy botnets could result in reputational damage and significant financial losses.

Since it was first observed in the wild in 2016, the Socks5Systemz botnet has grown steadily, seemingly unnoticed by cyber security professionals, and has infected a reported 10,000 devices worldwide [1]. Cyber security researchers noted a high concentration of compromised devices in India, with lower concentrations of devices infected in the United States, Latin America, Australia and multiple European and African countries [2]. Renting sections of the Socks5Systemz botnet costs between 1 USD and 4,000 USD, with options to increase the threading and time-range of the rentals [2]. Due to the lack of affected devices in Russia, some threat researchers have concluded that the botnet’s operators are likely Russian [2].

Darktrace’s Coverage of Socks5Systemz

The Darktrace Threat Research team conducted investigations into campaign-like activity across the customer base between August and November 2023, where multiple indicators of compromise (IoCs) relating to the Socks5Systemz proxy botnet were observed. Darktrace identified several stages of the attack chain described in static malware analysis by external researchers. Darktrace was also able to uncover additional IoCs and stages of the Socks5Systemz attack chain that had not featured in external threat research.

Delivery and Execution

Prior research on Socks5Systemz notes how the malware is typically delivered via user input, with delivery methods including phishing emails, exploit kits, malicious ads, and trojanized executables downloaded from peer-to-peer (P2P) networks [1].

Threat actors have also used separate malware loaders such as PrivateLoader and Amadey deliver the Socks5Systemz payload. These loaders will drop executable files that are responsible for setting up persistence and injecting the proxy bot into the infected device’s memory [2]. Although evidence of initial payload delivery did not appear during its investigations, Darktrace did discover IoCs relating to PrivateLoader and Amadey on multiple customer networks. Such activity included HTTP POST requests using PHP to rare external IPs and HTTP connections with a referrer header field, indicative of a redirected connection.

However, additional adjacent activity that may suggest initial user execution and was observed during Darktrace’s investigations. For example, an infected device on one deployment made a HTTP GET request to a rare external domain with a “.fun” top-level domain (TLD) for a PDF file. The URI also appears to have contained a client ID. While this download and HTTP request likely corresponded to the gathering and transmission of further telemetry data and infection verification [2], the downloaded PDF file may have represented a malicious payload.

Advanced Search log details highlighting a device infected by Socks5Systemz downloading a suspicious PDF file.
Figure 1: Advanced Search log details highlighting a device infected by Socks5Systemz downloading a suspicious PDF file.

Establishing C2 Communication  

Once the proxy bot has been injected into the device’s memory, the malware attempts to contact servers owned by the botnet’s operators. Across several customer environments, Darktrace identified infected devices attempting to establish connections with such C2 servers. First, affected devices would make repeated HTTP GET requests over port 80 to rare external domains; these endpoints typically had “.ua” and “.ru” TLDs. The majority of these connection attempts were not preceded by a DNS host lookup, suggesting that the domains were already loaded in the device’s cache memory or hardcoded into the code of running processes.

Figure 2: Breach log data connections identifying repeated unusual HTTP connections over port 80 for domains without prior DNS host lookup.

While most initial HTTP GET requests across investigated incidents did not feature DNS host lookups, Darktrace did identify affected devices on a small number of customer environments performing a series of DNS host lookups for seemingly algorithmically generated domains (DGA). These domains feature the same TLDs as those seen in connections without prior DNS host lookups.  

Figure 3: Cyber AI Analyst data indicating a subset of DGAs queried via DNS by infected devices.

These DNS requests follow the activity reported by researchers, where infected devices query a hardcoded DNS server controlled by the threat actor for an DGA domain [2]. However, as the bulk of Darktrace’s investigations presented HTTP requests without a prior DNS host lookup, this activity indicates a significant deviation from the behavior reported by OSINT sources. This could indicate that multiple variations of the Socks5Systemz botnet were circulating at the time of investigation.

Most hostnames observed during this time of investigation follow a specific regular expression format: /[a-z]{7}\.(ua|net|info|com|ru)/ or /[a-z0-9]{15}\.(ua)/. Darktrace also noticed the HTTP GET requests for DGA domains followed a consistent URI pattern: /single.php?c=<STRING>. The requests were also commonly made using the “Mozilla/5.0 (Windows; U; MSIE 9.0; Windows NT 9.0; en-US)” user agent over port 80.

This URI pattern observed during Darktrace’s investigations appears to reflect infected devices contacting Socks5Systemz C2 servers to register the system and details of the host, and signal it is ready to receive further instructions [2]. These URIs are encrypted with a RC4 stream cipher and contain information relating to the device’s operating system and architecture, as well as details of the infection.

The HTTP GET requests during this time, which involved devices made to a variety a variety of similar DGA domains, appeared alongside IP addresses that were later identified as Socks5Systemz C2 servers.

Figure 4: Cyber AI Analyst investigation details highlighting HTTP GET activity whereby RC4 encrypted data is sent to proxy C2 domains.

However, not all affected devices observed by Darktrace used DGA domains to transmit RC4 encoded data. Some investigated systems were observed making similar HTTP GET requests over port 80, albeit to the external domain: “bddns[.]cc”, using the aforementioned Mozilla user agent. During these requests, Darktrace identified a consistent URI pattern, similar to that seen in the DGA domain GET requests: /sign/<RC4 cipher text>.  

Darktrace DETECT recognized the rarity of the domains and IPs that were connected to by affected devices, as well as the usage of the new Mozilla user agent.  The HTTP connections, and the corresponding Darktrace DETECT model breaches, parallel the analysis made by external researchers: if the initial DGA DNS requests do not return a valid C2 server, infected devices connect to, and request the IP address of a server from, the above-mentioned domain [2].

Connection to Proxy

After sending host and infection details via HTTP and receiving commands from the C2 server, affected devices were frequently observed initiating activity to join the Sock5Systemz botnet. Infected hosts would first make HTTP GET requests to an IP identified as Socks5Systemz’s proxy checker application, usually sending the URI “proxy-activity.txt” to the domain over the HTTP protocol. This likely represents an additional validation check to confirm that the infected device is ready to join the botnet.

Figure 5: Cyber AI Analyst investigation detailing HTTP GET requests over port 80 to the Socks5Systemz Proxy Checker Application.

Following the final validation checks, devices would then attempt TCP connections to a range of IPs, which have been associated with BackConnect proxy servers, over port 1074. At this point, the device is able to receive commands from actors who login to and operate the corresponding BackConnect server. This BackConnect server will transmit traffic from the user renting the segment of the botnet [2].

Darktrace observed a range of activity associated with this stage of the attack, including the use of new or unusual user agents, connections to suspicious IPs, and other anomalous external connectivity which represented a deviation from affected devices’ expected behavior.

Additional Activities Following Proxy Addition

The Darktrace Threat Research team found evidence of the possible deployment of additional malware strains during their investigation into devices affected by Socks5Systemz. IoCs associated with both the Amadey and PrivateLoader loader malware strains, both of which are known to distribute Socks5Systemz, were also observed on affected devices. Additionally, Darktrace observed multiple infected systems performing cryptocurrency mining operations around the time of the Sock5Systemz compromise, utilizing the MinerGate protocol to conduct login and job functions, as well as making DNS requests for mining pools.

While such behavior would fall outside of the expected activity for Socks5Systemz and cannot be definitively attributed to it, Darktrace did observe devices affected by the botnet performing additional malicious downloads and operations during its investigations.

Conclusion

Ultimately, Darktrace’s anomaly-based approach to threat detection enabled it to effectively identify and alert for malicious Socks5Systemz botnet activity long before external researchers had documented its IoCs and tactics, techniques, and procedures (TTPs).  

In fact, Darktrace not only identified multiple distinct attack phases later outlined in external research but also uncovered deviations from these expected patterns of behavior. By proactively detecting emerging threats through anomaly detection rather than relying on existing threat intelligence, Darktrace is well positioned to detect evolving threats like Socks5Systemz, regardless of what their future iterations might look like.

Faced with the threat of persistent botnets, it is crucial for organizations to detect malicious activity in its early stages before additional devices are compromised, making it increasingly difficult to remediate. Darktrace’s suite of products enables the swift and effective detection of such threats. Moreover, when enabled in autonomous response mode, Darktrace RESPOND is uniquely positioned to take immediate, targeted actions to contain these attacks from the onset.

Credit to Adam Potter, Cyber Security Analyst, Anna Gilbertson, Cyber Security Analyst

Appendices

DETECT Model Breaches

  • Anomalous Connection / Multiple Failed Connections to Rare Endpoint
  • Anomalous Connection / Multiple Connections to New External TCP Port
  • Compromise / Beaconing Activity To External Rare
  • Compromise / DGA Beacon
  • Compromise / Beacon to Young Endpoint
  • Compromise / Slow Beaconing Activity To External Rare
  • Compromise / HTTP Beaconing to Rare Destination
  • Compromise / Quick and Regular Windows HTTP Beaconing
  • Compromise / Agent Beacon (Medium Period)
  • Compromise / Agent Beacon (Long Period)
  • Device / New User Agent
  • Device / New User Agent and New IP

Cyber AI Analyst Incidents

  • Possible HTTP Command and Control
  • Possible HTTP Command and Control to Multiple Endpoints
  • Unusual Repeated Connections
  • Unusual Repeated Connections to Multiple Endpoints
  • Multiple DNS Requests for Algorithmically Generated Domains

Indicators of Compromise

IoC - Type - Description

185.141.63[.]172 - IP Address - Socks5Systemz C2 Endpoint

193.242.211[.]141 - IP Address - Socks5Systemz C2 Endpoint

109.230.199[.]181 - IP Address - Socks5Systemz C2 Endpoint

109.236.88[.]134 - IP Address - Socks5Systemz C2 Endpoint

217.23.5[.]14 - IP Address - Socks5Systemz Proxy Checker App

88.80.148[.]8 - IP Address - Socks5Systemz Backconnect Endpoint

88.80.148[.]219 - IP Address - Socks5Systemz Backconnect Endpoint

185.141.63[.]4 - IP Address - Socks5Systemz Backconnect Endpoint

185.141.63[.]2 - IP Address - Socks5Systemz Backconnect Endpoint

195.154.188[.]211 - IP Address - Socks5Systemz Backconnect Endpoint

91.92.111[.]132 - IP Address - Socks5Systemz Backconnect Endpoint

91.121.30[.]185 - IP Address - Socks5Systemz Backconnect Endpoint

94.23.58[.]173 - IP Address - Socks5Systemz Backconnect Endpoint

37.187.148[.]204 - IP Address - Socks5Systemz Backconnect Endpoint

188.165.192[.]18 - IP Address - Socks5Systemz Backconnect Endpoint

/single.php?c=<RC4 data hex encoded> - URI - Socks5Systemz HTTP GET Request

/sign/<RC4 data hex encoded> - URI - Socks5Systemz HTTP GET Request

/proxy-activity.txt - URI - Socks5Systemz HTTP GET Request

datasheet[.]fun - Hostname - Socks5Systemz C2 Endpoint

bddns[.]cc - Hostname - Socks5Systemz C2 Endpoint

send-monitoring[.]bit - Hostname - Socks5Systemz C2 Endpoint

MITRE ATT&CK Mapping

Command and Control

T1071 - Application Layer Protocol

T1071.001 – Web protocols

T1568 – Dynamic Resolution

T1568.002 – Domain Generation Algorithms

T1132 – Data Encoding

T1132 – Non-Standard Encoding

T1090 – Proxy

T1090.002 – External Proxy

Exfiltration

T1041 – Exfiltration over C2 channel

Impact

T1496 – Resource Hijacking

References

1. https://www.bleepingcomputer.com/news/security/socks5systemz-proxy-service-infects-10-000-systems-worldwide/

2. https://www.bitsight.com/blog/unveiling-socks5systemz-rise-new-proxy-service-privateloader-and-amadey

Continue reading
About the author
Adam Potter
Cyber Analyst
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